Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chai, Juanjuan; Kora, Guruprasad; Ahn, Tae-Hyuk

2014-10-09

To supply some background, phylogenetic studies have provided detailed knowledge on the evolutionary mechanisms of genes and species in Bacteria and Archaea. However, the evolution of cellular functions, represented by metabolic pathways and biological processes, has not been systematically characterized. Many clades in the prokaryotic tree of life have now been covered by sequenced genomes in GenBank. This enables a large-scale functional phylogenomics study of many computationally inferred cellular functions across all sequenced prokaryotes. Our results show a total of 14,727 GenBank prokaryotic genomes were re-annotated using a new protein family database, UniFam, to obtain consistent functional annotations for accuratemore » comparison. The functional profile of a genome was represented by the biological process Gene Ontology (GO) terms in its annotation. The GO term enrichment analysis differentiated the functional profiles between selected archaeal taxa. 706 prokaryotic metabolic pathways were inferred from these genomes using Pathway Tools and MetaCyc. The consistency between the distribution of metabolic pathways in the genomes and the phylogenetic tree of the genomes was measured using parsimony scores and retention indices. The ancestral functional profiles at the internal nodes of the phylogenetic tree were reconstructed to track the gains and losses of metabolic pathways in evolutionary history. In conclusion, our functional phylogenomics analysis shows divergent functional profiles of taxa and clades. Such function-phylogeny correlation stems from a set of clade-specific cellular functions with low parsimony scores. On the other hand, many cellular functions are sparsely dispersed across many clades with high parsimony scores. These different types of cellular functions have distinct evolutionary patterns reconstructed from the prokaryotic tree.« less

The Co-regulation Data Harvester: Automating gene annotation starting from a transcriptome database

NASA Astrophysics Data System (ADS)

Tsypin, Lev M.; Turkewitz, Aaron P.

Identifying co-regulated genes provides a useful approach for defining pathway-specific machinery in an organism. To be efficient, this approach relies on thorough genome annotation, a process much slower than genome sequencing per se. Tetrahymena thermophila, a unicellular eukaryote, has been a useful model organism and has a fully sequenced but sparsely annotated genome. One important resource for studying this organism has been an online transcriptomic database. We have developed an automated approach to gene annotation in the context of transcriptome data in T. thermophila, called the Co-regulation Data Harvester (CDH). Beginning with a gene of interest, the CDH identifies co-regulated genes by accessing the Tetrahymena transcriptome database. It then identifies their closely related genes (orthologs) in other organisms by using reciprocal BLAST searches. Finally, it collates the annotations of those orthologs' functions, which provides the user with information to help predict the cellular role of the initial query. The CDH, which is freely available, represents a powerful new tool for analyzing cell biological pathways in Tetrahymena. Moreover, to the extent that genes and pathways are conserved between organisms, the inferences obtained via the CDH should be relevant, and can be explored, in many other systems.

Plant Reactome: a resource for plant pathways and comparative analysis.

PubMed

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D; Wu, Guanming; Fabregat, Antonio; Elser, Justin L; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D; Ware, Doreen; Jaiswal, Pankaj

2017-01-04

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Modeling biochemical pathways in the gene ontology

DOE PAGES

Hill, David P.; D’Eustachio, Peter; Berardini, Tanya Z.; ...

2016-09-01

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes inmore » the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.« less

Derivation of Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Teleost Reproductive Axis.

EPA Science Inventory

Oligonucleotide microarrays are a powerful tool for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-based analyses to detect diffe...

Bioinformatics for spermatogenesis: annotation of male reproduction based on proteomics

PubMed Central

Zhou, Tao; Zhou, Zuo-Min; Guo, Xue-Jiang

2013-01-01

Proteomics strategies have been widely used in the field of male reproduction, both in basic and clinical research. Bioinformatics methods are indispensable in proteomics-based studies and are used for data presentation, database construction and functional annotation. In the present review, we focus on the functional annotation of gene lists obtained through qualitative or quantitative methods, summarizing the common and male reproduction specialized proteomics databases. We introduce several integrated tools used to find the hidden biological significance from the data obtained. We further describe in detail the information on male reproduction derived from Gene Ontology analyses, pathway analyses and biomedical analyses. We provide an overview of bioinformatics annotations in spermatogenesis, from gene function to biological function and from biological function to clinical application. On the basis of recently published proteomics studies and associated data, we show that bioinformatics methods help us to discover drug targets for sperm motility and to scan for cancer-testis genes. In addition, we summarize the online resources relevant to male reproduction research for the exploration of the regulation of spermatogenesis. PMID:23852026

AnnoSys—implementation of a generic annotation system for schema-based data using the example of biodiversity collection data

PubMed Central

Kusber, W.-H.; Tschöpe, O.; Güntsch, A.; Berendsohn, W. G.

2017-01-01

Abstract Biological research collections holding billions of specimens world-wide provide the most important baseline information for systematic biodiversity research. Increasingly, specimen data records become available in virtual herbaria and data portals. The traditional (physical) annotation procedure fails here, so that an important pathway of research documentation and data quality control is broken. In order to create an online annotation system, we analysed, modeled and adapted traditional specimen annotation workflows. The AnnoSys system accesses collection data from either conventional web resources or the Biological Collection Access Service (BioCASe) and accepts XML-based data standards like ABCD or DarwinCore. It comprises a searchable annotation data repository, a user interface, and a subscription based message system. We describe the main components of AnnoSys and its current and planned interoperability with biodiversity data portals and networks. Details are given on the underlying architectural model, which implements the W3C OpenAnnotation model and allows the adaptation of AnnoSys to different problem domains. Advantages and disadvantages of different digital annotation and feedback approaches are discussed. For the biodiversity domain, AnnoSys proposes best practice procedures for digital annotations of complex records. Database URL: https://annosys.bgbm.fu-berlin.de/AnnoSys/AnnoSys PMID:28365735

Gramene 2013: comparative plant genomics resources.

PubMed

Monaco, Marcela K; Stein, Joshua; Naithani, Sushma; Wei, Sharon; Dharmawardhana, Palitha; Kumari, Sunita; Amarasinghe, Vindhya; Youens-Clark, Ken; Thomason, James; Preece, Justin; Pasternak, Shiran; Olson, Andrew; Jiao, Yinping; Lu, Zhenyuan; Bolser, Dan; Kerhornou, Arnaud; Staines, Dan; Walts, Brandon; Wu, Guanming; D'Eustachio, Peter; Haw, Robin; Croft, David; Kersey, Paul J; Stein, Lincoln; Jaiswal, Pankaj; Ware, Doreen

2014-01-01

Gramene (http://www.gramene.org) is a curated online resource for comparative functional genomics in crops and model plant species, currently hosting 27 fully and 10 partially sequenced reference genomes in its build number 38. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. Whole-genome alignments complemented by phylogenetic gene family trees help infer syntenic and orthologous relationships. Genetic variation data, sequences and genome mappings available for 10 species, including Arabidopsis, rice and maize, help infer putative variant effects on genes and transcripts. The pathways section also hosts 10 species-specific metabolic pathways databases developed in-house or by our collaborators using Pathway Tools software, which facilitates searches for pathway, reaction and metabolite annotations, and allows analyses of user-defined expression datasets. Recently, we released a Plant Reactome portal featuring 133 curated rice pathways. This portal will be expanded for Arabidopsis, maize and other plant species. We continue to provide genetic and QTL maps and marker datasets developed by crop researchers. The project provides a unique community platform to support scientific research in plant genomics including studies in evolution, genetics, plant breeding, molecular biology, biochemistry and systems biology.

Statistical assessment of crosstalk enrichment between gene groups in biological networks.

PubMed

McCormack, Theodore; Frings, Oliver; Alexeyenko, Andrey; Sonnhammer, Erik L L

2013-01-01

Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

A Graphical Systems Model and Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Female Teleost Reproductive Axis

EPA Science Inventory

Oligonucleotide microarrays and other ‘omics’ approaches are powerful tools for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-b...

Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

PubMed Central

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/ PMID:26363020

Metabolic network prediction through pairwise rational kernels.

PubMed

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times. The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

NetPath: a public resource of curated signal transduction pathways

PubMed Central

2010-01-01

We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches. PMID:20067622

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

PubMed Central

Yue, Zongliang; Zheng, Qi; Neylon, Michael T; Yoo, Minjae; Shin, Jimin; Zhao, Zhiying; Tan, Aik Choon

2018-01-01

Abstract Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug–gene, miRNA–gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/. PMID:29126216

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways.

PubMed

Beltrame, Luca; Calura, Enrica; Popovici, Razvan R; Rizzetto, Lisa; Guedez, Damariz Rivero; Donato, Michele; Romualdi, Chiara; Draghici, Sorin; Cavalieri, Duccio

2011-08-01

Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists. The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.

A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

PubMed Central

Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-01

Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data.

PubMed

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/. © The Author(s) 2015. Published by Oxford University Press.

Composition and applications of focus libraries to phenotypic assays

PubMed Central

Wassermann, Anne Mai; Camargo, Luiz M.; Auld, Douglas S.

2014-01-01

The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these. PMID:25104937

An editor for pathway drawing and data visualization in the Biopathways Workbench.

PubMed

Byrnes, Robert W; Cotter, Dawn; Maer, Andreia; Li, Joshua; Nadeau, David; Subramaniam, Shankar

2009-10-02

Pathway models serve as the basis for much of systems biology. They are often built using programs designed for the purpose. Constructing new models generally requires simultaneous access to experimental data of diverse types, to databases of well-characterized biological compounds and molecular intermediates, and to reference model pathways. However, few if any software applications provide all such capabilities within a single user interface. The Pathway Editor is a program written in the Java programming language that allows de-novo pathway creation and downloading of LIPID MAPS (Lipid Metabolites and Pathways Strategy) and KEGG lipid metabolic pathways, and of measured time-dependent changes to lipid components of metabolism. Accessed through Java Web Start, the program downloads pathways from the LIPID MAPS Pathway database (Pathway) as well as from the LIPID MAPS web server http://www.lipidmaps.org. Data arises from metabolomic (lipidomic), microarray, and protein array experiments performed by the LIPID MAPS consortium of laboratories and is arranged by experiment. Facility is provided to create, connect, and annotate nodes and processes on a drawing panel with reference to database objects and time course data. Node and interaction layout as well as data display may be configured in pathway diagrams as desired. Users may extend diagrams, and may also read and write data and non-lipidomic KEGG pathways to and from files. Pathway diagrams in XML format, containing database identifiers referencing specific compounds and experiments, can be saved to a local file for subsequent use. The program is built upon a library of classes, referred to as the Biopathways Workbench, that convert between different file formats and database objects. An example of this feature is provided in the form of read/construct/write access to models in SBML (Systems Biology Markup Language) contained in the local file system. Inclusion of access to multiple experimental data types and of pathway diagrams within a single interface, automatic updating through connectivity to an online database, and a focus on annotation, including reference to standardized lipid nomenclature as well as common lipid names, supports the view that the Pathway Editor represents a significant, practicable contribution to current pathway modeling tools.

MassTRIX: mass translator into pathways.

PubMed

Suhre, Karsten; Schmitt-Kopplin, Philippe

2008-07-01

Recent technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain. MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities. The MassTRIX web server is freely accessible at http://masstrix.org.

Rapid evolution and gene expression: a rapidly evolving Mendelian trait that silences field crickets has widespread effects on mRNA and protein expression.

PubMed

Pascoal, S; Liu, X; Ly, T; Fang, Y; Rockliffe, N; Paterson, S; Shirran, S L; Botting, C H; Bailey, N W

2016-06-01

A major advance in modern evolutionary biology is the ability to start linking phenotypic evolution in the wild with genomic changes that underlie that evolution. We capitalized on a rapidly evolving Hawaiian population of crickets (Teleogryllus oceanicus) to test hypotheses about the genomic consequences of a recent Mendelian mutation of large effect which disrupts the development of sound-producing structures on male forewings. The resulting silent phenotype, flatwing, persists because of natural selection imposed by an acoustically orienting parasitoid, but it interferes with mate attraction. We examined gene expression differences in developing wing buds of wild-type and flatwing male crickets using RNA-seq and quantitative proteomics. Most differentially expressed (DE) transcripts were down-regulated in flatwing males (625 up vs. 1716 down), whereas up- and down-regulated proteins were equally represented (30 up and 34 down). Differences between morphs were clearly not restricted to a single pathway, and we recovered annotations associated with a broad array of functions that would not be predicted a priori. Using a candidate gene detection test based on homology, we identified 30% of putative Drosophila wing development genes in the cricket transcriptome, but only 10% were DE. In addition to wing-related annotations, endocrine pathways and several biological processes such as reproduction, immunity and locomotion were DE in the mutant crickets at both biological levels. Our results illuminate the breadth of genetic pathways that are potentially affected in the early stages of adaptation. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

PubMed

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Fault Tolerance in Protein Interaction Networks: Stable Bipartite Subgraphs and Redundant Pathways

PubMed Central

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J.

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair. PMID:19399174

SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency

PubMed Central

Yi, Ming; Stephens, Robert M.

2008-01-01

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data. PMID:18818771

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery.

PubMed

Tebani, Abdellah; Afonso, Carlos; Bekri, Soumeya

2018-05-01

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects.

PubMed

Danchin, Antoine; Ouzounis, Christos; Tokuyasu, Taku; Zucker, Jean-Daniel

2018-07-01

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. © 2018 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Lynx web services for annotations and systems analysis of multi-gene disorders.

PubMed

Sulakhe, Dinanath; Taylor, Andrew; Balasubramanian, Sandhya; Feng, Bo; Xie, Bingqing; Börnigen, Daniela; Dave, Utpal J; Foster, Ian T; Gilliam, T Conrad; Maltsev, Natalia

2014-07-01

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

PathFinder: reconstruction and dynamic visualization of metabolic pathways.

PubMed

Goesmann, Alexander; Haubrock, Martin; Meyer, Folker; Kalinowski, Jörn; Giegerich, Robert

2002-01-01

Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.

Molecular signatures database (MSigDB) 3.0.

PubMed

Liberzon, Arthur; Subramanian, Aravind; Pinchback, Reid; Thorvaldsdóttir, Helga; Tamayo, Pablo; Mesirov, Jill P

2011-06-15

Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets. We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site. MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.

AutoFACT: An Automatic Functional Annotation and Classification Tool

PubMed Central

Koski, Liisa B; Gray, Michael W; Lang, B Franz; Burger, Gertraud

2005-01-01

Background Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. Results We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. Conclusion AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at . PMID:15960857

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways

PubMed Central

Rizzetto, Lisa; Guedez, Damariz Rivero; Donato, Michele; Romualdi, Chiara; Draghici, Sorin; Cavalieri, Duccio

2011-01-01

Motivation: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. Results: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and ‘wet lab’ scientists. Availability and implementation: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X. Contact: duccio.cavalieri@unifi.it; sorin@wayne.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21653523

Software Tool for Researching Annotations of Proteins (STRAP): Open-Source Protein Annotation Software with Data Visualization

PubMed Central

Bhatia, Vivek N.; Perlman, David H.; Costello, Catherine E.; McComb, Mark E.

2009-01-01

In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP) – a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP includes meta-information on the protein in addition to complimentary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger dataset. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie (biological process, cellular component and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large datasets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net. PMID:19839595

Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis.

PubMed

Ferrari, Raffaele; Forabosco, Paola; Vandrovcova, Jana; Botía, Juan A; Guelfi, Sebastian; Warren, Jason D; Momeni, Parastoo; Weale, Michael E; Ryten, Mina; Hardy, John

2016-02-24

In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products. Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses. This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work.

SorghumFDB: sorghum functional genomics database with multidimensional network analysis.

PubMed

Tian, Tian; You, Qi; Zhang, Liwei; Yi, Xin; Yan, Hengyu; Xu, Wenying; Su, Zhen

2016-01-01

Sorghum (Sorghum bicolor [L.] Moench) has excellent agronomic traits and biological properties, such as heat and drought-tolerance. It is a C4 grass and potential bioenergy-producing plant, which makes it an important crop worldwide. With the sorghum genome sequence released, it is essential to establish a sorghum functional genomics data mining platform. We collected genomic data and some functional annotations to construct a sorghum functional genomics database (SorghumFDB). SorghumFDB integrated knowledge of sorghum gene family classifications (transcription regulators/factors, carbohydrate-active enzymes, protein kinases, ubiquitins, cytochrome P450, monolignol biosynthesis related enzymes, R-genes and organelle-genes), detailed gene annotations, miRNA and target gene information, orthologous pairs in the model plants Arabidopsis, rice and maize, gene loci conversions and a genome browser. We further constructed a dynamic network of multidimensional biological relationships, comprised of the co-expression data, protein-protein interactions and miRNA-target pairs. We took effective measures to combine the network, gene set enrichment and motif analyses to determine the key regulators that participate in related metabolic pathways, such as the lignin pathway, which is a major biological process in bioenergy-producing plants.Database URL: http://structuralbiology.cau.edu.cn/sorghum/index.html. © The Author(s) 2016. Published by Oxford University Press.

Payao: a community platform for SBML pathway model curation

PubMed Central

Matsuoka, Yukiko; Ghosh, Samik; Kikuchi, Norihiro; Kitano, Hiroaki

2010-01-01

Summary: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members. Availability and implementation: Freely available for model curation service at http://www.payaologue.org. Web site implemented in Seaser Framework 2.0 with S2Flex2, MySQL 5.0 and Tomcat 5.5, with all major browsers supported. Contact: kitano@sbi.jp PMID:20371497

Transcriptomic basis for drought-resistance in Brassica napus L.

NASA Astrophysics Data System (ADS)

Wang, Pei; Yang, Cuiling; Chen, Hao; Song, Chunpeng; Zhang, Xiao; Wang, Daojie

2017-01-01

Based on transcriptomic data from four experimental settings with drought-resistant and drought-sensitive cultivars under drought and well-watered conditions, statistical analysis revealed three categories encompassing 169 highly differentially expressed genes (DEGs) in response to drought in Brassica napus L., including 37 drought-resistant cultivar-related genes, 35 drought-sensitive cultivar-related genes and 97 cultivar non-specific ones. We provide evidence that the identified DEGs were fairly uniformly distributed on different chromosomes and their expression patterns are variety specific. Except commonly enriched in response to various stimuli or stresses, different categories of DEGs show specific enrichment in certain biological processes or pathways, which indicated the possibility of functional differences among the three categories. Network analysis revealed relationships among the 169 DEGs, annotated biological processes and pathways. The 169 DEGs can be classified into different functional categories via preferred pathways or biological processes. Some pathways might simultaneously involve a large number of shared DEGs, and these pathways are likely to cross-talk and have overlapping biological functions. Several members of the identified DEGs fit to drought stress signal transduction pathway in Arabidopsis thaliana. Finally, quantitative real-time PCR validations confirmed the reproducibility of the RNA-seq data. These investigations are profitable for the improvement of crop varieties through transgenic engineering.

Pathway-based analyses.

PubMed

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

PathText: a text mining integrator for biological pathway visualizations

PubMed Central

Kemper, Brian; Matsuzaki, Takuya; Matsuoka, Yukiko; Tsuruoka, Yoshimasa; Kitano, Hiroaki; Ananiadou, Sophia; Tsujii, Jun'ichi

2010-01-01

Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com. PMID:20529930

Transcriptomic Responses to Salinity Stress in the Pacific Oyster Crassostrea gigas

PubMed Central

Zhao, Xuelin; Yu, Hong; Kong, Lingfeng; Li, Qi

2012-01-01

Background Low salinity is one of the main factors limiting the distribution and survival of marine species. As a euryhaline species, the Pacific oyster Crassostrea gigas is considered to be tolerant to relative low salinity. The genes that regulate C. gigas responses to osmotic stress were monitored using the next-generation sequencing of whole transcriptome with samples taken from gills. By RNAseq technology, transcript catalogs of up- and down-regulated genes were generated from the oysters exposed to low and optimal salinity seawater. Methodology/Principal Findings Through Illumina sequencing, we reported 1665 up-regulated transcripts and 1815 down-regulated transcripts. A total of 45771 protein-coding contigs were identified from two groups based on sequence similarities with known proteins. As determined by GO annotation and KEGG pathway mapping, functional annotation of the genes recovered diverse biological functions and processes. The genes that changed expression significantly were highly represented in cellular process and regulation of biological process, intracellular and cell, binding and protein binding according to GO annotation. The results highlighted genes related to osmoregulation, signaling and interactions of osmotic stress response, anti-apoptotic reactions as well as immune response, cell adhesion and communication, cytoskeleton and cell cycle. Conclusions/Significance Through more than 1.5 million sequence reads and the expression data of the two libraries, the study provided some useful insights into signal transduction pathways in oysters and offered a number of candidate genes as potential markers of tolerance to hypoosmotic stress for oysters. In addition, the characterization of C. gigas transcriptome will not only provide a better understanding of the molecular mechanisms about the response to osmotic stress of the oysters, but also facilitate research into biological processes to find underlying physiological adaptations to hypoosmotic shock for marine invertebrates. PMID:23029449

Improving integrative searching of systems chemical biology data using semantic annotation.

PubMed

Chen, Bin; Ding, Ying; Wild, David J

2012-03-08

Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology. Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl. The document is available at http://chem2bio2owl.wikispaces.com.

Annotation: a computational solution for streamlining metabolomics analysis

PubMed Central

Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Benton, H. Paul; Siuzdak, Gary

2017-01-01

Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation. PMID:29039932

[Transcriptome analysis of Dunaliella viridis].

PubMed

Zhu, Shuai-qi; Gong, Yi-fu; Hang, Yu-qing; Liu, Hao; Wang, He-yu

2015-08-01

In order to understand the gene information, function, haloduric pathway (glycerolipid metabolism) and related key genes for Dunaliella viridis, we used Illumina HiSeqTM 2000 high-throughput sequencing technology to sequence its transcriptome. Trinity soft was used to assemble the data to form transcripts. Based on the Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG ) databases, we carried out functional annotation and classification, pathway annotation, and the opening reading fragment (ORF) sequence prediction of transcripts. The key genes in the glycerolipid metabolism were analyzed. The results suggested that 81,593 transcripts were found, and 77,117 ORF sequences were predicted, accounting for 94.50% of all transcripts. COG classification results showed that 16,569 transcripts were assigned to 24 categories. GO classification annotated 76,436 transcripts. The number of transcripts for biologcial processes was 30,678, accounting for 40.14% of all transcripts. KEGG pathway analysis showed that 26,428 transcripts were annotated to 317 pathways, and 131 pathways were related to metabolism, accounting for 41.32% of all annotated pathways. Only one transcript was annotated as coding the key enzyme dihydroxyacetone kinase involved in the glycerolipid pathway. This enzyme could be related to glycerol biosynthesis under salt stress. This study further improved the gene information and laid the foundation of metabolic pathway research for Dunaliella viridis.

Computational annotation of genes differentially expressed along olive fruit development

PubMed Central

Galla, Giulio; Barcaccia, Gianni; Ramina, Angelo; Collani, Silvio; Alagna, Fiammetta; Baldoni, Luciana; Cultrera, Nicolò GM; Martinelli, Federico; Sebastiani, Luca; Tonutti, Pietro

2009-01-01

Background Olea europaea L. is a traditional tree crop of the Mediterranean basin with a worldwide economical high impact. Differently from other fruit tree species, little is known about the physiological and molecular basis of the olive fruit development and a few sequences of genes and gene products are available for olive in public databases. This study deals with the identification of large sets of differentially expressed genes in developing olive fruits and the subsequent computational annotation by means of different software. Results mRNA from fruits of the cv. Leccino sampled at three different stages [i.e., initial fruit set (stage 1), completed pit hardening (stage 2) and veraison (stage 3)] was used for the identification of differentially expressed genes putatively involved in main processes along fruit development. Four subtractive hybridization libraries were constructed: forward and reverse between stage 1 and 2 (libraries A and B), and 2 and 3 (libraries C and D). All sequenced clones (1,132 in total) were analyzed through BlastX against non-redundant NCBI databases and about 60% of them showed similarity to known proteins. A total of 89 out of 642 differentially expressed unique sequences was further investigated by Real-Time PCR, showing a validation of the SSH results as high as 69%. Library-specific cDNA repertories were annotated according to the three main vocabularies of the gene ontology (GO): cellular component, biological process and molecular function. BlastX analysis, GO terms mapping and annotation analysis were performed using the Blast2GO software, a research tool designed with the main purpose of enabling GO based data mining on sequence sets for which no GO annotation is yet available. Bioinformatic analysis pointed out a significantly different distribution of the annotated sequences for each GO category, when comparing the three fruit developmental stages. The olive fruit-specific transcriptome dataset was used to query all known KEGG (Kyoto Encyclopaedia of Genes and Genomes) metabolic pathways for characterizing and positioning retrieved EST records. The integration of the olive sequence datasets within the MapMan platform for microarray analysis allowed the identification of specific biosynthetic pathways useful for the definition of key functional categories in time course analyses for gene groups. Conclusion The bioinformatic annotation of all gene sequences was useful to shed light on metabolic pathways and transcriptional aspects related to carbohydrates, fatty acids, secondary metabolites, transcription factors and hormones as well as response to biotic and abiotic stresses throughout olive drupe development. These results represent a first step toward both functional genomics and systems biology research for understanding the gene functions and regulatory networks in olive fruit growth and ripening. PMID:19852839

categoryCompare, an analytical tool based on feature annotations

PubMed Central

Flight, Robert M.; Harrison, Benjamin J.; Mohammad, Fahim; Bunge, Mary B.; Moon, Lawrence D. F.; Petruska, Jeffrey C.; Rouchka, Eric C.

2014-01-01

Assessment of high-throughput—omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered “biomarkers.” The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, categoryCompare, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohn's disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohn's vs. UC comparison showed gross similarities in inflammatory response in the two diseases, with particular processes specific to each disease. PMID:24808906

A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes.

PubMed

Islam, Mohammad Tawhidul; Mohamedali, Abidali; Ahn, Seong Beom; Nawar, Ishmam; Baker, Mark S; Ranganathan, Shoba

2017-01-01

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

Comparative salivary proteomics analysis of children with and without dental caries using the iTRAQ/MRM approach.

PubMed

Wang, Kun; Wang, Yufei; Wang, Xiuqing; Ren, Qian; Han, Sili; Ding, Longjiang; Li, Zhongcheng; Zhou, Xuedong; Li, Wei; Zhang, Linglin

2018-01-19

Dental caries is a major worldwide oral disease afflicting a large proportion of children. As an important host factor of caries susceptibility, saliva plays a significant role in the occurrence and development of caries. The aim of the present study was to characterize the healthy and cariogenic salivary proteome and determine the changes in salivary protein expression of children with varying degrees of active caries, also to establish salivary proteome profiles with a potential therapeutic use against dental caries. In this study, unstimulated saliva samples were collected from 30 children (age 10-12 years) with no dental caries (NDC, n = 10), low dental caries (LDC, n = 10), and high dental caries (HDC, n = 10). Salivary proteins were extracted, reduced, alkylated, trypsin digested and labeled with isobaric tags for relative and absolute quantitation, and then they were analyzed with GO annotation, biological pathway analysis, hierarchical clustering analysis, and protein-protein interaction analysis. Targeted verifications were then performed using multiple reaction monitoring mass spectrometry. A total of 244 differentially expressed proteins annotated with GO annotation in biological processes, cellular component and molecular function were identified in comparisons among children with varying degrees of active caries. A number of caries-related proteins as well as pathways were identified in this study. As compared with caries-free children, the most significantly enriched pathways involved by the up-regulated proteins in LDC and HDC were the ubiquitin mediated proteolysis pathway and African trypanosomiasis pathway, respectively. Subsequently, we selected 53 target proteins with differential expression in different comparisons, including mucin 7, mucin 5B, histatin 1, cystatin S and cystatin SN, basic salivary proline rich protein 2, for further verification using MRM assays. Protein-protein interaction analysis of these proteins revealed complex protein interaction networks, indicating synergistic action of salivary proteins in caries resistance or cariogenicity. Overall, our results afford new insight into the salivary proteome of children with dental caries. These findings might have bright prospect in future in developing novel biomimetic peptides with preventive and therapeutic benefits for childhood caries.

Disentangling the multigenic and pleiotropic nature of molecular function

PubMed Central

2015-01-01

Background Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. PMID:26678917

The pathway not taken: understanding 'omics data in the perinatal context.

PubMed

Edlow, Andrea G; Slonim, Donna K; Wick, Heather C; Hui, Lisa; Bianchi, Diana W

2015-07-01

'Omics analysis of large datasets has an increasingly important role in perinatal research, but understanding gene expression analyses in the fetal context remains a challenge. We compared the interpretation provided by a widely used systems biology resource (ingenuity pathway analysis [IPA]) with that from gene set enrichment analysis (GSEA) with functional annotation curated specifically for the fetus (Developmental FunctionaL Annotation at Tufts [DFLAT]). Using amniotic fluid supernatant transcriptome datasets previously produced by our group, we analyzed 3 different developmental perturbations: aneuploidy (Trisomy 21 [T21]), hemodynamic (twin-twin transfusion syndrome [TTTS]), and metabolic (maternal obesity) vs sex- and gestational age-matched control subjects. Differentially expressed probe sets were identified with the use of paired t-tests with the Benjamini-Hochberg correction for multiple testing (P < .05). Functional analyses were performed with IPA and GSEA/DFLAT. Outputs were compared for biologic relevance to the fetus. Compared with control subjects, there were 414 significantly dysregulated probe sets in T21 fetuses, 2226 in TTTS recipient twins, and 470 in fetuses of obese women. Each analytic output was unique but complementary. For T21, both IPA and GSEA/DFLAT identified dysregulation of brain, cardiovascular, and integumentary system development. For TTTS, both analytic tools identified dysregulation of cell growth/proliferation, immune and inflammatory signaling, brain, and cardiovascular development. For maternal obesity, both tools identified dysregulation of immune and inflammatory signaling, brain and musculoskeletal development, and cell death. GSEA/DFLAT identified substantially more dysregulated biologic functions in fetuses of obese women (1203 vs 151). For all 3 datasets, GSEA/DFLAT provided more comprehensive information about brain development. IPA consistently provided more detailed annotation about cell death. IPA produced many dysregulated terms that pertained to cancer (14 in T21, 109 in TTTS, 26 in maternal obesity); GSEA/DFLAT did not. Interpretation of the fetal amniotic fluid supernatant transcriptome depends on the analytic program, which suggests that >1 resource should be used. Within IPA, physiologic cellular proliferation in the fetus produced many "false positive" annotations that pertained to cancer, which reflects its bias toward adult diseases. This study supports the use of gene annotation resources with a developmental focus, such as DFLAT, for 'omics studies in perinatal medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

SacconePhD, Scott F; Chesler, Elissa J; Bierut, Laura J

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well representedmore » by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.« less

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

PubMed

Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

2017-01-01

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Computational analysis of microRNA function in heart development.

PubMed

Liu, Ganqiang; Ding, Min; Chen, Jiajia; Huang, Jinyan; Wang, Haiyun; Jing, Qing; Shen, Bairong

2010-09-01

Emerging evidence suggests that specific spatio-temporal microRNA (miRNA) expression is required for heart development. In recent years, hundreds of miRNAs have been discovered. In contrast, functional annotations are available only for a very small fraction of these regulatory molecules. In order to provide a global perspective for the biologists who study the relationship between differentially expressed miRNAs and heart development, we employed computational analysis to uncover the specific cellular processes and biological pathways targeted by miRNAs in mouse heart development. Here, we utilized Gene Ontology (GO) categories, KEGG Pathway, and GeneGo Pathway Maps as a gene functional annotation system for miRNA target enrichment analysis. The target genes of miRNAs were found to be enriched in functional categories and pathway maps in which miRNAs could play important roles during heart development. Meanwhile, we developed miRHrt (http://sysbio.suda.edu.cn/mirhrt/), a database aiming to provide a comprehensive resource of miRNA function in regulating heart development. These computational analysis results effectively illustrated the correlation of differentially expressed miRNAs with cellular functions and heart development. We hope that the identified novel heart development-associated pathways and the database presented here would facilitate further understanding of the roles and mechanisms of miRNAs in heart development.

XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits.

PubMed

Fang, Hai; Knezevic, Bogdan; Burnham, Katie L; Knight, Julian C

2016-12-13

Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem. We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants. XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at http://galahad.well.ox.ac.uk/XGR .

Efficient searching and annotation of metabolic networks using chemical similarity

PubMed Central

Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Tyo, Keith E.J.

2015-01-01

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Availability and implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/. Contact: k-tyo@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25417203

Efficient searching and annotation of metabolic networks using chemical similarity.

PubMed

Pertusi, Dante A; Stine, Andrew E; Broadbelt, Linda J; Tyo, Keith E J

2015-04-01

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Python files will be available for download at http://tyolab.northwestern.edu/tools/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Co-clustering phenome–genome for phenotype classification and disease gene discovery

PubMed Central

Hwang, TaeHyun; Atluri, Gowtham; Xie, MaoQiang; Dey, Sanjoy; Hong, Changjin; Kumar, Vipin; Kuang, Rui

2012-01-01

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways. PMID:22735708

Mining disease fingerprints from within genetic pathways.

PubMed

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.

Mining Disease Fingerprints From Within Genetic Pathways

PubMed Central

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components (‘fingerprints’) of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ∼77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways. PMID:23304411

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies

PubMed Central

2013-01-01

Background The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the “correlation” of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. Results Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method’s performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. Conclusions The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network. PMID:23432934

Reconstruction of metabolic pathways for the cattle genome

PubMed Central

Seo, Seongwon; Lewin, Harris A

2009-01-01

Background Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement. Results An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly. Conclusion CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology. PMID:19284618

Quantification of the impact of PSI:Biology according to the annotations of the determined structures.

PubMed

DePietro, Paul J; Julfayev, Elchin S; McLaughlin, William A

2013-10-21

Protein Structure Initiative:Biology (PSI:Biology) is the third phase of PSI where protein structures are determined in high-throughput to characterize their biological functions. The transition to the third phase entailed the formation of PSI:Biology Partnerships which are composed of structural genomics centers and biomedical science laboratories. We present a method to examine the impact of protein structures determined under the auspices of PSI:Biology by measuring their rates of annotations. The mean numbers of annotations per structure and per residue are examined. These are designed to provide measures of the amount of structure to function connections that can be leveraged from each structure. One result is that PSI:Biology structures are found to have a higher rate of annotations than structures determined during the first two phases of PSI. A second result is that the subset of PSI:Biology structures determined through PSI:Biology Partnerships have a higher rate of annotations than those determined exclusive of those partnerships. Both results hold when the annotation rates are examined either at the level of the entire protein or for annotations that are known to fall at specific residues within the portion of the protein that has a determined structure. We conclude that PSI:Biology determines structures that are estimated to have a higher degree of biomedical interest than those determined during the first two phases of PSI based on a broad array of biomedical annotations. For the PSI:Biology Partnerships, we see that there is an associated added value that represents part of the progress toward the goals of PSI:Biology. We interpret the added value to mean that team-based structural biology projects that utilize the expertise and technologies of structural genomics centers together with biological laboratories in the community are conducted in a synergistic manner. We show that the annotation rates can be used in conjunction with established metrics, i.e. the numbers of structures and impact of publication records, to monitor the progress of PSI:Biology towards its goals of examining structure to function connections of high biomedical relevance. The metric provides an objective means to quantify the overall impact of PSI:Biology as it uses biomedical annotations from external sources.

Quantification of the impact of PSI:Biology according to the annotations of the determined structures

PubMed Central

2013-01-01

Background Protein Structure Initiative:Biology (PSI:Biology) is the third phase of PSI where protein structures are determined in high-throughput to characterize their biological functions. The transition to the third phase entailed the formation of PSI:Biology Partnerships which are composed of structural genomics centers and biomedical science laboratories. We present a method to examine the impact of protein structures determined under the auspices of PSI:Biology by measuring their rates of annotations. The mean numbers of annotations per structure and per residue are examined. These are designed to provide measures of the amount of structure to function connections that can be leveraged from each structure. Results One result is that PSI:Biology structures are found to have a higher rate of annotations than structures determined during the first two phases of PSI. A second result is that the subset of PSI:Biology structures determined through PSI:Biology Partnerships have a higher rate of annotations than those determined exclusive of those partnerships. Both results hold when the annotation rates are examined either at the level of the entire protein or for annotations that are known to fall at specific residues within the portion of the protein that has a determined structure. Conclusions We conclude that PSI:Biology determines structures that are estimated to have a higher degree of biomedical interest than those determined during the first two phases of PSI based on a broad array of biomedical annotations. For the PSI:Biology Partnerships, we see that there is an associated added value that represents part of the progress toward the goals of PSI:Biology. We interpret the added value to mean that team-based structural biology projects that utilize the expertise and technologies of structural genomics centers together with biological laboratories in the community are conducted in a synergistic manner. We show that the annotation rates can be used in conjunction with established metrics, i.e. the numbers of structures and impact of publication records, to monitor the progress of PSI:Biology towards its goals of examining structure to function connections of high biomedical relevance. The metric provides an objective means to quantify the overall impact of PSI:Biology as it uses biomedical annotations from external sources. PMID:24139526

Metabolic pathways for the whole community.

PubMed

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

An integrated and comparative approach towards identification, characterization and functional annotation of candidate genes for drought tolerance in sorghum (Sorghum bicolor (L.) Moench).

PubMed

Woldesemayat, Adugna Abdi; Van Heusden, Peter; Ndimba, Bongani K; Christoffels, Alan

2017-12-22

Drought is the most disastrous abiotic stress that severely affects agricultural productivity worldwide. Understanding the biological basis of drought-regulated traits, requires identification and an in-depth characterization of genetic determinants using model organisms and high-throughput technologies. However, studies on drought tolerance have generally been limited to traditional candidate gene approach that targets only a single gene in a pathway that is related to a trait. In this study, we used sorghum, one of the model crops that is well adapted to arid regions, to mine genes and define determinants for drought tolerance using drought expression libraries and RNA-seq data. We provide an integrated and comparative in silico candidate gene identification, characterization and annotation approach, with an emphasis on genes playing a prominent role in conferring drought tolerance in sorghum. A total of 470 non-redundant functionally annotated drought responsive genes (DRGs) were identified using experimental data from drought responses by employing pairwise sequence similarity searches, pathway and interpro-domain analysis, expression profiling and orthology relation. Comparison of the genomic locations between these genes and sorghum quantitative trait loci (QTLs) showed that 40% of these genes were co-localized with QTLs known for drought tolerance. The genome reannotation conducted using the Program to Assemble Spliced Alignment (PASA), resulted in 9.6% of existing single gene models being updated. In addition, 210 putative novel genes were identified using AUGUSTUS and PASA based analysis on expression dataset. Among these, 50% were single exonic, 69.5% represented drought responsive and 5.7% were complete gene structure models. Analysis of biochemical metabolism revealed 14 metabolic pathways that are related to drought tolerance and also had a strong biological network, among categories of genes involved. Identification of these pathways, signifies the interplay of biochemical reactions that make up the metabolic network, constituting fundamental interface for sorghum defence mechanism against drought stress. This study suggests untapped natural variability in sorghum that could be used for developing drought tolerance. The data presented here, may be regarded as an initial reference point in functional and comparative genomics in the Gramineae family.

Effect of curcumin on aged Drosophila melanogaster: a pathway prediction analysis.

PubMed

Zhang, Zhi-guo; Niu, Xu-yan; Lu, Ai-ping; Xiao, Gary Guishan

2015-02-01

To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

microRNAs Databases: Developmental Methodologies, Structural and Functional Annotations.

PubMed

Singh, Nagendra Kumar

2017-09-01

microRNA (miRNA) is an endogenous and evolutionary conserved non-coding RNA, involved in post-transcriptional process as gene repressor and mRNA cleavage through RNA-induced silencing complex (RISC) formation. In RISC, miRNA binds in complementary base pair with targeted mRNA along with Argonaut proteins complex, causes gene repression or endonucleolytic cleavage of mRNAs and results in many diseases and syndromes. After the discovery of miRNA lin-4 and let-7, subsequently large numbers of miRNAs were discovered by low-throughput and high-throughput experimental techniques along with computational process in various biological and metabolic processes. The miRNAs are important non-coding RNA for understanding the complex biological phenomena of organism because it controls the gene regulation. This paper reviews miRNA databases with structural and functional annotations developed by various researchers. These databases contain structural and functional information of animal, plant and virus miRNAs including miRNAs-associated diseases, stress resistance in plant, miRNAs take part in various biological processes, effect of miRNAs interaction on drugs and environment, effect of variance on miRNAs, miRNAs gene expression analysis, sequence of miRNAs, structure of miRNAs. This review focuses on the developmental methodology of miRNA databases such as computational tools and methods used for extraction of miRNAs annotation from different resources or through experiment. This study also discusses the efficiency of user interface design of every database along with current entry and annotations of miRNA (pathways, gene ontology, disease ontology, etc.). Here, an integrated schematic diagram of construction process for databases is also drawn along with tabular and graphical comparison of various types of entries in different databases. Aim of this paper is to present the importance of miRNAs-related resources at a single place.

The Gene Ontology of eukaryotic cilia and flagella.

PubMed

Roncaglia, Paola; van Dam, Teunis J P; Christie, Karen R; Nacheva, Lora; Toedt, Grischa; Huynen, Martijn A; Huntley, Rachael P; Gibson, Toby J; Lomax, Jane

2017-01-01

Recent research into ciliary structure and function provides important insights into inherited diseases termed ciliopathies and other cilia-related disorders. This wealth of knowledge needs to be translated into a computational representation to be fully exploitable by the research community. To this end, members of the Gene Ontology (GO) and SYSCILIA Consortia have worked together to improve representation of ciliary substructures and processes in GO. Members of the SYSCILIA and Gene Ontology Consortia suggested additions and changes to GO, to reflect new knowledge in the field. The project initially aimed to improve coverage of ciliary parts, and was then broadened to cilia-related biological processes. Discussions were documented in a public tracker. We engaged the broader cilia community via direct consultation and by referring to the literature. Ontology updates were implemented via ontology editing tools. So far, we have created or modified 127 GO terms representing parts and processes related to eukaryotic cilia/flagella or prokaryotic flagella. A growing number of biological pathways are known to involve cilia, and we continue to incorporate this knowledge in GO. The resulting expansion in GO allows more precise representation of experimentally derived knowledge, and SYSCILIA and GO biocurators have created 199 annotations to 50 human ciliary proteins. The revised ontology was also used to curate mouse proteins in a collaborative project. The revised GO and annotations, used in comparative 'before and after' analyses of representative ciliary datasets, improve enrichment results significantly. Our work has resulted in a broader and deeper coverage of ciliary composition and function. These improvements in ontology and protein annotation will benefit all users of GO enrichment analysis tools, as well as the ciliary research community, in areas ranging from microscopy image annotation to interpretation of high-throughput studies. We welcome feedback to further enhance the representation of cilia biology in GO.

Transcriptomic analysis of flower development in wintersweet (Chimonanthus praecox).

PubMed

Liu, Daofeng; Sui, Shunzhao; Ma, Jing; Li, Zhineng; Guo, Yulong; Luo, Dengpan; Yang, Jianfeng; Li, Mingyang

2014-01-01

Wintersweet (Chimonanthus praecox) is familiar as a garden plant and woody ornamental flower. On account of its unique flowering time and strong fragrance, it has a high ornamental and economic value. Despite a long history of human cultivation, our understanding of wintersweet genetics and molecular biology remains scant, reflecting a lack of basic genomic and transcriptomic data. In this study, we assembled three cDNA libraries, from three successive stages in flower development, designated as the flower bud with displayed petal, open flower and senescing flower stages. Using the Illumina RNA-Seq method, we obtained 21,412,928, 26,950,404, 24,912,954 qualified Illumina reads, respectively, for the three successive stages. The pooled reads from all three libraries were then assembled into 106,995 transcripts, 51,793 of which were annotated in the NCBI non-redundant protein database. Of these annotated sequences, 32,649 and 21,893 transcripts were assigned to gene ontology categories and clusters of orthologous groups, respectively. We could map 15,587 transcripts onto 312 pathways using the Kyoto Encyclopedia of Genes and Genomes pathway database. Based on these transcriptomic data, we obtained a large number of candidate genes that were differentially expressed at the open flower and senescing flower stages. An analysis of differentially expressed genes involved in plant hormone signal transduction pathways indicated that although flower opening and senescence may be independent of the ethylene signaling pathway in wintersweet, salicylic acid may be involved in the regulation of flower senescence. We also succeeded in isolating key genes of floral scent biosynthesis and proposed a biosynthetic pathway for monoterpenes and sesquiterpenes in wintersweet flowers, based on the annotated sequences. This comprehensive transcriptomic analysis presents fundamental information on the genes and pathways which are involved in flower development in wintersweet. And our data provided a useful database for further research of wintersweet and other Calycanthaceae family plants.

Transcriptomic Analysis of Flower Development in Wintersweet (Chimonanthus praecox)

PubMed Central

Liu, Daofeng; Sui, Shunzhao; Ma, Jing; Li, Zhineng; Guo, Yulong; Luo, Dengpan; Yang, Jianfeng; Li, Mingyang

2014-01-01

Wintersweet (Chimonanthus praecox) is familiar as a garden plant and woody ornamental flower. On account of its unique flowering time and strong fragrance, it has a high ornamental and economic value. Despite a long history of human cultivation, our understanding of wintersweet genetics and molecular biology remains scant, reflecting a lack of basic genomic and transcriptomic data. In this study, we assembled three cDNA libraries, from three successive stages in flower development, designated as the flower bud with displayed petal, open flower and senescing flower stages. Using the Illumina RNA-Seq method, we obtained 21,412,928, 26,950,404, 24,912,954 qualified Illumina reads, respectively, for the three successive stages. The pooled reads from all three libraries were then assembled into 106,995 transcripts, 51,793 of which were annotated in the NCBI non-redundant protein database. Of these annotated sequences, 32,649 and 21,893 transcripts were assigned to gene ontology categories and clusters of orthologous groups, respectively. We could map 15,587 transcripts onto 312 pathways using the Kyoto Encyclopedia of Genes and Genomes pathway database. Based on these transcriptomic data, we obtained a large number of candidate genes that were differentially expressed at the open flower and senescing flower stages. An analysis of differentially expressed genes involved in plant hormone signal transduction pathways indicated that although flower opening and senescence may be independent of the ethylene signaling pathway in wintersweet, salicylic acid may be involved in the regulation of flower senescence. We also succeeded in isolating key genes of floral scent biosynthesis and proposed a biosynthetic pathway for monoterpenes and sesquiterpenes in wintersweet flowers, based on the annotated sequences. This comprehensive transcriptomic analysis presents fundamental information on the genes and pathways which are involved in flower development in wintersweet. And our data provided a useful database for further research of wintersweet and other Calycanthaceae family plants. PMID:24489818

A draft genome assembly of the army worm, Spodoptera frugiperda.

PubMed

Kakumani, Pavan Kumar; Malhotra, Pawan; Mukherjee, Sunil K; Bhatnagar, Raj K

2014-08-01

Spodoptera is an agriculturally important pest insect and studies in understanding its biology have been limited by the unavailability of its genome. In the present study, the genomic DNA was sequenced and assembled into 37,243 scaffolds of size, 358 Mb with N50 of 53.7 kb. Based on degree of identity, we could anchor 305 Mb of the genome onto all the 28 chromosomes of Bombyx mori. Repeat elements were identified, which accounts for 20.28% of the total genome. Further, we predicted 11,595 genes, with an average intron length of 726 bp. The genes were annotated and domain analysis revealed that Sf genes share a significant homology and expression pattern with B. mori, despite differences in KOG gene categories and representation of certain protein families. The present study on Sf genome would help in the characterization of cellular pathways to understand its biology and comparative evolutionary studies among lepidopteran family members to help annotate their genomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

DOE PAGES

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; ...

2014-10-16

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.« less

Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

PubMed Central

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.

2014-01-01

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface. PMID:25329157

Getting the most out of parasitic helminth transcriptomes using HelmDB: implications for biology and biotechnology.

PubMed

Mangiola, Stefano; Young, Neil D; Korhonen, Pasi; Mondal, Alinda; Scheerlinck, Jean-Pierre; Sternberg, Paul W; Cantacessi, Cinzia; Hall, Ross S; Jex, Aaron R; Gasser, Robin B

2013-12-01

Compounded by a massive global food shortage, many parasitic diseases have a devastating, long-term impact on animal and human health and welfare worldwide. Parasitic helminths (worms) affect the health of billions of animals. Unlocking the systems biology of these neglected pathogens will underpin the design of new and improved interventions against them. Currently, the functional annotation of genomic and transcriptomic sequence data for socio-economically important parasitic worms relies almost exclusively on comparative bioinformatic analyses using model organism- and other databases. However, many genes and gene products of parasitic helminths (often >50%) cannot be annotated using this approach, because they are specific to parasites and/or do not have identifiable homologs in other organisms for which sequence data are available. This inability to fully annotate transcriptomes and predicted proteomes is a major challenge and constrains our understanding of the biology of parasites, interactions with their hosts and of parasitism and the pathogenesis of disease on a molecular level. In the present article, we compiled transcriptomic data sets of key, socioeconomically important parasitic helminths, and constructed and validated a curated database, called HelmDB (www.helmdb.org). We demonstrate how this database can be used effectively for the improvement of functional annotation by employing data integration and clustering. Importantly, HelmDB provides a practical and user-friendly toolkit for sequence browsing and comparative analyses among divergent helminth groups (including nematodes and trematodes), and should be readily adaptable and applicable to a wide range of other organisms. This web-based, integrative database should assist 'systems biology' studies of parasitic helminths, and the discovery and prioritization of novel drug and vaccine targets. This focus provides a pathway toward developing new and improved approaches for the treatment and control of parasitic diseases, with the potential for important biotechnological outcomes. Copyright © 2012 Elsevier Inc. All rights reserved.

DNAtraffic--a new database for systems biology of DNA dynamics during the cell life.

PubMed

Kuchta, Krzysztof; Barszcz, Daniela; Grzesiuk, Elzbieta; Pomorski, Pawel; Krwawicz, Joanna

2012-01-01

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications.

DNAtraffic—a new database for systems biology of DNA dynamics during the cell life

PubMed Central

Kuchta, Krzysztof; Barszcz, Daniela; Grzesiuk, Elzbieta; Pomorski, Pawel; Krwawicz, Joanna

2012-01-01

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications. PMID:22110027

Annotated chemical patent corpus: a gold standard for text mining.

PubMed

Akhondi, Saber A; Klenner, Alexander G; Tyrchan, Christian; Manchala, Anil K; Boppana, Kiran; Lowe, Daniel; Zimmermann, Marc; Jagarlapudi, Sarma A R P; Sayle, Roger; Kors, Jan A; Muresan, Sorel

2014-01-01

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.

Annotated Chemical Patent Corpus: A Gold Standard for Text Mining

PubMed Central

Akhondi, Saber A.; Klenner, Alexander G.; Tyrchan, Christian; Manchala, Anil K.; Boppana, Kiran; Lowe, Daniel; Zimmermann, Marc; Jagarlapudi, Sarma A. R. P.; Sayle, Roger; Kors, Jan A.; Muresan, Sorel

2014-01-01

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org. PMID:25268232

Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue.

PubMed

Kerkentzes, Konstantinos; Lagani, Vincenzo; Tsamardinos, Ioannis; Vyberg, Mogens; Røe, Oluf Dimitri

2014-01-01

Novel statistical methods and increasingly more accurate gene annotations can transform "old" biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches. The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways. Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93-100% (AUC = 0.93-1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue. Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.

Integrating physical and genetic maps: from genomes to interaction networks

PubMed Central

Beyer, Andreas; Bandyopadhyay, Sourav; Ideker, Trey

2009-01-01

Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree. PMID:17703239

BiologicalNetworks 2.0 - an integrative view of genome biology data

PubMed Central

2010-01-01

Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other) and their relations (interactions, co-expression, co-citations, and other). The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org. PMID:21190573

The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation.

PubMed

Kocher, Jean-Pierre A; Quest, Daniel J; Duffy, Patrick; Meiners, Michael A; Moore, Raymond M; Rider, David; Hossain, Asif; Hart, Steven N; Dinu, Valentin

2014-07-01

The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines. The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu. © The Author 2014. Published by Oxford University Press.

In silico analysis of expressed sequence tags from Trichostrongylus vitrinus (Nematoda): comparison of the automated ESTExplorer workflow platform with conventional database searches.

PubMed

Nagaraj, Shivashankar H; Gasser, Robin B; Nisbet, Alasdair J; Ranganathan, Shoba

2008-01-01

The analysis of expressed sequence tags (EST) offers a rapid and cost effective approach to elucidate the transcriptome of an organism, but requires several computational methods for assembly and annotation. Researchers frequently analyse each step manually, which is laborious and time consuming. We have recently developed ESTExplorer, a semi-automated computational workflow system, in order to achieve the rapid analysis of EST datasets. In this study, we evaluated EST data analysis for the parasitic nematode Trichostrongylus vitrinus (order Strongylida) using ESTExplorer, compared with database matching alone. We functionally annotated 1776 ESTs obtained via suppressive-subtractive hybridisation from T. vitrinus, an important parasitic trichostrongylid of small ruminants. Cluster and comparative genomic analyses of the transcripts using ESTExplorer indicated that 290 (41%) sequences had homologues in Caenorhabditis elegans, 329 (42%) in parasitic nematodes, 202 (28%) in organisms other than nematodes, and 218 (31%) had no significant match to any sequence in the current databases. Of the C. elegans homologues, 90 were associated with 'non-wildtype' double-stranded RNA interference (RNAi) phenotypes, including embryonic lethality, maternal sterility, sterile progeny, larval arrest and slow growth. We could functionally classify 267 (38%) sequences using the Gene Ontologies (GO) and establish pathway associations for 230 (33%) sequences using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Further examination of this EST dataset revealed a number of signalling molecules, proteases, protease inhibitors, enzymes, ion channels and immune-related genes. In addition, we identified 40 putative secreted proteins that could represent potential candidates for developing novel anthelmintics or vaccines. We further compared the automated EST sequence annotations, using ESTExplorer, with database search results for individual T. vitrinus ESTs. ESTExplorer reliably and rapidly annotated 301 ESTs, with pathway and GO information, eliminating 60 low quality hits from database searches. We evaluated the efficacy of ESTExplorer in analysing EST data, and demonstrate that computational tools can be used to accelerate the process of gene discovery in EST sequencing projects. The present study has elucidated sets of relatively conserved and potentially novel genes for biological investigation, and the annotated EST set provides further insight into the molecular biology of T. vitrinus, towards the identification of novel drug targets.

Shotgun proteomic analysis of Bombyx mori brain: emphasis on regulation of behavior and development of the nervous system.

PubMed

Wang, Guo-Bao; Zheng, Qin; Shen, Yun-Wang; Wu, Xiao-Feng

2016-02-01

The insect brain plays crucial roles in the regulation of growth and development and in all types of behavior. We used sodium dodecyl sulfate polyacrylamide gel electrophoresis and high-performance liquid chromatography - electron spray ionization tandem mass spectrometry (ESI-MS/MS) shotgun to identify the proteome of the silkworm brain, to investigate its protein composition and to understand their biological functions. A total of 2210 proteins with molecular weights in the range of 5.64-1539.82 kDa and isoelectric points in the range of 3.78-12.55 were identified. These proteins were annotated according to Gene Ontology Annotation into the categories of molecular function, biological process and cellular component. We characterized two categories of proteins: one includes behavior-related proteins involved in the regulation of behaviors, such as locomotion, reproduction and learning; the other consists of proteins related to the development or function of the nervous system. The identified proteins were classified into 283 different pathways according to KEGG analysis, including the PI3K-Akt signaling pathway which plays a crucial role in mediating survival signals in a wide range of neuronal cell types. This extensive protein profile provides a basis for further understanding of the physiological functions in the silkworm brain. © 2014 Institute of Zoology, Chinese Academy of Sciences.

Highlighting the Need for Systems-Level Experimental Characterization of Plant Metabolic Enzymes.

PubMed

Engqvist, Martin K M

2016-01-01

The biology of living organisms is determined by the action and interaction of a large number of individual gene products, each with specific functions. Discovering and annotating the function of gene products is key to our understanding of these organisms. Controlled experiments and bioinformatic predictions both contribute to functional gene annotation. For most species it is difficult to gain an overview of what portion of gene annotations are based on experiments and what portion represent predictions. Here, I survey the current state of experimental knowledge of enzymes and metabolism in Arabidopsis thaliana as well as eleven economically important crops and forestry trees - with a particular focus on reactions involving organic acids in central metabolism. I illustrate the limited availability of experimental data for functional annotation of enzymes in most of these species. Many enzymes involved in metabolism of citrate, malate, fumarate, lactate, and glycolate in crops and forestry trees have not been characterized. Furthermore, enzymes involved in key biosynthetic pathways which shape important traits in crops and forestry trees have not been characterized. I argue for the development of novel high-throughput platforms with which limited functional characterization of gene products can be performed quickly and relatively cheaply. I refer to this approach as systems-level experimental characterization. The data collected from such platforms would form a layer intermediate between bioinformatic gene function predictions and in-depth experimental studies of these functions. Such a data layer would greatly aid in the pursuit of understanding a multiplicity of biological processes in living organisms.

Expanding the horizons of microRNA bioinformatics.

PubMed

Huntley, Rachael P; Kramarz, Barbara; Sawford, Tony; Umrao, Zara; Kalea, Anastasia Z; Acquaah, Vanessa; Martin, Maria-Jesus; Mayr, Manuel; Lovering, Ruth C

2018-06-05

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyse microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics datasets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 3,900 Gene Ontology annotations associated with almost 500 miRNAs from human, mouse and rat and over 2,200 experimentally validated miRNA:target interactions. We illustrate how this resource can be used to create miRNA-focused interaction networks with a biological context using the known biological role of miRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent datasets for reproducible functional analysis of microRNAs across all biological research areas. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

The Listeria monocytogenes strain 10403S BioCyc database

PubMed Central

Orsi, Renato H.; Bergholz, Teresa M.; Wiedmann, Martin; Boor, Kathryn J.

2015-01-01

Listeria monocytogenes is a food-borne pathogen of humans and other animals. The striking ability to survive several stresses usually used for food preservation makes L. monocytogenes one of the biggest concerns to the food industry, while the high mortality of listeriosis in specific groups of humans makes it a great concern for public health. Previous studies have shown that a regulatory network involving alternative sigma (σ) factors and transcription factors is pivotal to stress survival. However, few studies have evaluated at the metabolic networks controlled by these regulatory mechanisms. The L. monocytogenes BioCyc database uses the strain 10403S as a model. Computer-generated initial annotation for all genes also allowed for identification, annotation and display of predicted reactions and pathways carried out by a single cell. Further ongoing manual curation based on published data as well as database mining for selected genes allowed the more refined annotation of functions, which, in turn, allowed for annotation of new pathways and fine-tuning of previously defined pathways to more L. monocytogenes-specific pathways. Using RNA-Seq data, several transcription start sites and promoter regions were mapped to the 10403S genome and annotated within the database. Additionally, the identification of promoter regions and a comprehensive review of available literature allowed the annotation of several regulatory interactions involving σ factors and transcription factors. The L. monocytogenes 10403S BioCyc database is a new resource for researchers studying Listeria and related organisms. It allows users to (i) have a comprehensive view of all reactions and pathways predicted to take place within the cell in the cellular overview, as well as to (ii) upload their own data, such as differential expression data, to visualize the data in the scope of predicted pathways and regulatory networks and to carry on enrichment analyses using several different annotations available within the database. Database URL: http://biocyc.org/organism-summary?object=10403S_RAST PMID:25819074

The Listeria monocytogenes strain 10403S BioCyc database.

PubMed

Orsi, Renato H; Bergholz, Teresa M; Wiedmann, Martin; Boor, Kathryn J

2015-01-01

Listeria monocytogenes is a food-borne pathogen of humans and other animals. The striking ability to survive several stresses usually used for food preservation makes L. monocytogenes one of the biggest concerns to the food industry, while the high mortality of listeriosis in specific groups of humans makes it a great concern for public health. Previous studies have shown that a regulatory network involving alternative sigma (σ) factors and transcription factors is pivotal to stress survival. However, few studies have evaluated at the metabolic networks controlled by these regulatory mechanisms. The L. monocytogenes BioCyc database uses the strain 10403S as a model. Computer-generated initial annotation for all genes also allowed for identification, annotation and display of predicted reactions and pathways carried out by a single cell. Further ongoing manual curation based on published data as well as database mining for selected genes allowed the more refined annotation of functions, which, in turn, allowed for annotation of new pathways and fine-tuning of previously defined pathways to more L. monocytogenes-specific pathways. Using RNA-Seq data, several transcription start sites and promoter regions were mapped to the 10403S genome and annotated within the database. Additionally, the identification of promoter regions and a comprehensive review of available literature allowed the annotation of several regulatory interactions involving σ factors and transcription factors. The L. monocytogenes 10403S BioCyc database is a new resource for researchers studying Listeria and related organisms. It allows users to (i) have a comprehensive view of all reactions and pathways predicted to take place within the cell in the cellular overview, as well as to (ii) upload their own data, such as differential expression data, to visualize the data in the scope of predicted pathways and regulatory networks and to carry on enrichment analyses using several different annotations available within the database. © The Author(s) 2015. Published by Oxford University Press.

Next-generation sequencing analysis of gene regulation in the rat model of retinopathy of prematurity.

PubMed

Griffith, Rachel M; Li, Hu; Zhang, Nan; Favazza, Tara L; Fulton, Anne B; Hansen, Ronald M; Akula, James D

2013-08-01

The purpose of this study was to identify the genes, biochemical signaling pathways, and biological themes involved in the pathogenesis of retinopathy of prematurity (ROP). Next-generation sequencing (NGS) was performed on the RNA transcriptome of rats with the Penn et al. (Pediatr Res 36:724-731, 1994) oxygen-induced retinopathy model of ROP at the height of vascular abnormality, postnatal day (P) 19, and normalized to age-matched, room-air-reared littermate controls. Eight custom-developed pathways with potential relevance to known ROP sequelae were evaluated for significant regulation in ROP: The three major Wnt signaling pathways, canonical, planar cell polarity (PCP), and Wnt/Ca(2+); two signaling pathways mediated by the Rho GTPases RhoA and Cdc42, which are, respectively, thought to intersect with canonical and non-canonical Wnt signaling; nitric oxide signaling pathways mediated by two nitric oxide synthase (NOS) enzymes, neuronal (nNOS) and endothelial (eNOS); and the retinoic acid (RA) signaling pathway. Regulation of other biological pathways and themes was detected by gene ontology using the Kyoto Encyclopedia of Genes and Genomes and the NIH's Database for Annotation, Visualization, and Integrated Discovery's GO terms databases. Canonical Wnt signaling was found to be regulated, but the non-canonical PCP and Wnt/Ca(2+) pathways were not. Nitric oxide signaling, as measured by the activation of nNOS and eNOS, was also regulated, as was RA signaling. Biological themes related to protein translation (ribosomes), neural signaling, inflammation and immunity, cell cycle, and cell death were (among others) highly regulated in ROP rats. These several genes and pathways identified by NGS might provide novel targets for intervention in ROP.

Next Generation Sequencing Analysis of Gene Regulation in the Rat Model of Retinopathy of Prematurity

PubMed Central

Griffith, Rachel M.; Li, Hu; Zhang, Nan; Favazza, Tara L.; Fulton, Anne B.; Hansen, Ronald M.; Akula, James D.

2013-01-01

Purpose To identify the genes, biochemical signaling pathways and biological themes involved in the pathogenesis of retinopathy of prematurity (ROP). Methods Next-generation sequencing (NGS) was performed on the RNA transcriptome of rats with the Penn et al. (1994) oxygen-induced retinopathy (OIR) model of ROP at the height of vascular abnormality, postnatal day (P) 19, and normalized to age-matched, room-air-reared littermate controls. Eight custom developed pathways with potential relevance to known ROP sequelae were evaluated for significant regulation in ROP: The three major Wnt signaling pathways, canonical, planar cell polarity (PCP), and Wnt/Ca2+, two signaling pathways mediated by the Rho GTPases RhoA and Cdc42, which are respectively thought to intersect with canonical and noncanonical Wnt signaling, nitric oxide signaling pathways mediated by two nitrox oxide synthase (NOS) enzymes, neuronal (nNOS) and endothelial (eNOS), and the retinoic acid (RA) signaling pathway. Regulation of other biological pathways and themes were detected by gene ontology using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the NIH's Database for Annotation, Visualization and Integrated Discovery (DAVID)'s GO terms databases. Results Canonical Wnt signaling was found to be regulated, but the non-canonical PCP and Wnt/Ca2+ pathways were not. Nitric oxide (NO) signaling, as measured by the activation of nNOS eNOS, was also regulated, as was RA signaling. Biological themes related to protein translation (ribosomes), neural signaling, inflammation and immunity, cell cycle and cell death, were (among others) highly regulated in ROP rats. Conclusions These several genes and pathways identified by NGS might provide novel targets for intervention in ROP. PMID:23775346

Mapping annotations with textual evidence using an scLDA model.

PubMed

Jin, Bo; Chen, Vicky; Chen, Lujia; Lu, Xinghua

2011-01-01

Most of the knowledge regarding genes and proteins is stored in biomedical literature as free text. Extracting information from complex biomedical texts demands techniques capable of inferring biological concepts from local text regions and mapping them to controlled vocabularies. To this end, we present a sentence-based correspondence latent Dirichlet allocation (scLDA) model which, when trained with a corpus of PubMed documents with known GO annotations, performs the following tasks: 1) learning major biological concepts from the corpus, 2) inferring the biological concepts existing within text regions (sentences), and 3) identifying the text regions in a document that provides evidence for the observed annotations. When applied to new gene-related documents, a trained scLDA model is capable of predicting GO annotations and identifying text regions as textual evidence supporting the predicted annotations. This study uses GO annotation data as a testbed; the approach can be generalized to other annotated data, such as MeSH and MEDLINE documents.

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics

PubMed Central

Schoof, Heiko; Ernst, Rebecca; Nazarov, Vladimir; Pfeifer, Lukas; Mewes, Hans-Werner; Mayer, Klaus F. X.

2004-01-01

Arabidopsis thaliana is the most widely studied model plant. Functional genomics is intensively underway in many laboratories worldwide. Beyond the basic annotation of the primary sequence data, the annotated genetic elements of Arabidopsis must be linked to diverse biological data and higher order information such as metabolic or regulatory pathways. The MIPS Arabidopsis thaliana database MAtDB aims to provide a comprehensive resource for Arabidopsis as a genome model that serves as a primary reference for research in plants and is suitable for transfer of knowledge to other plants, especially crops. The genome sequence as a common backbone serves as a scaffold for the integration of data, while, in a complementary effort, these data are enhanced through the application of state-of-the-art bioinformatics tools. This information is visualized on a genome-wide and a gene-by-gene basis with access both for web users and applications. This report updates the information given in a previous report and provides an outlook on further developments. The MAtDB web interface can be accessed at http://mips.gsf.de/proj/thal/db. PMID:14681437

Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling–A Feasibility Study

PubMed Central

Weißenborn, Sandra; Walther, Dirk

2017-01-01

Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes. PMID:29163570

New challenges for text mining: mapping between text and manually curated pathways

PubMed Central

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community

PubMed Central

Hosmani, Prashant S.; Villalobos-Ayala, Krystal; Miller, Sherry; Shippy, Teresa; Flores, Mirella; Rosendale, Andrew; Cordola, Chris; Bell, Tracey; Mann, Hannah; DeAvila, Gabe; DeAvila, Daniel; Moore, Zachary; Buller, Kyle; Ciolkevich, Kathryn; Nandyal, Samantha; Mahoney, Robert; Van Voorhis, Joshua; Dunlevy, Megan; Farrow, David; Hunter, David; Morgan, Taylar; Shore, Kayla; Guzman, Victoria; Izsak, Allison; Dixon, Danielle E.; Cridge, Andrew; Cano, Liliana; Cao, Xiaolong; Jiang, Haobo; Leng, Nan; Johnson, Shannon; Cantarel, Brandi L.; Richards, Stephen; English, Adam; Shatters, Robert G.; Childers, Chris; Chen, Mei-Ju; Hunter, Wayne; Cilia, Michelle; Mueller, Lukas A.; Munoz-Torres, Monica; Nelson, David; Poelchau, Monica F.; Benoit, Joshua B.; Wiersma-Koch, Helen; D’Elia, Tom; Brown, Susan J.

2017-01-01

Abstract The Asian citrus psyllid (Diaphorina citri Kuwayama) is the insect vector of the bacterium Candidatus Liberibacter asiaticus (CLas), the pathogen associated with citrus Huanglongbing (HLB, citrus greening). HLB threatens citrus production worldwide. Suppression or reduction of the insect vector using chemical insecticides has been the primary method to inhibit the spread of citrus greening disease. Accurate structural and functional annotation of the Asian citrus psyllid genome, as well as a clear understanding of the interactions between the insect and CLas, are required for development of new molecular-based HLB control methods. A draft assembly of the D. citri genome has been generated and annotated with automated pipelines. However, knowledge transfer from well-curated reference genomes such as that of Drosophila melanogaster to newly sequenced ones is challenging due to the complexity and diversity of insect genomes. To identify and improve gene models as potential targets for pest control, we manually curated several gene families with a focus on genes that have key functional roles in D. citri biology and CLas interactions. This community effort produced 530 manually curated gene models across developmental, physiological, RNAi regulatory and immunity-related pathways. As previously shown in the pea aphid, RNAi machinery genes putatively involved in the microRNA pathway have been specifically duplicated. A comprehensive transcriptome enabled us to identify a number of gene families that are either missing or misassembled in the draft genome. In order to develop biocuration as a training experience, we included undergraduate and graduate students from multiple institutions, as well as experienced annotators from the insect genomics research community. The resulting gene set (OGS v1.0) combines both automatically predicted and manually curated gene models. Database URL: https://citrusgreening.org/ PMID:29220441

Gene expression profiles in liver of mouse after chronic exposure to drinking water.

PubMed

Wu, Bing; Zhang, Yan; Zhao, Dayong; Zhang, Xuxiang; Kong, Zhiming; Cheng, Shupei

2009-10-01

cDNA micorarray approach was applied to hepatic transcriptional profile analysis in male mouse (Mus musculus, ICR) to assess the potential health effects of drinking water in Nanjing, China. Mice were treated with continuous exposure to drinking water for 90 days. Hepatic gene expression was analyzed with Affymetrix Mouse Genome 430A 2.0 arrays, and pathway analysis was carried out by Molecule Annotation System 2.0 and KEGG pathway database. A total of 836 genes were found to be significantly altered (1.5-fold, P < or = 0.05), including 294 up-regulated genes and 542 down-regulated genes. According to biological pathway analysis, drinking water exposure resulted in aberration of gene expression and biological pathways linked to xenobiotic metabolism, signal transduction, cell cycle and oxidative stress response. Further, deregulation of several genes associated with carcinogenesis or tumor progression including Ccnd1, Egfr, Map2k3, Mcm2, Orc2l and Smad2 was observed. Although transcription changes in identified genes are unlikely to be used as a sole indicator of adverse health effects, the results of this study could enhance our understanding of early toxic effects of drinking water exposure and support future studies on drinking water safety.

Characterization of mango (Mangifera indica L.) transcriptome and chloroplast genome.

PubMed

Azim, M Kamran; Khan, Ishtaiq A; Zhang, Yong

2014-05-01

We characterized mango leaf transcriptome and chloroplast genome using next generation DNA sequencing. The RNA-seq output of mango transcriptome generated >12 million reads (total nucleotides sequenced >1 Gb). De novo transcriptome assembly generated 30,509 unigenes with lengths in the range of 300 to ≥3,000 nt and 67× depth of coverage. Blast searching against nonredundant nucleotide databases and several Viridiplantae genomic datasets annotated 24,593 mango unigenes (80% of total) and identified Citrus sinensis as closest neighbor of mango with 9,141 (37%) matched sequences. The annotation with gene ontology and Clusters of Orthologous Group terms categorized unigene sequences into 57 and 25 classes, respectively. More than 13,500 unigenes were assigned to 293 KEGG pathways. Besides major plant biology related pathways, KEGG based gene annotation pointed out active presence of an array of biochemical pathways involved in (a) biosynthesis of bioactive flavonoids, flavones and flavonols, (b) biosynthesis of terpenoids and lignins and (c) plant hormone signal transduction. The mango transcriptome sequences revealed 235 proteases belonging to five catalytic classes of proteolytic enzymes. The draft genome of mango chloroplast (cp) was obtained by a combination of Sanger and next generation sequencing. The draft mango cp genome size is 151,173 bp with a pair of inverted repeats of 27,093 bp separated by small and large single copy regions, respectively. Out of 139 genes in mango cp genome, 91 found to be protein coding. Sequence analysis revealed cp genome of C. sinensis as closest neighbor of mango. We found 51 short repeats in mango cp genome supposed to be associated with extensive rearrangements. This is the first report of transcriptome and chloroplast genome analysis of any Anacardiaceae family member.

Bioinformatics resource manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools

PubMed Central

2012-01-01

Background MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. Results BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs. Conclusions BRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java™ and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm). PMID:23174015

Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's anticancer screen.

PubMed

Huang, Ruili; Wallqvist, Anders; Covell, David G

2006-03-01

We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.

Recent advances in modeling languages for pathway maps and computable biological networks.

PubMed

Slater, Ted

2014-02-01

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Discovering and annotating fish early life-stage (FELS) adverse outcome pathways: Putting the research strategy into practice

EPA Science Inventory

In May 2012, a HESI-sponsored expert workshop yielded a proposed research strategy for systematically discovering, characterizing, and annotating fish early life-stage (FELS) adverse outcome pathways (AOPs) as well as prioritizing AOP development in light of current restrictions ...

Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality.

PubMed

Jiang, Yue; Xiong, Xuejian; Danska, Jayne; Parkinson, John

2016-01-12

Metatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function. Here, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76% of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions. The application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of reference genomes can impact comprehensive annotation of metatranscriptomes. Consequently, beyond the application of standardized pipelines, additional caution must be taken when interpreting their output and performing downstream, microbiome-specific, analyses. The pipeline used in these analyses along with a tutorial has been made freely available for download from our project website: http://www.compsysbio.org/microbiome .

Genome Annotation in a Community College Cell Biology Lab

ERIC Educational Resources Information Center

Beagley, C. Timothy

2013-01-01

The Biology Department at Salt Lake Community College has used the IMG-ACT toolbox to introduce a genome mapping and annotation exercise into the laboratory portion of its Cell Biology course. This project provides students with an authentic inquiry-based learning experience while introducing them to computational biology and contemporary learning…

The De Novo Transcriptome and Its Functional Annotation in the Seed Beetle Callosobruchus maculatus.

PubMed

Sayadi, Ahmed; Immonen, Elina; Bayram, Helen; Arnqvist, Göran

2016-01-01

Despite their unparalleled biodiversity, the genomic resources available for beetles (Coleoptera) remain relatively scarce. We present an integrative and high quality annotated transcriptome of the beetle Callosobruchus maculatus, an important and cosmopolitan agricultural pest as well as an emerging model species in ecology and evolutionary biology. Using Illumina sequencing technology, we sequenced 492 million read pairs generated from 51 samples of different developmental stages (larvae, pupae and adults) of C. maculatus. Reads were de novo assembled using the Trinity software, into a single combined assembly as well as into three separate assemblies based on data from the different developmental stages. The combined assembly generated 218,192 transcripts and 145,883 putative genes. Putative genes were annotated with the Blast2GO software and the Trinotate pipeline. In total, 33,216 putative genes were successfully annotated using Blastx against the Nr (non-redundant) database and 13,382 were assigned to 34,100 Gene Ontology (GO) terms. We classified 5,475 putative genes into Clusters of Orthologous Groups (COG) and 116 metabolic pathways maps were predicted based on the annotation. Our analyses suggested that the transcriptional specificity increases with ontogeny. For example, out of 33,216 annotated putative genes, 51 were only expressed in larvae, 63 only in pupae and 171 only in adults. Our study illustrates the importance of including samples from several developmental stages when the aim is to provide an integrative and high quality annotated transcriptome. Our results will represent an invaluable resource for those working with the ecology, evolution and pest control of C. maculatus, as well for comparative studies of the transcriptomics and genomics of beetles more generally.

The De Novo Transcriptome and Its Functional Annotation in the Seed Beetle Callosobruchus maculatus

PubMed Central

Sayadi, Ahmed; Immonen, Elina; Bayram, Helen

2016-01-01

Despite their unparalleled biodiversity, the genomic resources available for beetles (Coleoptera) remain relatively scarce. We present an integrative and high quality annotated transcriptome of the beetle Callosobruchus maculatus, an important and cosmopolitan agricultural pest as well as an emerging model species in ecology and evolutionary biology. Using Illumina sequencing technology, we sequenced 492 million read pairs generated from 51 samples of different developmental stages (larvae, pupae and adults) of C. maculatus. Reads were de novo assembled using the Trinity software, into a single combined assembly as well as into three separate assemblies based on data from the different developmental stages. The combined assembly generated 218,192 transcripts and 145,883 putative genes. Putative genes were annotated with the Blast2GO software and the Trinotate pipeline. In total, 33,216 putative genes were successfully annotated using Blastx against the Nr (non-redundant) database and 13,382 were assigned to 34,100 Gene Ontology (GO) terms. We classified 5,475 putative genes into Clusters of Orthologous Groups (COG) and 116 metabolic pathways maps were predicted based on the annotation. Our analyses suggested that the transcriptional specificity increases with ontogeny. For example, out of 33,216 annotated putative genes, 51 were only expressed in larvae, 63 only in pupae and 171 only in adults. Our study illustrates the importance of including samples from several developmental stages when the aim is to provide an integrative and high quality annotated transcriptome. Our results will represent an invaluable resource for those working with the ecology, evolution and pest control of C. maculatus, as well for comparative studies of the transcriptomics and genomics of beetles more generally. PMID:27442123

RADIOISOTOPE TECHNIQUES FOR INSTRUCTION IN THE BIOLOGICAL SCIENCES, A LIST OF ANNOTATED REFERENCES.

ERIC Educational Resources Information Center

HURLBURT, EVELYN M.

REFERENCES TO BIOLOGICAL EXPERIMENTS THAT EMPHASIZE THE USE OF RADIOISOTOPES AS TRACERS ARE INCLUDED IN THIS ANNOTATED BIBLIOGRAPHY. MATERIALS INCLUDED ARE CONSIDERED TO BE READILY AVAILABLE AND WERE PUBLISHED AFTER 1960. SECTION I IS COMPOSED OF SELECTED SOURCES. ENTRIES INCLUDE (1) COMPLETE CITATIONS, (2) A BRIEF ANNOTATION, AND (3) LISTS OF…

Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase.

PubMed

Patil, Sonali; Pincas, Hanna; Seto, Jeremy; Nudelman, German; Nudelman, Irina; Sealfon, Stuart C

2010-10-07

Dendritic cells are antigen-presenting cells that play an essential role in linking the innate and adaptive immune systems. Much research has focused on the signaling pathways triggered upon infection of dendritic cells by various pathogens. The high level of activity in the field makes it desirable to have a pathway-based resource to access the information in the literature. Current pathway diagrams lack either comprehensiveness, or an open-access editorial interface. Hence, there is a need for a dependable, expertly curated knowledgebase that integrates this information into a map of signaling networks. We have built a detailed diagram of the dendritic cell signaling network, with the goal of providing researchers with a valuable resource and a facile method for community input. Network construction has relied on comprehensive review of the literature and regular updates. The diagram includes detailed depictions of pathways activated downstream of different pathogen recognition receptors such as Toll-like receptors, retinoic acid-inducible gene-I-like receptors, C-type lectin receptors and nucleotide-binding oligomerization domain-like receptors. Initially assembled using CellDesigner software, it provides an annotated graphical representation of interactions stored in Systems Biology Mark-up Language. The network, which comprises 249 nodes and 213 edges, has been web-published through the Biological Pathway Publisher software suite. Nodes are annotated with PubMed references and gene-related information, and linked to a public wiki, providing a discussion forum for updates and corrections. To gain more insight into regulatory patterns of dendritic cell signaling, we analyzed the network using graph-theory methods: bifan, feedforward and multi-input convergence motifs were enriched. This emphasis on activating control mechanisms is consonant with a network that subserves persistent and coordinated responses to pathogen detection. This map represents a navigable aid for presenting a consensus view of the current knowledge on dendritic cell signaling that can be continuously improved through contributions of research community experts. Because the map is available in a machine readable format, it can be edited and may assist researchers in data analysis. Furthermore, the availability of a comprehensive knowledgebase might help further research in this area such as vaccine development. The dendritic cell signaling knowledgebase is accessible at http://tsb.mssm.edu/pathwayPublisher/DC_pathway/DC_pathway_index.html.

Assessment of protein set coherence using functional annotations

PubMed Central

Chagoyen, Monica; Carazo, Jose M; Pascual-Montano, Alberto

2008-01-01

Background Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information (e.g. significantly enriched annotations, pairwise functional similarities), they do not specifically measure the degree of homogeneity of a protein set. Results In this work we present a method that scores the degree of functional homogeneity, or coherence, of a set of proteins on the basis of the global similarity of their functional annotations. The method uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set. As such, it can be used as a first step in the validation of sets expected to be homogeneous prior to further functional interpretation. Conclusion We evaluate our method by analysing known biologically relevant sets as well as random ones. The known relevant sets comprise macromolecular complexes, cellular components and pathways described for Saccharomyces cerevisiae, which are mostly significantly coherent. Finally, we illustrate the usefulness of our approach for validating 'functional modules' obtained from computational analysis of protein-protein interaction networks. Matlab code and supplementary data are available at PMID:18937846

Transcriptator: An Automated Computational Pipeline to Annotate Assembled Reads and Identify Non Coding RNA.

PubMed

Tripathi, Kumar Parijat; Evangelista, Daniela; Zuccaro, Antonio; Guarracino, Mario Rosario

2015-01-01

RNA-seq is a new tool to measure RNA transcript counts, using high-throughput sequencing at an extraordinary accuracy. It provides quantitative means to explore the transcriptome of an organism of interest. However, interpreting this extremely large data into biological knowledge is a problem, and biologist-friendly tools are lacking. In our lab, we developed Transcriptator, a web application based on a computational Python pipeline with a user-friendly Java interface. This pipeline uses the web services available for BLAST (Basis Local Search Alignment Tool), QuickGO and DAVID (Database for Annotation, Visualization and Integrated Discovery) tools. It offers a report on statistical analysis of functional and Gene Ontology (GO) annotation's enrichment. It helps users to identify enriched biological themes, particularly GO terms, pathways, domains, gene/proteins features and protein-protein interactions related informations. It clusters the transcripts based on functional annotations and generates a tabular report for functional and gene ontology annotations for each submitted transcript to the web server. The implementation of QuickGo web-services in our pipeline enable the users to carry out GO-Slim analysis, whereas the integration of PORTRAIT (Prediction of transcriptomic non coding RNA (ncRNA) by ab initio methods) helps to identify the non coding RNAs and their regulatory role in transcriptome. In summary, Transcriptator is a useful software for both NGS and array data. It helps the users to characterize the de-novo assembled reads, obtained from NGS experiments for non-referenced organisms, while it also performs the functional enrichment analysis of differentially expressed transcripts/genes for both RNA-seq and micro-array experiments. It generates easy to read tables and interactive charts for better understanding of the data. The pipeline is modular in nature, and provides an opportunity to add new plugins in the future. Web application is freely available at: http://www-labgtp.na.icar.cnr.it/Transcriptator.

MicroScope: a platform for microbial genome annotation and comparative genomics

PubMed Central

Vallenet, D.; Engelen, S.; Mornico, D.; Cruveiller, S.; Fleury, L.; Lajus, A.; Rouy, Z.; Roche, D.; Salvignol, G.; Scarpelli, C.; Médigue, C.

2009-01-01

The initial outcome of genome sequencing is the creation of long text strings written in a four letter alphabet. The role of in silico sequence analysis is to assist biologists in the act of associating biological knowledge with these sequences, allowing investigators to make inferences and predictions that can be tested experimentally. A wide variety of software is available to the scientific community, and can be used to identify genomic objects, before predicting their biological functions. However, only a limited number of biologically interesting features can be revealed from an isolated sequence. Comparative genomics tools, on the other hand, by bringing together the information contained in numerous genomes simultaneously, allow annotators to make inferences based on the idea that evolution and natural selection are central to the definition of all biological processes. We have developed the MicroScope platform in order to offer a web-based framework for the systematic and efficient revision of microbial genome annotation and comparative analysis (http://www.genoscope.cns.fr/agc/microscope). Starting with the description of the flow chart of the annotation processes implemented in the MicroScope pipeline, and the development of traditional and novel microbial annotation and comparative analysis tools, this article emphasizes the essential role of expert annotation as a complement of automatic annotation. Several examples illustrate the use of implemented tools for the review and curation of annotations of both new and publicly available microbial genomes within MicroScope’s rich integrated genome framework. The platform is used as a viewer in order to browse updated annotation information of available microbial genomes (more than 440 organisms to date), and in the context of new annotation projects (117 bacterial genomes). The human expertise gathered in the MicroScope database (about 280,000 independent annotations) contributes to improve the quality of microbial genome annotation, especially for genomes initially analyzed by automatic procedures alone. Database URLs: http://www.genoscope.cns.fr/agc/mage and http://www.genoscope.cns.fr/agc/microcyc PMID:20157493

MicroScope: a platform for microbial genome annotation and comparative genomics.

PubMed

Vallenet, D; Engelen, S; Mornico, D; Cruveiller, S; Fleury, L; Lajus, A; Rouy, Z; Roche, D; Salvignol, G; Scarpelli, C; Médigue, C

2009-01-01

The initial outcome of genome sequencing is the creation of long text strings written in a four letter alphabet. The role of in silico sequence analysis is to assist biologists in the act of associating biological knowledge with these sequences, allowing investigators to make inferences and predictions that can be tested experimentally. A wide variety of software is available to the scientific community, and can be used to identify genomic objects, before predicting their biological functions. However, only a limited number of biologically interesting features can be revealed from an isolated sequence. Comparative genomics tools, on the other hand, by bringing together the information contained in numerous genomes simultaneously, allow annotators to make inferences based on the idea that evolution and natural selection are central to the definition of all biological processes. We have developed the MicroScope platform in order to offer a web-based framework for the systematic and efficient revision of microbial genome annotation and comparative analysis (http://www.genoscope.cns.fr/agc/microscope). Starting with the description of the flow chart of the annotation processes implemented in the MicroScope pipeline, and the development of traditional and novel microbial annotation and comparative analysis tools, this article emphasizes the essential role of expert annotation as a complement of automatic annotation. Several examples illustrate the use of implemented tools for the review and curation of annotations of both new and publicly available microbial genomes within MicroScope's rich integrated genome framework. The platform is used as a viewer in order to browse updated annotation information of available microbial genomes (more than 440 organisms to date), and in the context of new annotation projects (117 bacterial genomes). The human expertise gathered in the MicroScope database (about 280,000 independent annotations) contributes to improve the quality of microbial genome annotation, especially for genomes initially analyzed by automatic procedures alone.Database URLs: http://www.genoscope.cns.fr/agc/mage and http://www.genoscope.cns.fr/agc/microcyc.

Transcriptome profiling identified differentially expressed genes and pathways associated with tamoxifen resistance in human breast cancer

PubMed Central

Men, Xin; Ma, Jun; Wu, Tong; Pu, Junyi; Wen, Shaojia; Shen, Jianfeng; Wang, Xun; Wang, Yamin; Chen, Chao; Dai, Penggao

2018-01-01

Tamoxifen (TAM) resistance is an important clinical problem in the treatment of breast cancer. In order to identify the mechanism of TAM resistance for estrogen receptor (ER)-positive breast cancer, we screened the transcriptome using RNA-seq and compared the gene expression profiles between the MCF-7 mamma carcinoma cell line and the TAM-resistant cell line TAMR/MCF-7, 52 significant differential expression genes (DEGs) were identified including SLIT2, ROBO, LHX, KLF, VEGFC, BAMBI, LAMA1, FLT4, PNMT, DHRS2, MAOA and ALDH. The DEGs were annotated in the GO, COG and KEGG databases. Annotation of the function of the DEGs in the KEGG database revealed the top three pathways enriched with the most DEGs, including pathways in cancer, the PI3K-AKT pathway, and focal adhesion. Then we compared the gene expression profiles between the Clinical progressive disease (PD) and the complete response (CR) from the cancer genome altas (TCGA). 10 common DEGs were identified through combining the clinical and cellular analysis results. Protein-protein interaction network was applied to analyze the association of ER signal pathway with the 10 DEGs. 3 significant genes (GFRA3, NPY1R and PTPRN2) were closely related to ER related pathway. These significant DEGs regulated many biological activities such as cell proliferation and survival, motility and migration, and tumor cell invasion. The interactions between these DEGs and drug resistance phenomenon need to be further elucidated at a functional level in further studies. Based on our findings, we believed that these DEGs could be therapeutic targets, which can be explored to develop new treatment options. PMID:29423105

Characterization and genome functional analysis of a novel metamitron-degrading strain Rhodococcus sp. MET via both triazinone and phenyl rings cleavage

NASA Astrophysics Data System (ADS)

Fang, Hua; Xu, Tianheng; Cao, Duantao; Cheng, Longyin; Yu, Yunlong

2016-08-01

A novel bacterium capable of utilizing metamitron as the sole source of carbon and energy was isolated from contaminated soil and identified as Rhodococcus sp. MET based on its morphological characteristics, BIOLOG GP2 microplate profile, and 16S rDNA phylogeny. Genome sequencing and functional annotation of the isolate MET showed a 6,340,880 bp genome with a 62.47% GC content and 5,987 protein-coding genes. In total, 5,907 genes were annotated with the COG, GO, KEGG, Pfam, Swiss-Prot, TrEMBL, and nr databases. The degradation rate of metamitron by the isolate MET obviously increased with increasing substrate concentrations from 1 to 10 mg/l and subsequently decreased at 100 mg/l. The optimal pH and temperature for metamitron biodegradation were 7.0 and 20-30 °C, respectively. Based on genome annotation of the metamitron degradation genes and the metabolites detected by HPLC-MS/MS, the following metamitron biodegradation pathways were proposed: 1) Metamitron was transformed into 2-(3-hydrazinyl-2-ethyl)-hydrazono-2-phenylacetic acid by triazinone ring cleavage and further mineralization; 2) Metamitron was converted into 3-methyl-4-amino-6(2-hydroxy-muconic acid)-1,2,4-triazine-5(4H)-one by phenyl ring cleavage and further mineralization. The coexistence of diverse mineralization pathways indicates that our isolate may effectively bioremediate triazinone herbicide-contaminated soils.

Computational Functional Analysis of Lipid Metabolic Enzymes.

PubMed

Bagnato, Carolina; Have, Arjen Ten; Prados, María B; Beligni, María V

2017-01-01

The computational analysis of enzymes that participate in lipid metabolism has both common and unique challenges when compared to the whole protein universe. Some of the hurdles that interfere with the functional annotation of lipid metabolic enzymes that are common to other pathways include the definition of proper starting datasets, the construction of reliable multiple sequence alignments, the definition of appropriate evolutionary models, and the reconstruction of phylogenetic trees with high statistical support, particularly for large datasets. Most enzymes that take part in lipid metabolism belong to complex superfamilies with many members that are not involved in lipid metabolism. In addition, some enzymes that do not have sequence similarity catalyze similar or even identical reactions. Some of the challenges that, albeit not unique, are more specific to lipid metabolism refer to the high compartmentalization of the routes, the catalysis in hydrophobic environments and, related to this, the function near or in biological membranes.In this work, we provide guidelines intended to assist in the proper functional annotation of lipid metabolic enzymes, based on previous experiences related to the phospholipase D superfamily and the annotation of the triglyceride synthesis pathway in algae. We describe a pipeline that starts with the definition of an initial set of sequences to be used in similarity-based searches and ends in the reconstruction of phylogenies. We also mention the main issues that have to be taken into consideration when using tools to analyze subcellular localization, hydrophobicity patterns, or presence of transmembrane domains in lipid metabolic enzymes.

Analysis of expressed sequence tags (ESTs) from cocoa (Theobroma cacao L) upon infection with Phytophthora megakarya.

PubMed

Naganeeswaran, Sudalaimuthu Asari; Subbian, Elain Apshara; Ramaswamy, Manimekalai

2012-01-01

Phytophthora megakarya, the causative agent of cacao black pod disease in West African countries causes an extensive loss of yield. In this study we have analyzed 4 libraries of ESTs derived from Phytophthora megakarya infected cocoa leaf and pod tissues. Totally 6379 redundant sequences were retrieved from ESTtik database and EST processing was performed using seqclean tool. Clustering and assembling using CAP3 generated 3333 non-redundant (907 contigs and 2426 singletons) sequences. The primary sequence analysis of 3333 non-redundant sequences showed that the GC percentage was 42.7 and the sequence length ranged from 101 - 2576 nucleotides. Further, functional analysis (Blast, Interproscan, Gene ontology and KEGG search) were executed and 1230 orthologous genes were annotated. Totally 272 enzymes corresponding to 114 metabolic pathways were identified. Functional annotation revealed that most of the sequences are related to molecular function, stress response and biological processes. The annotated enzymes are aldehyde dehydrogenase (E.C: 1.2.1.3), catalase (E.C: 1.11.1.6), acetyl-CoA C-acetyltransferase (E.C: 2.3.1.9), threonine ammonia-lyase (E.C: 4.3.1.19), acetolactate synthase (E.C: 2.2.1.6), O-methyltransferase (E.C: 2.1.1.68) which play an important role in amino acid biosynthesis and phenyl propanoid biosynthesis. All this information was stored in MySQL database management system to be used in future for reconstruction of biotic stress response pathway in cocoa.

Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets.

PubMed

Salem, Saeed; Ozcaglar, Cagri

2014-01-01

Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression. We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

Whipworm kinomes reflect a unique biology and adaptation to the host animal.

PubMed

Stroehlein, Andreas J; Young, Neil D; Korhonen, Pasi K; Chang, Bill C H; Nejsum, Peter; Pozio, Edoardo; La Rosa, Giuseppe; Sternberg, Paul W; Gasser, Robin B

2017-11-01

Roundworms belong to a diverse phylum (Nematoda) which is comprised of many parasitic species including whipworms (genus Trichuris). These worms have adapted to a biological niche within the host and exhibit unique morphological characteristics compared with other nematodes. Although these adaptations are known, the underlying molecular mechanisms remain elusive. The availability of genomes and transcriptomes of some whipworms now enables detailed studies of their molecular biology. Here, we defined and curated the full complement of an important class of enzymes, the protein kinases (kinomes) of two species of Trichuris, using an advanced and integrated bioinformatic pipeline. We investigated the transcription of Trichuris suis kinase genes across developmental stages, sexes and tissues, and reveal that selectively transcribed genes can be linked to central roles in developmental and reproductive processes. We also classified and functionally annotated the curated kinomes by integrating evidence from structural modelling and pathway analyses, and compared them with other curated kinomes of phylogenetically diverse nematode species. Our findings suggest unique adaptations in signalling processes governing worm morphology and biology, and provide an important resource that should facilitate experimental investigations of kinases and the biology of signalling pathways in nematodes. Copyright © 2017 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.

An Approach to Function Annotation for Proteins of Unknown Function (PUFs) in the Transcriptome of Indian Mulberry.

PubMed

Dhanyalakshmi, K H; Naika, Mahantesha B N; Sajeevan, R S; Mathew, Oommen K; Shafi, K Mohamed; Sowdhamini, Ramanathan; N Nataraja, Karaba

2016-01-01

The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs). Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS), which also provides a web service API (Application Programming Interface) for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

PubMed

Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

2016-09-09

Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

Corpus annotation for mining biomedical events from literature

PubMed Central

Kim, Jin-Dong; Ohta, Tomoko; Tsujii, Jun'ichi

2008-01-01

Background Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. Results We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation. Conclusion The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain. PMID:18182099

New insights about host response to smallpox using microarray data.

PubMed

Esteves, Gustavo H; Simoes, Ana C Q; Souza, Estevao; Dias, Rodrigo A; Ospina, Raydonal; Venancio, Thiago M

2007-08-24

Smallpox is a lethal disease that was endemic in many parts of the world until eradicated by massive immunization. Due to its lethality, there are serious concerns about its use as a bioweapon. Here we analyze publicly available microarray data to further understand survival of smallpox infected macaques, using systems biology approaches. Our goal is to improve the knowledge about the progression of this disease. We used KEGG pathways annotations to define groups of genes (or modules), and subsequently compared them to macaque survival times. This technique provided additional insights about the host response to this disease, such as increased expression of the cytokines and ECM receptors in the individuals with higher survival times. These results could indicate that these gene groups could influence an effective response from the host to smallpox. Macaques with higher survival times clearly express some specific pathways previously unidentified using regular gene-by-gene approaches. Our work also shows how third party analysis of public datasets can be important to support new hypotheses to relevant biological problems.

Genome and proteome annotation: organization, interpretation and integration

PubMed Central

Reeves, Gabrielle A.; Talavera, David; Thornton, Janet M.

2008-01-01

Recent years have seen a huge increase in the generation of genomic and proteomic data. This has been due to improvements in current biological methodologies, the development of new experimental techniques and the use of computers as support tools. All these raw data are useless if they cannot be properly analysed, annotated, stored and displayed. Consequently, a vast number of resources have been created to present the data to the wider community. Annotation tools and databases provide the means to disseminate these data and to comprehend their biological importance. This review examines the various aspects of annotation: type, methodology and availability. Moreover, it puts a special interest on novel annotation fields, such as that of phenotypes, and highlights the recent efforts focused on the integrating annotations. PMID:19019817

SNAD: Sequence Name Annotation-based Designer.

PubMed

Sidorov, Igor A; Reshetov, Denis A; Gorbalenya, Alexander E

2009-08-14

A growing diversity of biological data is tagged with unique identifiers (UIDs) associated with polynucleotides and proteins to ensure efficient computer-mediated data storage, maintenance, and processing. These identifiers, which are not informative for most people, are often substituted by biologically meaningful names in various presentations to facilitate utilization and dissemination of sequence-based knowledge. This substitution is commonly done manually that may be a tedious exercise prone to mistakes and omissions. Here we introduce SNAD (Sequence Name Annotation-based Designer) that mediates automatic conversion of sequence UIDs (associated with multiple alignment or phylogenetic tree, or supplied as plain text list) into biologically meaningful names and acronyms. This conversion is directed by precompiled or user-defined templates that exploit wealth of annotation available in cognate entries of external databases. Using examples, we demonstrate how this tool can be used to generate names for practical purposes, particularly in virology. A tool for controllable annotation-based conversion of sequence UIDs into biologically meaningful names and acronyms has been developed and placed into service, fostering links between quality of sequence annotation, and efficiency of communication and knowledge dissemination among researchers.

Social networks to biological networks: systems biology of Mycobacterium tuberculosis.

PubMed

Vashisht, Rohit; Bhardwaj, Anshu; Osdd Consortium; Brahmachari, Samir K

2013-07-01

Contextualizing relevant information to construct a network that represents a given biological process presents a fundamental challenge in the network science of biology. The quality of network for the organism of interest is critically dependent on the extent of functional annotation of its genome. Mostly the automated annotation pipelines do not account for unstructured information present in volumes of literature and hence large fraction of genome remains poorly annotated. However, if used, this information could substantially enhance the functional annotation of a genome, aiding the development of a more comprehensive network. Mining unstructured information buried in volumes of literature often requires manual intervention to a great extent and thus becomes a bottleneck for most of the automated pipelines. In this review, we discuss the potential of scientific social networking as a solution for systematic manual mining of data. Focusing on Mycobacterium tuberculosis, as a case study, we discuss our open innovative approach for the functional annotation of its genome. Furthermore, we highlight the strength of such collated structured data in the context of drug target prediction based on systems level analysis of pathogen.

Global Metabolic Reconstruction and Metabolic Gene Evolution in the Cattle Genome

PubMed Central

Kim, Woonsu; Park, Hyesun; Seo, Seongwon

2016-01-01

The sequence of cattle genome provided a valuable opportunity to systematically link genetic and metabolic traits of cattle. The objectives of this study were 1) to reconstruct genome-scale cattle-specific metabolic pathways based on the most recent and updated cattle genome build and 2) to identify duplicated metabolic genes in the cattle genome for better understanding of metabolic adaptations in cattle. A bioinformatic pipeline of an organism for amalgamating genomic annotations from multiple sources was updated. Using this, an amalgamated cattle genome database based on UMD_3.1, was created. The amalgamated cattle genome database is composed of a total of 33,292 genes: 19,123 consensus genes between NCBI and Ensembl databases, 8,410 and 5,493 genes only found in NCBI or Ensembl, respectively, and 266 genes from NCBI scaffolds. A metabolic reconstruction of the cattle genome and cattle pathway genome database (PGDB) was also developed using Pathway Tools, followed by an intensive manual curation. The manual curation filled or revised 68 pathway holes, deleted 36 metabolic pathways, and added 23 metabolic pathways. Consequently, the curated cattle PGDB contains 304 metabolic pathways, 2,460 reactions including 2,371 enzymatic reactions, and 4,012 enzymes. Furthermore, this study identified eight duplicated genes in 12 metabolic pathways in the cattle genome compared to human and mouse. Some of these duplicated genes are related with specific hormone biosynthesis and detoxifications. The updated genome-scale metabolic reconstruction is a useful tool for understanding biology and metabolic characteristics in cattle. There has been significant improvements in the quality of cattle genome annotations and the MetaCyc database. The duplicated metabolic genes in the cattle genome compared to human and mouse implies evolutionary changes in the cattle genome and provides a useful information for further research on understanding metabolic adaptations of cattle. PMID:26992093

Interleukins and their signaling pathways in the Reactome biological pathway database.

PubMed

Jupe, Steve; Ray, Keith; Roca, Corina Duenas; Varusai, Thawfeek; Shamovsky, Veronica; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-04-01

There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer.

PubMed

Gao, Xue-Xin; Gao, Lei; Wang, Jiu-Qiang; Qu, Su-Su; Qu, Yue; Sun, Hong-Lei; Liu, Si-Dang; Shang, Ying-Li

2016-07-12

Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ansong, Charles; Tolic, Nikola; Purvine, Samuel O.

Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. For example systems biology-oriented genome scale modeling efforts greatly benefit from accurate annotation of protein-coding genes to develop proper functioning models. However, determining protein-coding genes for most new genomes is almost completely performed by inference, using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. With the ability to directly measure peptides arising from expressed proteins, mass spectrometry-based proteomics approaches can be used to augment and verify codingmore » regions of a genomic sequence and importantly detect post-translational processing events. In this study we utilized “shotgun” proteomics to guide accurate primary genome annotation of the bacterial pathogen Salmonella Typhimurium 14028 to facilitate a systems-level understanding of Salmonella biology. The data provides protein-level experimental confirmation for 44% of predicted protein-coding genes, suggests revisions to 48 genes assigned incorrect translational start sites, and uncovers 13 non-annotated genes missed by gene prediction programs. We also present a comprehensive analysis of post-translational processing events in Salmonella, revealing a wide range of complex chemical modifications (70 distinct modifications) and confirming more than 130 signal peptide and N-terminal methionine cleavage events in Salmonella. This study highlights several ways in which proteomics data applied during the primary stages of annotation can improve the quality of genome annotations, especially with regards to the annotation of mature protein products.« less

Caveat emptor: limitations of the automated reconstruction of metabolic pathways in Plasmodium.

PubMed

Ginsburg, Hagai

2009-01-01

The functional reconstruction of metabolic pathways from an annotated genome is a tedious and demanding enterprise. Automation of this endeavor using bioinformatics algorithms could cope with the ever-increasing number of sequenced genomes and accelerate the process. Here, the manual reconstruction of metabolic pathways in the functional genomic database of Plasmodium falciparum--Malaria Parasite Metabolic Pathways--is described and compared with pathways generated automatically as they appear in PlasmoCyc, metaSHARK and the Kyoto Encyclopedia for Genes and Genomes. A critical evaluation of this comparison discloses that the automatic reconstruction of pathways generates manifold paths that need an expert manual verification to accept some and reject most others based on manually curated gene annotation.

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.

PubMed

Karchin, Rachel; Diekhans, Mark; Kelly, Libusha; Thomas, Daryl J; Pieper, Ursula; Eswar, Narayanan; Haussler, David; Sali, Andrej

2005-06-15

The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org http://salilab.org/LS-SNP/supp-info.pdf.

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.

PubMed

Misirli, Goksel; Cavaliere, Matteo; Waites, William; Pocock, Matthew; Madsen, Curtis; Gilfellon, Owen; Honorato-Zimmer, Ricardo; Zuliani, Paolo; Danos, Vincent; Wipat, Anil

2016-03-15

Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk. © The Author 2015. Published by Oxford University Press.

Microarray profiling of human white adipose tissue after exogenous leptin injection.

PubMed

Taleb, S; Van Haaften, R; Henegar, C; Hukshorn, C; Cancello, R; Pelloux, V; Hanczar, B; Viguerie, N; Langin, D; Evelo, C; Zucker, J; Clément, K; Saris, W H M

2006-03-01

Leptin is a secreted adipocyte hormone that plays a key role in the regulation of body weight homeostasis. The leptin effect on human white adipose tissue (WAT) is still debated. The aim of this study was to assess whether the administration of polyethylene glycol-leptin (PEG-OB) in a single supraphysiological dose has transcriptional effects on genes of WAT and to identify its target genes and functional pathways in WAT. Blood samples and WAT biopsies were obtained from 10 healthy nonobese men before treatment and 72 h after the PEG-OB injection, leading to an approximate 809-fold increase in circulating leptin. The WAT gene expression profile before and after the PEG-OB injection was compared using pangenomic microarrays. Functional gene annotations based on the gene ontology of the PEG-OB regulated genes were performed using both an 'in house' automated procedure and GenMAPP (Gene Microarray Pathway Profiler), designed for viewing and analyzing gene expression data in the context of biological pathways. Statistical analysis of microarray data revealed that PEG-OB had a major down-regulated effect on WAT gene expression, as we obtained 1,822 and 100 down- and up-regulated genes, respectively. Microarray data were validated using reverse transcription quantitative PCR. Functional gene annotations of PEG-OB regulated genes revealed that the functional class related to immunity and inflammation was among the most mobilized PEG-OB pathway in WAT. These genes are mainly expressed in the cell of the stroma vascular fraction in comparison with adipocytes. Our observations support the hypothesis that leptin could act on WAT, particularly on genes related to inflammation and immunity, which may suggest a novel leptin target pathway in human WAT.

Characterization of gonadal transcriptomes from the turbot (Scophthalmus maximus).

PubMed

Hu, Yulong; Huang, Meng; Wang, Weiji; Guan, Jiantao; Kong, Jie

2016-01-01

The mechanisms underlying sexual reproduction and sex ratio determination remains unclear in turbot, a flatfish of great commercial value. And there is limited information in the turbot database regarding genes related to the reproductive system. Here, we conducted high-throughput transcriptome profiling of turbot gonad tissues to better understand their reproductive functions and to supply essential gene sequence information for marker-assisted selection programs in the turbot industry. In this study, two gonad libraries representing sex differences in Scophthalmus maximus yielded 453 818 high-quality reads that were assembled into 24 611 contigs and 33 713 singletons by using 454 pyrosequencing, 13 936 contigs and singletons (CS) of which were annotated using BLASTx. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses revealed that various biological functions and processes were associated with many of the annotated CS. Expression analyses showed that 510 genes were differentially expressed in males versus females; 80% of these genes were annotated. In addition, 6484 and 6036 single nucleotide polymorphisms (SNPs) were identified in male and female libraries, respectively. This transcriptome resource will serve as the foundation for cDNA or SNP microarray construction, gene expression characterization, and sex-specific linkage mapping in turbot.

An approach to describing and analysing bulk biological annotation quality: a case study using UniProtKB.

PubMed

Bell, Michael J; Gillespie, Colin S; Swan, Daniel; Lord, Phillip

2012-09-15

Annotations are a key feature of many biological databases, used to convey our knowledge of a sequence to the reader. Ideally, annotations are curated manually, however manual curation is costly, time consuming and requires expert knowledge and training. Given these issues and the exponential increase of data, many databases implement automated annotation pipelines in an attempt to avoid un-annotated entries. Both manual and automated annotations vary in quality between databases and annotators, making assessment of annotation reliability problematic for users. The community lacks a generic measure for determining annotation quality and correctness, which we look at addressing within this article. Specifically we investigate word reuse within bulk textual annotations and relate this to Zipf's Principle of Least Effort. We use the UniProt Knowledgebase (UniProtKB) as a case study to demonstrate this approach since it allows us to compare annotation change, both over time and between automated and manually curated annotations. By applying power-law distributions to word reuse in annotation, we show clear trends in UniProtKB over time, which are consistent with existing studies of quality on free text English. Further, we show a clear distinction between manual and automated analysis and investigate cohorts of protein records as they mature. These results suggest that this approach holds distinct promise as a mechanism for judging annotation quality. Source code is available at the authors website: http://homepages.cs.ncl.ac.uk/m.j.bell1/annotation. phillip.lord@newcastle.ac.uk.

Studies of Scientific Disciplines. An Annotated Bibliography.

ERIC Educational Resources Information Center

Weisz, Diane; Kruytbosch, Carlos

Provided in this bibliography are annotated lists of social studies of science literature, arranged alphabetically by author in 13 disciplinary areas. These areas include astronomy; general biology; biochemistry and molecular biology; biomedicine; chemistry; earth and space sciences; economics; engineering; mathematics; physics; political science;…

Told through the wine: A liquid chromatography-mass spectrometry interplatform comparison reveals the influence of the global approach on the final annotated metabolites in non-targeted metabolomics.

PubMed

Díaz, Ramon; Gallart-Ayala, Hector; Sancho, Juan V; Nuñez, Oscar; Zamora, Tatiana; Martins, Claudia P B; Hernández, Félix; Hernández-Cassou, Santiago; Saurina, Javier; Checa, Antonio

2016-02-12

This work focuses on the influence of the selected LC-HRMS platform on the final annotated compounds in non-targeted metabolomics. Two platforms that differed in columns, mobile phases, gradients, chromatographs, mass spectrometers (Orbitrap [Platform#1] and Q-TOF [Platform#2]), data processing and marker selection protocols were compared. A total of 42 wines samples from three different protected denomination of origin (PDO) were analyzed. At the feature level, good (O)PLS-DA models were obtained for both platforms (Q(2)[Platform#1]=0.89, 0.83 and 0.72; Q(2)[Platform#2]=0.86, 0.86 and 0.77 for Penedes, Ribera del Duero and Rioja wines respectively) with 100% correctly classified samples in all cases. At the annotated metabolite level, platforms proposed 9 and 8 annotated metabolites respectively which were identified by matching standards or the MS/MS spectra of the compounds. At this stage, there was no coincidence among platforms regarding the suggested metabolites. When screened on the raw data, 6 and 5 of these compounds were detected on the other platform with a similar trend. Some of the detected metabolites showed complimentary information when integrated on biological pathways. Through the use of some examples at the annotated metabolite level, possible explanations of this initial divergence on the results are presented. This work shows the complications that may arise on the comparison of non-targeted metabolomics platforms even when metabolite focused approaches are used in the identification. Copyright © 2016 Elsevier B.V. All rights reserved.

ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii.

PubMed

May, Patrick; Christian, Jan-Ole; Kempa, Stefan; Walther, Dirk

2009-05-04

The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern high-throughput technologies there is an imperative need to integrate large-scale data sets from high-throughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.

ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.

PubMed

Zhang, Kunlin; Chang, Suhua; Cui, Sijia; Guo, Liyuan; Zhang, Liuyan; Wang, Jing

2011-07-01

Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.

Compound annotation with real time cellular activity profiles to improve drug discovery.

PubMed

Fang, Ye

2016-01-01

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery. In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed. Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

A methodology to annotate systems biology markup language models with the synthetic biology open language.

PubMed

Roehner, Nicholas; Myers, Chris J

2014-02-21

Recently, we have begun to witness the potential of synthetic biology, noted here in the form of bacteria and yeast that have been genetically engineered to produce biofuels, manufacture drug precursors, and even invade tumor cells. The success of these projects, however, has often failed in translation and application to new projects, a problem exacerbated by a lack of engineering standards that combine descriptions of the structure and function of DNA. To address this need, this paper describes a methodology to connect the systems biology markup language (SBML) to the synthetic biology open language (SBOL), existing standards that describe biochemical models and DNA components, respectively. Our methodology involves first annotating SBML model elements such as species and reactions with SBOL DNA components. A graph is then constructed from the model, with vertices corresponding to elements within the model and edges corresponding to the cause-and-effect relationships between these elements. Lastly, the graph is traversed to assemble the annotating DNA components into a composite DNA component, which is used to annotate the model itself and can be referenced by other composite models and DNA components. In this way, our methodology can be used to build up a hierarchical library of models annotated with DNA components. Such a library is a useful input to any future genetic technology mapping algorithm that would automate the process of composing DNA components to satisfy a behavioral specification. Our methodology for SBML-to-SBOL annotation is implemented in the latest version of our genetic design automation (GDA) software tool, iBioSim.

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation.

PubMed

Bolleman, Jerven T; Mungall, Christopher J; Strozzi, Francesco; Baran, Joachim; Dumontier, Michel; Bonnal, Raoul J P; Buels, Robert; Hoehndorf, Robert; Fujisawa, Takatomo; Katayama, Toshiaki; Cock, Peter J A

2016-06-13

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned "omics" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

DOE PAGES

Bolleman, Jerven T.; Mungall, Christopher J.; Strozzi, Francesco; ...

2016-06-13

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. In this paper, we have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data formatmore » to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Finally, data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.« less

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolleman, Jerven T.; Mungall, Christopher J.; Strozzi, Francesco

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. In this paper, we have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data formatmore » to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Finally, data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.« less

Cis-Natural Antisense Transcripts Are Mainly Co-expressed with Their Sense Transcripts and Primarily Related to Energy Metabolic Pathways during Muscle Development.

PubMed

Zhao, Yunxia; Hou, Ye; Zhao, Changzhi; Liu, Fei; Luan, Yu; Jing, Lu; Li, Xinyun; Zhu, Mengjin; Zhao, Shuhong

2016-01-01

Cis-natural antisense transcripts (cis-NATs) are a new class of RNAs identified in various species. However, the biological functions of cis-NATs are largely unknown. In this study, we investigated the transcriptional characteristics and functions of cis-NATs in the muscle tissue of lean Landrace and indigenous fatty Lantang pigs. In total, 3,306 cis-NATs of 2,469 annotated genes were identified in the muscle tissue of pigs. More than 1,300 cis-NATs correlated with their sense genes at the transcriptional level, and approximately 80% of them were co-expressed in the two breeds. Furthermore, over 1,200 differentially expressed cis-NATs were identified during muscle development. Function annotation showed that the cis-NATs participated in muscle development mainly by co-expressing with genes involved in energy metabolic pathways, including citrate cycle (TCA cycle), glycolysis or gluconeogenesis, mitochondrial activation and so on. Moreover, these cis-NATs and their sense genes abruptly increased at the transition from the late fetal stages to the early postnatal stages and then decreased along with muscle development. In conclusion, the cis-NATs in the muscle tissue of pigs were identified and determined to be mainly co-expressed with their sense genes. The co-expressed cis-NATs and their sense gene were primarily related to energy metabolic pathways during muscle development in pigs. Our results offered novel evidence on the roles of cis-NATs during the muscle development of pigs.

Bovine Genome Database: supporting community annotation and analysis of the Bos taurus genome

PubMed Central

2010-01-01

Background A goal of the Bovine Genome Database (BGD; http://BovineGenome.org) has been to support the Bovine Genome Sequencing and Analysis Consortium (BGSAC) in the annotation and analysis of the bovine genome. We were faced with several challenges, including the need to maintain consistent quality despite diversity in annotation expertise in the research community, the need to maintain consistent data formats, and the need to minimize the potential duplication of annotation effort. With new sequencing technologies allowing many more eukaryotic genomes to be sequenced, the demand for collaborative annotation is likely to increase. Here we present our approach, challenges and solutions facilitating a large distributed annotation project. Results and Discussion BGD has provided annotation tools that supported 147 members of the BGSAC in contributing 3,871 gene models over a fifteen-week period, and these annotations have been integrated into the bovine Official Gene Set. Our approach has been to provide an annotation system, which includes a BLAST site, multiple genome browsers, an annotation portal, and the Apollo Annotation Editor configured to connect directly to our Chado database. In addition to implementing and integrating components of the annotation system, we have performed computational analyses to create gene evidence tracks and a consensus gene set, which can be viewed on individual gene pages at BGD. Conclusions We have provided annotation tools that alleviate challenges associated with distributed annotation. Our system provides a consistent set of data to all annotators and eliminates the need for annotators to format data. Involving the bovine research community in genome annotation has allowed us to leverage expertise in various areas of bovine biology to provide biological insight into the genome sequence. PMID:21092105

MetaPathways v2.5: quantitative functional, taxonomic and usability improvements.

PubMed

Konwar, Kishori M; Hanson, Niels W; Bhatia, Maya P; Kim, Dongjae; Wu, Shang-Ju; Hahn, Aria S; Morgan-Lang, Connor; Cheung, Hiu Kan; Hallam, Steven J

2015-10-15

Next-generation sequencing is producing vast amounts of sequence information from natural and engineered ecosystems. Although this data deluge has an enormous potential to transform our lives, knowledge creation and translation need software applications that scale with increasing data processing and analysis requirements. Here, we present improvements to MetaPathways, an annotation and analysis pipeline for environmental sequence information that expedites this transformation. We specifically address pathway prediction hazards through integration of a weighted taxonomic distance and enable quantitative comparison of assembled annotations through a normalized read-mapping measure. Additionally, we improve LAST homology searches through BLAST-equivalent E-values and output formats that are natively compatible with prevailing software applications. Finally, an updated graphical user interface allows for keyword annotation query and projection onto user-defined functional gene hierarchies, including the Carbohydrate-Active Enzyme database. MetaPathways v2.5 is available on GitHub: http://github.com/hallamlab/metapathways2. shallam@mail.ubc.ca Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Soybean Knowledge Base (SoyKB): a Web Resource for Soybean Translational Genomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joshi, Trupti; Patil, Kapil; Fitzpatrick, Michael R.

2012-01-17

Background: Soybean Knowledge Base (SoyKB) is a comprehensive all-inclusive web resource for soybean translational genomics. SoyKB is designed to handle the management and integration of soybean genomics, transcriptomics, proteomics and metabolomics data along with annotation of gene function and biological pathway. It contains information on four entities, namely genes, microRNAs, metabolites and single nucleotide polymorphisms (SNPs). Methods: SoyKB has many useful tools such as Affymetrix probe ID search, gene family search, multiple gene/ metabolite search supporting co-expression analysis, and protein 3D structure viewer as well as download and upload capacity for experimental data and annotations. It has four tiers ofmore » registration, which control different levels of access to public and private data. It allows users of certain levels to share their expertise by adding comments to the data. It has a user-friendly web interface together with genome browser and pathway viewer, which display data in an intuitive manner to the soybean researchers, producers and consumers. Conclusions: SoyKB addresses the increasing need of the soybean research community to have a one-stop-shop functional and translational omics web resource for information retrieval and analysis in a user-friendly way. SoyKB can be publicly accessed at http://soykb.org/.« less

Apollo: a sequence annotation editor

PubMed Central

Lewis, SE; Searle, SMJ; Harris, N; Gibson, M; Iyer, V; Richter, J; Wiel, C; Bayraktaroglu, L; Birney, E; Crosby, MA; Kaminker, JS; Matthews, BB; Prochnik, SE; Smith, CD; Tupy, JL; Rubin, GM; Misra, S; Mungall, CJ; Clamp, ME

2002-01-01

The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them. FlyBase biologists successfully used Apollo to annotate the Drosophila melanogaster genome and it is increasingly being used as a starting point for the development of customized annotation editing tools for other genome projects. PMID:12537571

Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets

PubMed Central

2014-01-01

Background Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression. Results We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways. PMID:25221624

RADIOISOTOPE EXPERIMENTS IN HIGH SCHOOL BIOLOGY, AN ANNOTATED SELECTED BIBLIOGRAPHY.

ERIC Educational Resources Information Center

HURLBURT, EVELYN M.

SELECTED REFERENCES ON THE USE OF RADIOISOTOPES IN BIOLOGY ARE CONTAINED IN THIS ANNOTATED BIBLIOGRAPHY FOR SECONDARY SCHOOL STUDENTS. MATERIALS INCLUDED WERE PUBLISHED AFTER 1960 AND DEAL WITH THE PROPERTIES OF RADIATION, SIMPLE RADIATION DETECTION PROCEDURES, AND TECHNIQUES FOR USING RADIOISOTOPES EXPERIMENTALLY. THE REFERENCES ARE LISTED IN…

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

PubMed Central

Seaver, Samuel M. D.; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M. T.; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D.; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D.; Henry, Christopher S.

2014-01-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today’s annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed. PMID:24927599

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource.

PubMed

Seaver, Samuel M D; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M T; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D; Henry, Christopher S

2014-07-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.

The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications

PubMed Central

2011-01-01

Background The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. Results Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. Conclusions Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework. PMID:21806842

The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications.

PubMed

Katayama, Toshiaki; Wilkinson, Mark D; Vos, Rutger; Kawashima, Takeshi; Kawashima, Shuichi; Nakao, Mitsuteru; Yamamoto, Yasunori; Chun, Hong-Woo; Yamaguchi, Atsuko; Kawano, Shin; Aerts, Jan; Aoki-Kinoshita, Kiyoko F; Arakawa, Kazuharu; Aranda, Bruno; Bonnal, Raoul Jp; Fernández, José M; Fujisawa, Takatomo; Gordon, Paul Mk; Goto, Naohisa; Haider, Syed; Harris, Todd; Hatakeyama, Takashi; Ho, Isaac; Itoh, Masumi; Kasprzyk, Arek; Kido, Nobuhiro; Kim, Young-Joo; Kinjo, Akira R; Konishi, Fumikazu; Kovarskaya, Yulia; von Kuster, Greg; Labarga, Alberto; Limviphuvadh, Vachiranee; McCarthy, Luke; Nakamura, Yasukazu; Nam, Yunsun; Nishida, Kozo; Nishimura, Kunihiro; Nishizawa, Tatsuya; Ogishima, Soichi; Oinn, Tom; Okamoto, Shinobu; Okuda, Shujiro; Ono, Keiichiro; Oshita, Kazuki; Park, Keun-Joon; Putnam, Nicholas; Senger, Martin; Severin, Jessica; Shigemoto, Yasumasa; Sugawara, Hideaki; Taylor, James; Trelles, Oswaldo; Yamasaki, Chisato; Yamashita, Riu; Satoh, Noriyuki; Takagi, Toshihisa

2011-08-02

The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework.

GeneTopics - interpretation of gene sets via literature-driven topic models

PubMed Central

2013-01-01

Background Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set. Methods Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research. Results We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner. Conclusions Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly tied to relevant articles in the literature. Extending a general topic model method, the approach introduced here establishes a workflow for the interpretation of gene sets generated from diverse experimental scenarios that can complement the classical approach of comparison to reference gene sets. PMID:24564875

Manganese-Induced Neurotoxicity and Alterations in Gene Expression in Human Neuroblastoma SH-SY5Y Cells.

PubMed

Gandhi, Deepa; Sivanesan, Saravanadevi; Kannan, Krishnamurthi

2018-06-01

Manganese (Mn) is an essential trace element required for many physiological functions including proper biochemical and cellular functioning of the central nervous system (CNS). However, exposure to excess level of Mn through occupational settings or from environmental sources has been associated with neurotoxicity. The cellular and molecular mechanism of Mn-induced neurotoxicity remains unclear. In the current study, we investigated the effects of 30-day exposure to a sub-lethal concentration of Mn (100 μM) in human neuroblastoma cells (SH-SY5Y) using transcriptomic approach. Microarray analysis revealed differential expression of 1057 transcripts in Mn-exposed SH-SY5Y cells as compared to control cells. Gene functional annotation cluster analysis exhibited that the differentially expressed genes were associated with several biological pathways. Specifically, genes involved in neuronal pathways including neuron differentiation and development, regulation of neurogenesis, synaptic transmission, and neuronal cell death (apoptosis) were found to be significantly altered. KEGG pathway analysis showed upregulation of p53 signaling pathways and neuroactive ligand-receptor interaction pathways, and downregulation of neurotrophin signaling pathway. On the basis of the gene expression profile, possible molecular mechanisms underlying Mn-induced neuronal toxicity were predicted.

Comparison of human cell signaling pathway databases—evolution, drawbacks and challenges

PubMed Central

Chowdhury, Saikat; Sarkar, Ram Rup

2015-01-01

Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community. PMID:25632107

The RNA-binding protein repertoire of embryonic stem cells.

PubMed

Kwon, S Chul; Yi, Hyerim; Eichelbaum, Katrin; Föhr, Sophia; Fischer, Bernd; You, Kwon Tae; Castello, Alfredo; Krijgsveld, Jeroen; Hentze, Matthias W; Kim, V Narry

2013-09-01

RNA-binding proteins (RBPs) have essential roles in RNA-mediated gene regulation, and yet annotation of RBPs is limited mainly to those with known RNA-binding domains. To systematically identify the RBPs of embryonic stem cells (ESCs), we here employ interactome capture, which combines UV cross-linking of RBP to RNA in living cells, oligo(dT) capture and MS. From mouse ESCs (mESCs), we have defined 555 proteins constituting the mESC mRNA interactome, including 283 proteins not previously annotated as RBPs. Of these, 68 new RBP candidates are highly expressed in ESCs compared to differentiated cells, implicating a role in stem-cell physiology. Two well-known E3 ubiquitin ligases, Trim25 (also called Efp) and Trim71 (also called Lin41), are validated as RBPs, revealing a potential link between RNA biology and protein-modification pathways. Our study confirms and expands the atlas of RBPs, providing a useful resource for the study of the RNA-RBP network in stem cells.

High-performance web services for querying gene and variant annotation.

PubMed

Xin, Jiwen; Mark, Adam; Afrasiabi, Cyrus; Tsueng, Ginger; Juchler, Moritz; Gopal, Nikhil; Stupp, Gregory S; Putman, Timothy E; Ainscough, Benjamin J; Griffith, Obi L; Torkamani, Ali; Whetzel, Patricia L; Mungall, Christopher J; Mooney, Sean D; Su, Andrew I; Wu, Chunlei

2016-05-06

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times permonth. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info . Both are offered free of charge to the research community.

Semantic similarity analysis of protein data: assessment with biological features and issues.

PubMed

Guzzi, Pietro H; Mina, Marco; Guerra, Concettina; Cannataro, Mario

2012-09-01

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.

New insights about host response to smallpox using microarray data

PubMed Central

Esteves, Gustavo H; Simoes, Ana CQ; Souza, Estevao; Dias, Rodrigo A; Ospina, Raydonal; Venancio, Thiago M

2007-01-01

Background Smallpox is a lethal disease that was endemic in many parts of the world until eradicated by massive immunization. Due to its lethality, there are serious concerns about its use as a bioweapon. Here we analyze publicly available microarray data to further understand survival of smallpox infected macaques, using systems biology approaches. Our goal is to improve the knowledge about the progression of this disease. Results We used KEGG pathways annotations to define groups of genes (or modules), and subsequently compared them to macaque survival times. This technique provided additional insights about the host response to this disease, such as increased expression of the cytokines and ECM receptors in the individuals with higher survival times. These results could indicate that these gene groups could influence an effective response from the host to smallpox. Conclusion Macaques with higher survival times clearly express some specific pathways previously unidentified using regular gene-by-gene approaches. Our work also shows how third party analysis of public datasets can be important to support new hypotheses to relevant biological problems. PMID:17718913

ODG: Omics database generator - a tool for generating, querying, and analyzing multi-omics comparative databases to facilitate biological understanding.

PubMed

Guhlin, Joseph; Silverstein, Kevin A T; Zhou, Peng; Tiffin, Peter; Young, Nevin D

2017-08-10

Rapid generation of omics data in recent years have resulted in vast amounts of disconnected datasets without systemic integration and knowledge building, while individual groups have made customized, annotated datasets available on the web with few ways to link them to in-lab datasets. With so many research groups generating their own data, the ability to relate it to the larger genomic and comparative genomic context is becoming increasingly crucial to make full use of the data. The Omics Database Generator (ODG) allows users to create customized databases that utilize published genomics data integrated with experimental data which can be queried using a flexible graph database. When provided with omics and experimental data, ODG will create a comparative, multi-dimensional graph database. ODG can import definitions and annotations from other sources such as InterProScan, the Gene Ontology, ENZYME, UniPathway, and others. This annotation data can be especially useful for studying new or understudied species for which transcripts have only been predicted, and rapidly give additional layers of annotation to predicted genes. In better studied species, ODG can perform syntenic annotation translations or rapidly identify characteristics of a set of genes or nucleotide locations, such as hits from an association study. ODG provides a web-based user-interface for configuring the data import and for querying the database. Queries can also be run from the command-line and the database can be queried directly through programming language hooks available for most languages. ODG supports most common genomic formats as well as generic, easy to use tab-separated value format for user-provided annotations. ODG is a user-friendly database generation and query tool that adapts to the supplied data to produce a comparative genomic database or multi-layered annotation database. ODG provides rapid comparative genomic annotation and is therefore particularly useful for non-model or understudied species. For species for which more data are available, ODG can be used to conduct complex multi-omics, pattern-matching queries.

BioModels.net Web Services, a free and integrated toolkit for computational modelling software.

PubMed

Li, Chen; Courtot, Mélanie; Le Novère, Nicolas; Laibe, Camille

2010-05-01

Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.

Database constraints applied to metabolic pathway reconstruction tools.

PubMed

Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

2014-01-01

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.

A homology-based pipeline for global prediction of post-translational modification sites

NASA Astrophysics Data System (ADS)

Chen, Xiang; Shi, Shao-Ping; Xu, Hao-Dong; Suo, Sheng-Bao; Qiu, Jian-Ding

2016-05-01

The pathways of protein post-translational modifications (PTMs) have been shown to play particularly important roles for almost any biological process. Identification of PTM substrates along with information on the exact sites is fundamental for fully understanding or controlling biological processes. Alternative computational strategies would help to annotate PTMs in a high-throughput manner. Traditional algorithms are suited for identifying the common organisms and tissues that have a complete PTM atlas or extensive experimental data. While annotation of rare PTMs in most organisms is a clear challenge. In this work, to this end we have developed a novel homology-based pipeline named PTMProber that allows identification of potential modification sites for most of the proteomes lacking PTMs data. Cross-promotion E-value (CPE) as stringent benchmark has been used in our pipeline to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions. In addition, we developed a web-based tool to integrate this pipeline at http://bioinfo.ncu.edu.cn/PTMProber/index.aspx. In addition to pre-constructed prediction models of PTM, the website provides an extensional functionality to allow users to customize models.

Considerations to improve functional annotations in biological databases.

PubMed

Benítez-Páez, Alfonso

2009-12-01

Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community.

Evaluating Computational Gene Ontology Annotations.

PubMed

Škunca, Nives; Roberts, Richard J; Steffen, Martin

2017-01-01

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

Bioinformatics approach reveals systematic mechanism underlying lung adenocarcinoma.

PubMed

Wu, Xiya; Zhang, Wei; Hu, Yunhua; Yi, Xianghua

2015-01-01

The purpose of this work was to explore the systematic molecular mechanism of lung adenocarcinoma and gain a deeper insight into it. Comprehensive bioinformatics methods were applied. Initially, significant differentially expressed genes (DEGs) were analyzed from the Affymetrix microarray data (GSE27262) deposited in the Gene Expression Omnibus (GEO). Subsequently, gene ontology (GO) analysis was performed using online Database for Annotation, Visualization and Integration Discovery (DAVID) software. Finally, significant pathway crosstalk was investigated based on the information derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. According to our results, the N-terminal globular domain of the type X collagen (COL10A1) gene and transmembrane protein 100 (TMEM100) gene were identified to be the most significant DEGs in tumor tissue compared with the adjacent normal tissues. The main GO categories were biological process, cellular component and molecular function. In addition, the crosstalk was significantly different between non-small cell lung cancer pathways and inositol phosphate metabolism pathway, focal adhesion signal pathway, vascular smooth muscle contraction signal pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway and calcium signaling pathway in tumor. Dysfunctional genes and pathways may play key roles in the progression and development of lung adenocarcinoma. Our data provide a systematic perspective for understanding this mechanism and may be helpful in discovering an effective treatment for lung adenocarcinoma.

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

PubMed

Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M

2018-03-15

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online.

The Chinchilla Research Resource Database: resource for an otolaryngology disease model

PubMed Central

Shimoyama, Mary; Smith, Jennifer R.; De Pons, Jeff; Tutaj, Marek; Khampang, Pawjai; Hong, Wenzhou; Erbe, Christy B.; Ehrlich, Garth D.; Bakaletz, Lauren O.; Kerschner, Joseph E.

2016-01-01

The long-tailed chinchilla (Chinchilla lanigera) is an established animal model for diseases of the inner and middle ear, among others. In particular, chinchilla is commonly used to study diseases involving viral and bacterial pathogens and polymicrobial infections of the upper respiratory tract and the ear, such as otitis media. The value of the chinchilla as a model for human diseases prompted the sequencing of its genome in 2012 and the more recent development of the Chinchilla Research Resource Database (http://crrd.mcw.edu) to provide investigators with easy access to relevant datasets and software tools to enhance their research. The Chinchilla Research Resource Database contains a complete catalog of genes for chinchilla and, for comparative purposes, human. Chinchilla genes can be viewed in the context of their genomic scaffold positions using the JBrowse genome browser. In contrast to the corresponding records at NCBI, individual gene reports at CRRD include functional annotations for Disease, Gene Ontology (GO) Biological Process, GO Molecular Function, GO Cellular Component and Pathway assigned to chinchilla genes based on annotations from the corresponding human orthologs. Data can be retrieved via keyword and gene-specific searches. Lists of genes with similar functional attributes can be assembled by leveraging the hierarchical structure of the Disease, GO and Pathway vocabularies through the Ontology Search and Browser tool. Such lists can then be further analyzed for commonalities using the Gene Annotator (GA) Tool. All data in the Chinchilla Research Resource Database is freely accessible and downloadable via the CRRD FTP site or using the download functions available in the search and analysis tools. The Chinchilla Research Resource Database is a rich resource for researchers using, or considering the use of, chinchilla as a model for human disease. Database URL: http://crrd.mcw.edu PMID:27173523

Sequencing and de novo assembly of visceral mass transcriptome of the critically endangered land snail Satsuma myomphala: Annotation and SSR discovery.

PubMed

Kang, Se Won; Patnaik, Bharat Bhusan; Hwang, Hee-Ju; Park, So Young; Chung, Jong Min; Song, Dae Kwon; Patnaik, Hongray Howrelia; Lee, Jae Bong; Kim, Changmu; Kim, Soonok; Park, Hong Seog; Park, Seung-Hwan; Park, Young-Su; Han, Yeon Soo; Lee, Jun Sang; Lee, Yong Seok

2017-03-01

Satsuma myomphala is critically endangered through loss of natural habitats, predation by natural enemies, and indiscriminate collection. It is a protected species in Korea but lacks genomic resources for an understanding of varied functional processes attributable to evolutionary success under natural habitats. For assessing the genetic information of S. myomphala, we performed for the first time, de novo transcriptome sequencing and functional annotation of expressed sequences using Illumina Next-Generation Sequencing (NGS) platform and bioinformatics analysis. We identified 103,774 unigenes of which 37,959, 12,890, and 17,699 were annotated in the PANM (Protostome DB), Unigene, and COG (Clusters of Orthologous Groups) databases, respectively. In addition, 14,451 unigenes were predicted under Gene Ontology functional categories, with 4581 assigned to a single category. Furthermore, 3369 sequences with 646 having Enzyme Commission (EC) numbers were mapped to 122 pathways in the Kyoto Encyclopedia of Genes and Genomes Pathway database. The prominent protein domains included the Zinc finger (C2H2-like), Reverse Transcriptase, Thioredoxin-like fold, and RNA recognition motif domain. Many unigenes with homology to immunity, defense, and reproduction-related genes were screened in the transcriptome. We also detected 3120 putative simple sequence repeats (SSRs) encompassing dinucleotide to hexanucleotide repeat motifs from >1kb unigene sequences. A list of PCR primers of SSR loci have been identified to study the genetic polymorphisms. The transcriptome data represents a valuable resource for further investigations on the species genome structure and biology. The unigenes information and microsatellites would provide an indispensable tool for conservation of the species in natural and adaptive environments. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Transcriptome analysis reveals the time of the fourth round of genome duplication in common carp (Cyprinus carpio)

PubMed Central

2012-01-01

Background Common carp (Cyprinus carpio) is thought to have undergone one extra round of genome duplication compared to zebrafish. Transcriptome analysis has been used to study the existence and timing of genome duplication in species for which genome sequences are incomplete. Large-scale transcriptome data for the common carp genome should help reveal the timing of the additional duplication event. Results We have sequenced the transcriptome of common carp using 454 pyrosequencing. After assembling the 454 contigs and the published common carp sequences together, we obtained 49,669 contigs and identified genes using homology searches and an ab initio method. We identified 4,651 orthologous pairs between common carp and zebrafish and found 129,984 paralogous pairs within the common carp. An estimation of the synonymous substitution rate in the orthologous pairs indicated that common carp and zebrafish diverged 120 million years ago (MYA). We identified one round of genome duplication in common carp and estimated that it had occurred 5.6 to 11.3 MYA. In zebrafish, no genome duplication event after speciation was observed, suggesting that, compared to zebrafish, common carp had undergone an additional genome duplication event. We annotated the common carp contigs with Gene Ontology terms and KEGG pathways. Compared with zebrafish gene annotations, we found that a set of biological processes and pathways were enriched in common carp. Conclusions The assembled contigs helped us to estimate the time of the fourth-round of genome duplication in common carp. The resource that we have built as part of this study will help advance functional genomics and genome annotation studies in the future. PMID:22424280

Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns† †The authors declare no competing interests. ‡ ‡Electronic supplementary information (ESI) available: Pipeline Pilot protocols, xls file with the output of the Pipeline Pilot protocols, KNIME workflows, and supplementary figures showing the Pipeline Pilot protocols. See DOI: 10.1039/c6md00065g Click here for additional data file.

PubMed Central

Zdrazil, B.; Neefs, J.-M.; Van Vlijmen, H.; Herhaus, C.; Caracoti, A.; Brea, J.; Roibás, B.; Loza, M. I.; Queralt-Rosinach, N.; Furlong, L. I.; Gaulton, A.; Bartek, L.; Senger, S.; Chichester, C.; Engkvist, O.; Evelo, C. T.; Franklin, N. I.; Marren, D.; Ecker, G. F.

2016-01-01

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools. PMID:27774140

Annotare--a tool for annotating high-throughput biomedical investigations and resulting data.

PubMed

Shankar, Ravi; Parkinson, Helen; Burdett, Tony; Hastings, Emma; Liu, Junmin; Miller, Michael; Srinivasa, Rashmi; White, Joseph; Brazma, Alvis; Sherlock, Gavin; Stoeckert, Christian J; Ball, Catherine A

2010-10-01

Computational methods in molecular biology will increasingly depend on standards-based annotations that describe biological experiments in an unambiguous manner. Annotare is a software tool that enables biologists to easily annotate their high-throughput experiments, biomaterials and data in a standards-compliant way that facilitates meaningful search and analysis. Annotare is available from http://code.google.com/p/annotare/ under the terms of the open-source MIT License (http://www.opensource.org/licenses/mit-license.php). It has been tested on both Mac and Windows.

A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species.

PubMed

Perumal, Deepak; Lim, Chu Sing; Chow, Vincent T K; Sakharkar, Kishore R; Sakharkar, Meena K

2008-09-10

Comparative genomic analysis has revolutionized our ability to predict the metabolic subsystems that occur in newly sequenced genomes, and to explore the functional roles of the set of genes within each subsystem. These computational predictions can considerably reduce the volume of experimental studies required to assess basic metabolic properties of multiple bacterial species. However, experimental validations are still required to resolve the apparent inconsistencies in the predictions by multiple resources. Here, we present combined computational-experimental analyses on eight completely sequenced Pseudomonas species. Comparative pathway analyses reveal that several pathways within the Pseudomonas species show high plasticity and versatility. Potential bypasses in 11 metabolic pathways were identified. We further confirmed the presence of the enzyme O-acetyl homoserine (thiol) lyase (EC: 2.5.1.49) in P. syringae pv. tomato that revealed inconsistent annotations in KEGG and in the recently published SYSTOMONAS database. These analyses connect and integrate systematic data generation, computational data interpretation, and experimental validation and represent a synergistic and powerful means for conducting biological research.

Genome sequence analysis of a flocculant-producing bacterium, Paenibacillus shenyangensis.

PubMed

Fu, Lili; Jiang, Binhui; Liu, Jinliang; Zhao, Xin; Liu, Qian; Hu, Xiaomin

2016-03-01

To explore the metabolic process of Paenibacillus shenyangensis that is an efficient bioflocculant-producing bacterium. The biosynthesis mechanism of bioflocculation was used to enrich the genome of Paenibacillus shenyangensis and provide a basis for molecular genetics and functional genomics analyses. According to the analysis of de novo assembly, a total of 5,501,467 bp clean reads were generated, and were assembled into 92 contigs. 4800 unigenes were predicted of which 4393 were annotated showing a specific gene function in the NCBI-Nr database. 3423 genes were found in the database of cluster of orthologous groups. Among the 168 Kyoto Encyclopedia of Genes and Genomes database, cell growth and metabolism were the main biological processes, and a potential metabolic pathway was predicted from glucose to exopolysaccharide within the starch and sucrose metabolism pathway. By using the high-throughput sequencing technology, we provide a genome analysis of Paenibacillus shenyangensis that predicts the main metabolic processes and a potential pathway of exopolysaccharide biosynthesis.

Systems Biology Approach Reveals a Calcium-Dependent Mechanism for Basal Toxicity in Daphnia magna.

PubMed

Antczak, Philipp; White, Thomas A; Giri, Anirudha; Michelangeli, Francesco; Viant, Mark R; Cronin, Mark T D; Vulpe, Chris; Falciani, Francesco

2015-09-15

The expanding diversity and ever increasing amounts of man-made chemicals discharged to the environment pose largely unknown hazards to ecosystem and human health. The concept of adverse outcome pathways (AOPs) emerged as a comprehensive framework for risk assessment. However, the limited mechanistic information available for most chemicals and a lack of biological pathway annotation in many species represent significant challenges to effective implementation of this approach. Here, a systems level, multistep modeling strategy demonstrates how to integrate information on chemical structure with mechanistic insight from genomic studies, and phenotypic effects to define a putative adverse outcome pathway. Results indicated that transcriptional changes indicative of intracellular calcium mobilization were significantly overrepresented in Daphnia magna (DM) exposed to sublethal doses of presumed narcotic chemicals with log Kow ≥ 1.8. Treatment of DM with a calcium ATPase pump inhibitor substantially recapitulated the common transcriptional changes. We hypothesize that calcium mobilization is a potential key molecular initiating event in DM basal (narcosis) toxicity. Heart beat rate analysis and metabolome analysis indicated sublethal effects consistent with perturbations of calcium preceding overt acute toxicity. Together, the results indicate that altered calcium homeostasis may be a key early event in basal toxicity or narcosis induced by lipophilic compounds.

Literature Mining of Pathogenesis-Related Proteins in Human Pathogens for Database Annotation

DTIC Science & Technology

2009-10-01

Salmonella , and Shigella. In most cases the host is human, but may also include other mammal species. 2. Negative literature set of PH-PPIs. Of...cis.udel.edu The objective of Gallus Reactome is to provide a curated set of metabolic and signaling pathways for the chicken . To assist annotators...interested in papers that document pathways in the chicken , abstracts are classified according to the species that were the source of the experimental

Annotation and visualization of endogenous retroviral sequences using the Distributed Annotation System (DAS) and eBioX

PubMed Central

Martínez Barrio, Álvaro; Lagercrantz, Erik; Sperber, Göran O; Blomberg, Jonas; Bongcam-Rudloff, Erik

2009-01-01

Background The Distributed Annotation System (DAS) is a widely used network protocol for sharing biological information. The distributed aspects of the protocol enable the use of various reference and annotation servers for connecting biological sequence data to pertinent annotations in order to depict an integrated view of the data for the final user. Results An annotation server has been devised to provide information about the endogenous retroviruses detected and annotated by a specialized in silico tool called RetroTector. We describe the procedure to implement the DAS 1.5 protocol commands necessary for constructing the DAS annotation server. We use our server to exemplify those steps. Data distribution is kept separated from visualization which is carried out by eBioX, an easy to use open source program incorporating multiple bioinformatics utilities. Some well characterized endogenous retroviruses are shown in two different DAS clients. A rapid analysis of areas free from retroviral insertions could be facilitated by our annotations. Conclusion The DAS protocol has shown to be advantageous in the distribution of endogenous retrovirus data. The distributed nature of the protocol is also found to aid in combining annotation and visualization along a genome in order to enhance the understanding of ERV contribution to its evolution. Reference and annotation servers are conjointly used by eBioX to provide visualization of ERV annotations as well as other data sources. Our DAS data source can be found in the central public DAS service repository, , or at . PMID:19534743

Combinatory annotation of cell membrane receptors and signalling pathways of Bombyx mori prothoracic glands

PubMed Central

Moulos, Panagiotis; Samiotaki, Martina; Panayotou, George; Dedos, Skarlatos G.

2016-01-01

The cells of prothoracic glands (PG) are the main site of synthesis and secretion of ecdysteroids, the biochemical products of cholesterol conversion to steroids that shape the morphogenic development of insects. Despite the availability of genome sequences from several insect species and the extensive knowledge of certain signalling pathways that underpin ecdysteroidogenesis, the spectrum of signalling molecules and ecdysteroidogenic cascades is still not fully comprehensive. To fill this gap and obtain the complete list of cell membrane receptors expressed in PG cells, we used combinatory bioinformatic, proteomic and transcriptomic analysis and quantitative PCR to annotate and determine the expression profiles of genes identified as putative cell membrane receptors of the model insect species, Bombyx mori, and subsequently enrich the repertoire of signalling pathways that are present in its PG cells. The genome annotation dataset we report here highlights modules and pathways that may be directly involved in ecdysteroidogenesis and aims to disseminate data and assist other researchers in the discovery of the role of such receptors and their ligands. PMID:27576083

Microarray profiling analysis uncovers common molecular mechanisms of rubella virus, human cytomegalovirus, and herpes simplex virus type 2 infections in ECV304 cells.

PubMed

Mo, X; Xu, L; Yang, Q; Feng, H; Peng, J; Zhang, Y; Yuan, W; Wang, Y; Li, Y; Deng, Y; Wan, Y; Chen, Z; Li, F; Wu, X

2011-08-01

To study the common molecular mechanisms of various viruses infections that might result in congential cardiovascular diseases in perinatal period, changes in mRNA expression levels of ECV304 cells infected by rubella virus (RUBV), human cytomegalovirus (HCMV), and herpes simplex virus type 2 (HSV-2) were analyzed using a microarray system representing 18,716 human genes. 99 genes were found to exhibit differential expression (80 up-regulated and 19 down-regulated). Biological process analysis showed that 33 signaling pathways including 22 genes were relevant significantly to RV, HCMV and HSV-II infections. Of these 33 biological processes, 28 belong to one-gene biological processes and 5 belong to multiple-gene biological processes. Gene annotation indicated that the 5 multiple-gene biological processes including regulation of cell growth, collagen fibril organization, mRNA transport, cell adhesion and regulation of cell shape, and seven down- or up-regulated genes [CRIM1 (cysteine rich transmembrane BMP regulator 1), WISP2 (WNT1 inducible signaling pathway protein 2), COL12A1 (collagen, type XII, alpha 1), COL11A2 (collagen, type XI, alpha 2), CNTN5 (contactin 5), DDR1 (discoidin domain receptor tyrosine kinase 1), VEGF (vascular endothelial growth factor precursor)], are significantly correlated to RUBV, HCMV and HSV-2 infections in ECV304 cells. The results obtained in this study suggested the common molecular mechanisms of viruses infections that might result in congential cardiovascular diseases.

Comparative proteomic exploration of whey proteins in human and bovine colostrum and mature milk using iTRAQ-coupled LC-MS/MS.

PubMed

Yang, Mei; Cao, Xueyan; Wu, Rina; Liu, Biao; Ye, Wenhui; Yue, Xiqing; Wu, Junrui

2017-09-01

Whey, an essential source of dietary nutrients, is widely used in dairy foods for infants. A total of 584 whey proteins in human and bovine colostrum and mature milk were identified and quantified by the isobaric tag for relative and absolute quantification (iTRAQ) proteomic method. The 424 differentially expressed whey proteins were identified and analyzed according to gene ontology (GO) annotation, Kyoto encyclopedia of genes and genomes (KEGG) pathway, and multivariate statistical analysis. Biological processes principally involved biological regulation and response to stimulus. Major cellular components were extracellular region part and extracellular space. The most prevalent molecular function was protein binding. Twenty immune-related proteins and 13 proteins related to enzyme regulatory activity were differentially expressed in human and bovine milk. Differentially expressed whey proteins participated in many KEGG pathways, including major complement and coagulation cascades and in phagosomes. Whey proteins show obvious differences in expression in human and bovine colostrum and mature milk, with consequences for biological function. The results here increase our understanding of different whey proteomes, which could provide useful information for the development and manufacture of dairy products and nutrient food for infants. The advanced iTRAQ proteomic approach was used to analyze differentially expressed whey proteins in human and bovine colostrum and mature milk.

Enzyme Informatics

PubMed Central

Alderson, Rosanna G.; Ferrari, Luna De; Mavridis, Lazaros; McDonagh, James L.; Mitchell, John B. O.; Nath, Neetika

2012-01-01

Over the last 50 years, sequencing, structural biology and bioinformatics have completely revolutionised biomolecular science, with millions of sequences and tens of thousands of three dimensional structures becoming available. The bioinformatics of enzymes is well served by, mostly free, online databases. BRENDA describes the chemistry, substrate specificity, kinetics, preparation and biological sources of enzymes, while KEGG is valuable for understanding enzymes and metabolic pathways. EzCatDB, SFLD and MACiE are key repositories for data on the chemical mechanisms by which enzymes operate. At the current rate of genome sequencing and manual annotation, human curation will never finish the functional annotation of the ever-expanding list of known enzymes. Hence there is an increasing need for automated annotation, though it is not yet widespread for enzyme data. In contrast, functional ontologies such as the Gene Ontology already profit from automation. Despite our growing understanding of enzyme structure and dynamics, we are only beginning to be able to design novel enzymes. One can now begin to trace the functional evolution of enzymes using phylogenetics. The ability of enzymes to perform secondary functions, albeit relatively inefficiently, gives clues as to how enzyme function evolves. Substrate promiscuity in enzymes is one example of imperfect specificity in protein-ligand interactions. Similarly, most drugs bind to more than one protein target. This may sometimes result in helpful polypharmacology as a drug modulates plural targets, but also often leads to adverse side-effects. Many cheminformatics approaches can be used to model the interactions between druglike molecules and proteins in silico. We can even use quantum chemical techniques like DFT and QM/MM to compute the structural and energetic course of enzyme catalysed chemical reaction mechanisms, including a full description of bond making and breaking. PMID:23116471

MorphDB: Prioritizing Genes for Specialized Metabolism Pathways and Gene Ontology Categories in Plants.

PubMed

Zwaenepoel, Arthur; Diels, Tim; Amar, David; Van Parys, Thomas; Shamir, Ron; Van de Peer, Yves; Tzfadia, Oren

2018-01-01

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest.

Nuclear Receptor Signaling Atlas: Opening Access to the Biology of Nuclear Receptor Signaling Pathways.

PubMed

Becnel, Lauren B; Darlington, Yolanda F; Ochsner, Scott A; Easton-Marks, Jeremy R; Watkins, Christopher M; McOwiti, Apollo; Kankanamge, Wasula H; Wise, Michael W; DeHart, Michael; Margolis, Ronald N; McKenna, Neil J

2015-01-01

Signaling pathways involving nuclear receptors (NRs), their ligands and coregulators, regulate tissue-specific transcriptomes in diverse processes, including development, metabolism, reproduction, the immune response and neuronal function, as well as in their associated pathologies. The Nuclear Receptor Signaling Atlas (NURSA) is a Consortium focused around a Hub website (www.nursa.org) that annotates and integrates diverse 'omics datasets originating from the published literature and NURSA-funded Data Source Projects (NDSPs). These datasets are then exposed to the scientific community on an Open Access basis through user-friendly data browsing and search interfaces. Here, we describe the redesign of the Hub, version 3.0, to deploy "Web 2.0" technologies and add richer, more diverse content. The Molecule Pages, which aggregate information relevant to NR signaling pathways from myriad external databases, have been enhanced to include resources for basic scientists, such as post-translational modification sites and targeting miRNAs, and for clinicians, such as clinical trials. A portal to NURSA's Open Access, PubMed-indexed journal Nuclear Receptor Signaling has been added to facilitate manuscript submissions. Datasets and information on reagents generated by NDSPs are available, as is information concerning periodic new NDSP funding solicitations. Finally, the new website integrates the Transcriptomine analysis tool, which allows for mining of millions of richly annotated public transcriptomic data points in the field, providing an environment for dataset re-use and citation, bench data validation and hypothesis generation. We anticipate that this new release of the NURSA database will have tangible, long term benefits for both basic and clinical research in this field.

Quantitative proteomic analysis of milk fat globule membrane (MFGM) proteins in human and bovine colostrum and mature milk samples through iTRAQ labeling.

PubMed

Yang, Mei; Cong, Min; Peng, Xiuming; Wu, Junrui; Wu, Rina; Liu, Biao; Ye, Wenhui; Yue, Xiqing

2016-05-18

Milk fat globule membrane (MFGM) proteins have many functions. To explore the different proteomics of human and bovine MFGM, MFGM proteins were separated from human and bovine colostrum and mature milk, and analyzed by the iTRAQ proteomic approach. A total of 411 proteins were recognized and quantified. Among these, 232 kinds of differentially expressed proteins were identified. These differentially expressed proteins were analyzed based on multivariate analysis, gene ontology (GO) annotation and KEGG pathway. Biological processes involved were response to stimulus, localization, establishment of localization, and the immune system process. Cellular components engaged were the extracellular space, extracellular region parts, cell fractions, and vesicles. Molecular functions touched upon were protein binding, nucleotide binding, and enzyme inhibitor activity. The KEGG pathway analysis showed several pathways, including regulation of the actin cytoskeleton, focal adhesion, neurotrophin signaling pathway, leukocyte transendothelial migration, tight junction, complement and coagulation cascades, vascular endothelial growth factor signaling pathway, and adherens junction. These results enhance our understanding of different proteomes of human and bovine MFGM across different lactation phases, which could provide important information and potential directions for the infant milk powder and functional food industries.

Annotare—a tool for annotating high-throughput biomedical investigations and resulting data

PubMed Central

Shankar, Ravi; Parkinson, Helen; Burdett, Tony; Hastings, Emma; Liu, Junmin; Miller, Michael; Srinivasa, Rashmi; White, Joseph; Brazma, Alvis; Sherlock, Gavin; Stoeckert, Christian J.; Ball, Catherine A.

2010-01-01

Summary: Computational methods in molecular biology will increasingly depend on standards-based annotations that describe biological experiments in an unambiguous manner. Annotare is a software tool that enables biologists to easily annotate their high-throughput experiments, biomaterials and data in a standards-compliant way that facilitates meaningful search and analysis. Availability and Implementation: Annotare is available from http://code.google.com/p/annotare/ under the terms of the open-source MIT License (http://www.opensource.org/licenses/mit-license.php). It has been tested on both Mac and Windows. Contact: rshankar@stanford.edu PMID:20733062

GeneTools--application for functional annotation and statistical hypothesis testing.

PubMed

Beisvag, Vidar; Jünge, Frode K R; Bergum, Hallgeir; Jølsum, Lars; Lydersen, Stian; Günther, Clara-Cecilie; Ramampiaro, Heri; Langaas, Mette; Sandvik, Arne K; Laegreid, Astrid

2006-10-24

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis. GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

PubMed Central

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162

Biodegradation of DDT by Stenotrophomonas sp. DDT-1: Characterization and genome functional analysis

NASA Astrophysics Data System (ADS)

Pan, Xiong; Lin, Dunli; Zheng, Yuan; Zhang, Qian; Yin, Yuanming; Cai, Lin; Fang, Hua; Yu, Yunlong

2016-02-01

A novel bacterium capable of utilizing 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) as the sole carbon and energy source was isolated from a contaminated soil which was identified as Stenotrophomonas sp. DDT-1 based on morphological characteristics, BIOLOG GN2 microplate profile, and 16S rDNA phylogeny. Genome sequencing and functional annotation of the isolate DDT-1 showed a 4,514,569 bp genome size, 66.92% GC content, 4,033 protein-coding genes, and 76 RNA genes including 8 rRNA genes. Totally, 2,807 protein-coding genes were assigned to Clusters of Orthologous Groups (COGs), and 1,601 protein-coding genes were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The degradation half-lives of DDT increased with substrate concentration from 0.1 to 10.0 mg/l, whereas decreased with temperature from 15 °C to 35 °C. Neutral condition was the most favorable for DDT biodegradation. Based on genome annotation of DDT degradation genes and the metabolites detected by GC-MS, a mineralization pathway was proposed for DDT biodegradation in which it was orderly converted into DDE/DDD, DDMU, DDOH, and DDA via dechlorination, hydroxylation, and carboxylation, and ultimately mineralized to carbon dioxide. The results indicate that the isolate DDT-1 is a promising bacterial resource for the removal or detoxification of DDT residues in the environment.

Biodegradation of DDT by Stenotrophomonas sp. DDT-1: Characterization and genome functional analysis.

PubMed

Pan, Xiong; Lin, Dunli; Zheng, Yuan; Zhang, Qian; Yin, Yuanming; Cai, Lin; Fang, Hua; Yu, Yunlong

2016-02-18

A novel bacterium capable of utilizing 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) as the sole carbon and energy source was isolated from a contaminated soil which was identified as Stenotrophomonas sp. DDT-1 based on morphological characteristics, BIOLOG GN2 microplate profile, and 16S rDNA phylogeny. Genome sequencing and functional annotation of the isolate DDT-1 showed a 4,514,569 bp genome size, 66.92% GC content, 4,033 protein-coding genes, and 76 RNA genes including 8 rRNA genes. Totally, 2,807 protein-coding genes were assigned to Clusters of Orthologous Groups (COGs), and 1,601 protein-coding genes were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The degradation half-lives of DDT increased with substrate concentration from 0.1 to 10.0 mg/l, whereas decreased with temperature from 15 °C to 35 °C. Neutral condition was the most favorable for DDT biodegradation. Based on genome annotation of DDT degradation genes and the metabolites detected by GC-MS, a mineralization pathway was proposed for DDT biodegradation in which it was orderly converted into DDE/DDD, DDMU, DDOH, and DDA via dechlorination, hydroxylation, and carboxylation, and ultimately mineralized to carbon dioxide. The results indicate that the isolate DDT-1 is a promising bacterial resource for the removal or detoxification of DDT residues in the environment.

CycADS: an annotation database system to ease the development and update of BioCyc databases

PubMed Central

Vellozo, Augusto F.; Véron, Amélie S.; Baa-Puyoulet, Patrice; Huerta-Cepas, Jaime; Cottret, Ludovic; Febvay, Gérard; Calevro, Federica; Rahbé, Yvan; Douglas, Angela E.; Gabaldón, Toni; Sagot, Marie-France; Charles, Hubert; Colella, Stefano

2011-01-01

In recent years, genomes from an increasing number of organisms have been sequenced, but their annotation remains a time-consuming process. The BioCyc databases offer a framework for the integrated analysis of metabolic networks. The Pathway tool software suite allows the automated construction of a database starting from an annotated genome, but it requires prior integration of all annotations into a specific summary file or into a GenBank file. To allow the easy creation and update of a BioCyc database starting from the multiple genome annotation resources available over time, we have developed an ad hoc data management system that we called Cyc Annotation Database System (CycADS). CycADS is centred on a specific database model and on a set of Java programs to import, filter and export relevant information. Data from GenBank and other annotation sources (including for example: KAAS, PRIAM, Blast2GO and PhylomeDB) are collected into a database to be subsequently filtered and extracted to generate a complete annotation file. This file is then used to build an enriched BioCyc database using the PathoLogic program of Pathway Tools. The CycADS pipeline for annotation management was used to build the AcypiCyc database for the pea aphid (Acyrthosiphon pisum) whose genome was recently sequenced. The AcypiCyc database webpage includes also, for comparative analyses, two other metabolic reconstruction BioCyc databases generated using CycADS: TricaCyc for Tribolium castaneum and DromeCyc for Drosophila melanogaster. Linked to its flexible design, CycADS offers a powerful software tool for the generation and regular updating of enriched BioCyc databases. The CycADS system is particularly suited for metabolic gene annotation and network reconstruction in newly sequenced genomes. Because of the uniform annotation used for metabolic network reconstruction, CycADS is particularly useful for comparative analysis of the metabolism of different organisms. Database URL: http://www.cycadsys.org PMID:21474551

Large-scale annotation of small-molecule libraries using public databases.

PubMed

Zhou, Yingyao; Zhou, Bin; Chen, Kaisheng; Yan, S Frank; King, Frederick J; Jiang, Shumei; Winzeler, Elizabeth A

2007-01-01

While many large publicly accessible databases provide excellent annotation for biological macromolecules, the same is not true for small chemical compounds. Commercial data sources also fail to encompass an annotation interface for large numbers of compounds and tend to be cost prohibitive to be widely available to biomedical researchers. Therefore, using annotation information for the selection of lead compounds from a modern day high-throughput screening (HTS) campaign presently occurs only under a very limited scale. The recent rapid expansion of the NIH PubChem database provides an opportunity to link existing biological databases with compound catalogs and provides relevant information that potentially could improve the information garnered from large-scale screening efforts. Using the 2.5 million compound collection at the Genomics Institute of the Novartis Research Foundation (GNF) as a model, we determined that approximately 4% of the library contained compounds with potential annotation in such databases as PubChem and the World Drug Index (WDI) as well as related databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and ChemIDplus. Furthermore, the exact structure match analysis showed 32% of GNF compounds can be linked to third party databases via PubChem. We also showed annotations such as MeSH (medical subject headings) terms can be applied to in-house HTS databases in identifying signature biological inhibition profiles of interest as well as expediting the assay validation process. The automated annotation of thousands of screening hits in batch is becoming feasible and has the potential to play an essential role in the hit-to-lead decision making process.

Gonadal Transcriptome Analysis of Male and Female Olive Flounder (Paralichthys olivaceus)

PubMed Central

Fan, Zhaofei; You, Feng; Wang, Lijuan; Weng, Shenda; Wu, Zhihao; Hu, Jinwei; Zou, Yuxia; Tan, Xungang; Zhang, Peijun

2014-01-01

Olive flounder (Paralichthys olivaceus) is an important commercially cultured marine flatfish in China, Korea, and Japan, of which female grows faster than male. In order to explore the molecular mechanism of flounder sex determination and development, we used RNA-seq technology to investigate transcriptomes of flounder gonads. This produced 22,253,217 and 19,777,841 qualified reads from ovary and testes, which were jointly assembled into 97,233 contigs. Among them, 23,223 contigs were mapped to known genes, of which 2,193 were predicted to be differentially expressed in ovary and 887 in testes. According to annotation information, several sex-related biological pathways including ovarian steroidogenesis and estrogen signaling pathways were firstly found in flounder. The dimorphic expression of overall sex-related genes provides further insights into sex determination and gonadal development. Our study also provides an archive for further studies of molecular mechanism of fish sex determination. PMID:25121093

Efficient discovery of bioactive scaffolds by activity-directed synthesis

NASA Astrophysics Data System (ADS)

Karageorgis, George; Warriner, Stuart; Nelson, Adam

2014-10-01

The structures and biological activities of natural products have often provided inspiration in drug discovery. The functional benefits of natural products to the host organism steers the evolution of their biosynthetic pathways. Here, we describe a discovery approach—which we term activity-directed synthesis—in which reactions with alternative outcomes are steered towards functional products. Arrays of catalysed reactions of α-diazo amides, whose outcome was critically dependent on the specific conditions used, were performed. The products were assayed at increasingly low concentration, with the results informing the design of a subsequent reaction array. Finally, promising reactions were scaled up and, after purification, submicromolar ligands based on two scaffolds with no previous annotated activity against the androgen receptor were discovered. The approach enables the discovery, in tandem, of both bioactive small molecules and associated synthetic routes, analogous to the evolution of biosynthetic pathways to yield natural products.

Distinguishing friends, foes, and freeloaders in giant genomes.

PubMed

Bennetzen, Jeffrey L; Park, Minkyu

2018-04-01

Most annotations of large eukaryotic genomes initially find transposable elements (TEs) and other repeats, then mask them so that subsequent efforts can be concentrated on the annotation and study of non-TE genes. However, TEs often contribute to host biology, and their community biologies are of intrinsic interest. This review discusses the challenges, rationale and technologies for comprehensive TE annotation in the commonly giant genomes of animals and plants. Complete discovery of the TEs in a fully sequenced genome is laborious, but feasible, with current strategies in the hands of a careful researcher. These deep TE studies have begun to provide important perspectives on how genomes evolve and the degree to which genome changes do and do not affect eukaryotic biology. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes

PubMed Central

Mohamad Zobir, Siti Zuraidah; Mohd Fauzi, Fazlin; Liggi, Sonia; Drakakis, Georgios; Fu, Xianjun; Fan, Tai-Ping; Bender, Andreas

2016-01-01

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications. PMID:26989424

ADGO: analysis of differentially expressed gene sets using composite GO annotation.

PubMed

Nam, Dougu; Kim, Sang-Bae; Kim, Seon-Kyu; Yang, Sungjin; Kim, Seon-Young; Chu, In-Sun

2006-09-15

Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. chu@kribb.re.kr http://array.kobic.re.kr/ADGO.

Rhizoma Dioscoreae extract protects against alveolar bone loss by regulating the cell cycle: A predictive study based on the protein‑protein interaction network.

PubMed

Zhang, Zhi-Guo; Song, Chang-Heng; Zhang, Fang-Zhen; Chen, Yan-Jing; Xiang, Li-Hua; Xiao, Gary Guishan; Ju, Da-Hong

2016-06-01

Rhizoma Dioscoreae extract (RDE) exhibits a protective effect on alveolar bone loss in ovariectomized (OVX) rats. The aim of this study was to predict the pathways or targets that are regulated by RDE, by re‑assessing our previously reported data and conducting a protein‑protein interaction (PPI) network analysis. In total, 383 differentially expressed genes (≥3‑fold) between alveolar bone samples from the RDE and OVX group rats were identified, and a PPI network was constructed based on these genes. Furthermore, four molecular clusters (A‑D) in the PPI network with the smallest P‑values were detected by molecular complex detection (MCODE) algorithm. Using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Ingenuity Pathway Analysis (IPA) tools, two molecular clusters (A and B) were enriched for biological process in Gene Ontology (GO). Only cluster A was associated with biological pathways in the IPA database. GO and pathway analysis results showed that cluster A, associated with cell cycle regulation, was the most important molecular cluster in the PPI network. In addition, cyclin‑dependent kinase 1 (CDK1) may be a key molecule achieving the cell‑cycle‑regulatory function of cluster A. From the PPI network analysis, it was predicted that delayed cell cycle progression in excessive alveolar bone remodeling via downregulation of CDK1 may be another mechanism underling the anti‑osteopenic effect of RDE on alveolar bone.

t4 Workshop Report*

PubMed Central

Kleensang, Andre; Maertens, Alexandra; Rosenberg, Michael; Fitzpatrick, Suzanne; Lamb, Justin; Auerbach, Scott; Brennan, Richard; Crofton, Kevin M.; Gordon, Ben; Fornace, Albert J.; Gaido, Kevin; Gerhold, David; Haw, Robin; Henney, Adriano; Ma’ayan, Avi; McBride, Mary; Monti, Stefano; Ochs, Michael F.; Pandey, Akhilesh; Sharan, Roded; Stierum, Rob; Tugendreich, Stuart; Willett, Catherine; Wittwehr, Clemens; Xia, Jianguo; Patton, Geoffrey W.; Arvidson, Kirk; Bouhifd, Mounir; Hogberg, Helena T.; Luechtefeld, Thomas; Smirnova, Lena; Zhao, Liang; Adeleye, Yeyejide; Kanehisa, Minoru; Carmichael, Paul; Andersen, Melvin E.; Hartung, Thomas

2014-01-01

Summary Despite wide-spread consensus on the need to transform toxicology and risk assessment in order to keep pace with technological and computational changes that have revolutionized the life sciences, there remains much work to be done to achieve the vision of toxicology based on a mechanistic foundation. A workshop was organized to explore one key aspect of this transformation – the development of Pathways of Toxicity (PoT) as a key tool for hazard identification based on systems biology. Several issues were discussed in depth in the workshop: The first was the challenge of formally defining the concept of a PoT as distinct from, but complementary to, other toxicological pathway concepts such as mode of action (MoA). The workshop came up with a preliminary definition of PoT as “A molecular definition of cellular processes shown to mediate adverse outcomes of toxicants”. It is further recognized that normal physiological pathways exist that maintain homeostasis and these, sufficiently perturbed, can become PoT. Second, the workshop sought to define the adequate public and commercial resources for PoT information, including data, visualization, analyses, tools, and use-cases, as well as the kinds of efforts that will be necessary to enable the creation of such a resource. Third, the workshop explored ways in which systems biology approaches could inform pathway annotation, and which resources are needed and available that can provide relevant PoT information to the diverse user communities. PMID:24127042

Structuring osteosarcoma knowledge: an osteosarcoma-gene association database based on literature mining and manual annotation.

PubMed

Poos, Kathrin; Smida, Jan; Nathrath, Michaela; Maugg, Doris; Baumhoer, Daniel; Neumann, Anna; Korsching, Eberhard

2014-01-01

Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers. Up to now, clinico-pathological factors are the key determinants to guide prognosis and therapeutic treatments. Each day, new studies about OS are published and complicate the acquisition of information to support biomarker discovery and therapeutic improvements. Thus, it is necessary to provide a structured and annotated view on the current OS knowledge that is quick and easily accessible to researchers of the field. Therefore, we developed a publicly available database and Web interface that serves as resource for OS-associated genes and microRNAs. Genes and microRNAs were collected using an automated dictionary-based gene recognition procedure followed by manual review and annotation by experts of the field. In total, 911 genes and 81 microRNAs related to 1331 PubMed abstracts were collected (last update: 29 October 2013). Users can evaluate genes and microRNAs according to their potential prognostic and therapeutic impact, the experimental procedures, the sample types, the biological contexts and microRNA target gene interactions. Additionally, a pathway enrichment analysis of the collected genes highlights different aspects of OS progression. OS requires pathways commonly deregulated in cancer but also features OS-specific alterations like deregulated osteoclast differentiation. To our knowledge, this is the first effort of an OS database containing manual reviewed and annotated up-to-date OS knowledge. It might be a useful resource especially for the bone tumor research community, as specific information about genes or microRNAs is quick and easily accessible. Hence, this platform can support the ongoing OS research and biomarker discovery. Database URL: http://osteosarcoma-db.uni-muenster.de. © The Author(s) 2014. Published by Oxford University Press.

Structuring osteosarcoma knowledge: an osteosarcoma-gene association database based on literature mining and manual annotation

PubMed Central

Poos, Kathrin; Smida, Jan; Nathrath, Michaela; Maugg, Doris; Baumhoer, Daniel; Neumann, Anna; Korsching, Eberhard

2014-01-01

Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers. Up to now, clinico-pathological factors are the key determinants to guide prognosis and therapeutic treatments. Each day, new studies about OS are published and complicate the acquisition of information to support biomarker discovery and therapeutic improvements. Thus, it is necessary to provide a structured and annotated view on the current OS knowledge that is quick and easily accessible to researchers of the field. Therefore, we developed a publicly available database and Web interface that serves as resource for OS-associated genes and microRNAs. Genes and microRNAs were collected using an automated dictionary-based gene recognition procedure followed by manual review and annotation by experts of the field. In total, 911 genes and 81 microRNAs related to 1331 PubMed abstracts were collected (last update: 29 October 2013). Users can evaluate genes and microRNAs according to their potential prognostic and therapeutic impact, the experimental procedures, the sample types, the biological contexts and microRNA target gene interactions. Additionally, a pathway enrichment analysis of the collected genes highlights different aspects of OS progression. OS requires pathways commonly deregulated in cancer but also features OS-specific alterations like deregulated osteoclast differentiation. To our knowledge, this is the first effort of an OS database containing manual reviewed and annotated up-to-date OS knowledge. It might be a useful resource especially for the bone tumor research community, as specific information about genes or microRNAs is quick and easily accessible. Hence, this platform can support the ongoing OS research and biomarker discovery. Database URL: http://osteosarcoma-db.uni-muenster.de PMID:24865352

Comparative transcriptome analysis of shoot and root tissue of Bacopa monnieri identifies potential genes related to triterpenoid saponin biosynthesis.

PubMed

Jeena, Gajendra Singh; Fatima, Shahnoor; Tripathi, Pragya; Upadhyay, Swati; Shukla, Rakesh Kumar

2017-06-28

Bacopa monnieri commonly known as Brahmi is utilized in Ayurveda to improve memory and many other human health benefits. Bacosides enriched standardized extract of Bacopa monnieri is being marketed as a memory enhancing agent. In spite of its well known pharmacological properties it is not much studied in terms of transcripts involved in biosynthetic pathway and its regulation that controls the secondary metabolic pathway in this plant. The aim of this study was to identify the potential transcripts and provide a framework of identified transcripts involved in bacosides production through transcriptome assembly. We performed comparative transcriptome analysis of shoot and root tissue of Bacopa monnieri in two independent biological replicate and obtained 22.48 million and 22.0 million high quality processed reads in shoot and root respectively. After de novo assembly and quantitative assessment total 26,412 genes got annotated in root and 18,500 genes annotated in shoot sample. Quality of raw reads was determined by using SeqQC-V2.2. Assembled sequences were annotated using BLASTX against public database such as NR or UniProt. Searching against the KEGG pathway database indicated that 37,918 unigenes from root and 35,130 unigenes from shoot were mapped to 133 KEGG pathways. Based on the DGE data we found that most of the transcript related to CYP450s and UDP-glucosyltransferases were specifically upregulated in shoot tissue as compared to root tissue. Finally, we have selected 43 transcripts related to secondary metabolism including transcription factor families which are differentially expressed in shoot and root tissues were validated by qRT-PCR and their expression level were monitored after MeJA treatment and wounding for 1, 3 and 5 h. This study not only represents the first de novo transcriptome analysis of Bacopa monnieri but also provides information about the identification, expression and differential tissues specific distribution of transcripts related to triterpenoid sapogenin which is one of the most important pharmacologically active secondary metabolite present in Bacopa monnieri. The identified transcripts in this study will establish a foundation for future studies related to carrying out the metabolic engineering for increasing the bacosides biosynthesis and its regulation for human health benefits.

EST-PAC a web package for EST annotation and protein sequence prediction

PubMed Central

Strahm, Yvan; Powell, David; Lefèvre, Christophe

2006-01-01

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics. PMID:17147782

Exercise-driven metabolic pathways in healthy cartilage.

PubMed

Blazek, A D; Nam, J; Gupta, R; Pradhan, M; Perera, P; Weisleder, N L; Hewett, T E; Chaudhari, A M; Lee, B S; Leblebicioglu, B; Butterfield, T A; Agarwal, S

2016-07-01

Exercise is vital for maintaining cartilage integrity in healthy joints. Here we examined the exercise-driven transcriptional regulation of genes in healthy rat articular cartilage to dissect the metabolic pathways responsible for the potential benefits of exercise. Transcriptome-wide gene expression in the articular cartilage of healthy Sprague-Dawley female rats exercised daily (low intensity treadmill walking) for 2, 5, or 15 days was compared to that of non-exercised rats, using Affymetrix GeneChip arrays. Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for Gene Ontology (GO)-term enrichment and Functional Annotation analysis of differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genome (KEGG) pathway mapper was used to identify the metabolic pathways regulated by exercise. Microarray analysis revealed that exercise-induced 644 DEGs in healthy articular cartilage. The DAVID bioinformatics tool demonstrated high prevalence of functional annotation clusters with greater enrichment scores and GO-terms associated with extracellular matrix (ECM) biosynthesis/remodeling and inflammation/immune response. The KEGG database revealed that exercise regulates 147 metabolic pathways representing molecular interaction networks for Metabolism, Genetic Information Processing, Environmental Information Processing, Cellular Processes, Organismal Systems, and Diseases. These pathways collectively supported the complex regulation of the beneficial effects of exercise on the cartilage. Overall, the findings highlight that exercise is a robust transcriptional regulator of a wide array of metabolic pathways in healthy cartilage. The major actions of exercise involve ECM biosynthesis/cartilage strengthening and attenuation of inflammatory pathways to provide prophylaxis against onset of arthritic diseases in healthy cartilage. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Database Constraints Applied to Metabolic Pathway Reconstruction Tools

PubMed Central

Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

2014-01-01

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes. PMID:25202745

Transcriptomic analysis identifies genes and pathways related to myrmecophagy in the Malayan pangolin (Manis javanica)

PubMed Central

Ma, Jing-E; Li, Lin-Miao; Jiang, Hai-Ying; Zhang, Xiu-Juan; Li, Juan; Li, Guan-Yu; Yuan, Li-Hong; Wu, Jun

2017-01-01

The Malayan pangolin (Manis javanica) is an unusual, scale-covered, toothless mammal that specializes in myrmecophagy. Due to their threatened status and continuing decline in the wild, concerted efforts have been made to conserve and rescue this species in captivity in China. Maintaining this species in captivity is a significant challenge, partly because little is known of the molecular mechanisms of its digestive system. Here, the first large-scale sequencing analyses of the salivary gland, liver and small intestine transcriptomes of an adult M. javanica genome were performed, and the results were compared with published liver transcriptome profiles for a pregnant M. javanica female. A total of 24,452 transcripts were obtained, among which 22,538 were annotated on the basis of seven databases. In addition, 3,373 new genes were predicted, of which 1,459 were annotated. Several pathways were found to be involved in myrmecophagy, including olfactory transduction, amino sugar and nucleotide sugar metabolism, lipid metabolism, and terpenoid and polyketide metabolism pathways. Many of the annotated transcripts were involved in digestive functions: 997 transcripts were related to sensory perception, 129 were related to digestive enzyme gene families, and 199 were related to molecular transporters. One transcript for an acidic mammalian chitinase was found in the annotated data, and this might be closely related to the unique digestive function of pangolins. These pathways and transcripts are involved in specialization processes related to myrmecophagy (a form of insectivory) and carbohydrate, protein and lipid digestive pathways, probably reflecting adaptations to myrmecophagy. Our study is the first to investigate the molecular mechanisms underlying myrmecophagy in M. javanica, and we hope that our results may play a role in the conservation of this species. PMID:29302388

Improved Genome Assembly and Annotation for the Rock Pigeon (Columba livia)

PubMed Central

Holt, Carson; Campbell, Michael; Keays, David A.; Edelman, Nathaniel; Kapusta, Aurélie; Maclary, Emily; T. Domyan, Eric; Suh, Alexander; Warren, Wesley C.; Yandell, Mark; Gilbert, M. Thomas P.; Shapiro, Michael D.

2018-01-01

The domestic rock pigeon (Columba livia) is among the most widely distributed and phenotypically diverse avian species. C. livia is broadly studied in ecology, genetics, physiology, behavior, and evolutionary biology, and has recently emerged as a model for understanding the molecular basis of anatomical diversity, the magnetic sense, and other key aspects of avian biology. Here we report an update to the C. livia genome reference assembly and gene annotation dataset. Greatly increased scaffold lengths in the updated reference assembly, along with an updated annotation set, provide improved tools for evolutionary and functional genetic studies of the pigeon, and for comparative avian genomics in general. PMID:29519939

Saint: a lightweight integration environment for model annotation.

PubMed

Lister, Allyson L; Pocock, Matthew; Taschuk, Morgan; Wipat, Anil

2009-11-15

Saint is a web application which provides a lightweight annotation integration environment for quantitative biological models. The system enables modellers to rapidly mark up models with biological information derived from a range of data sources. Saint is freely available for use on the web at http://www.cisban.ac.uk/saint. The web application is implemented in Google Web Toolkit and Tomcat, with all major browsers supported. The Java source code is freely available for download at http://saint-annotate.sourceforge.net. The Saint web server requires an installation of libSBML and has been tested on Linux (32-bit Ubuntu 8.10 and 9.04).

A new approach for annotation of transposable elements using small RNA mapping

PubMed Central

El Baidouri, Moaine; Kim, Kyung Do; Abernathy, Brian; Arikit, Siwaret; Maumus, Florian; Panaud, Olivier; Meyers, Blake C.; Jackson, Scott A.

2015-01-01

Transposable elements (TEs) are mobile genomic DNA sequences found in most organisms. They so densely populate the genomes of many eukaryotic species that they are often the major constituents. With the rapid generation of many plant genome sequencing projects over the past few decades, there is an urgent need for improved TE annotation as a prerequisite for genome-wide studies. Analogous to the use of RNA-seq for gene annotation, we propose a new method for de novo TE annotation that uses as a guide 24 nt-siRNAs that are a part of TE silencing pathways. We use this new approach, called TASR (for Transposon Annotation using Small RNAs), for de novo annotation of TEs in Arabidopsis, rice and soybean and demonstrate that this strategy can be successfully applied for de novo TE annotation in plants. Executable PERL is available for download from: http://tasr-pipeline.sourceforge.net/ PMID:25813049

BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3

PubMed Central

Su, Gang; Morris, John H.; Demchak, Barry; Bader, Gary D.

2014-01-01

Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and pathway construction. Cytoscape provides core functionality to load, visualize, search, filter and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the network, visualizing network associated data (attributes) and identifying clusters. It also highlights new features that benefit experienced users. PMID:25199793

A Rich-Club Organization in Brain Ischemia Protein Interaction Network

PubMed Central

Alawieh, Ali; Sabra, Zahraa; Sabra, Mohammed; Tomlinson, Stephen; Zaraket, Fadi A.

2015-01-01

Ischemic stroke involves multiple pathophysiological mechanisms with complex interactions. Efforts to decipher those mechanisms and understand the evolution of cerebral injury is key for developing successful interventions. In an innovative approach, we use literature mining, natural language processing and systems biology tools to construct, annotate and curate a brain ischemia interactome. The curated interactome includes proteins that are deregulated after cerebral ischemia in human and experimental stroke. Network analysis of the interactome revealed a rich-club organization indicating the presence of a densely interconnected hub structure of prominent contributors to disease pathogenesis. Functional annotation of the interactome uncovered prominent pathways and highlighted the critical role of the complement and coagulation cascade in the initiation and amplification of injury starting by activation of the rich-club. We performed an in-silico screen for putative interventions that have pleiotropic effects on rich-club components and we identified estrogen as a prominent candidate. Our findings show that complex network analysis of disease related interactomes may lead to a better understanding of pathogenic mechanisms and provide cost-effective and mechanism-based discovery of candidate therapeutics. PMID:26310627

The online Tabloid Proteome: an annotated database of protein associations

PubMed Central

Turan, Demet; Tavernier, Jan

2018-01-01

Abstract A complete knowledge of the proteome can only be attained by determining the associations between proteins, along with the nature of these associations (e.g. physical contact in protein–protein interactions, participation in complex formation or different roles in the same pathway). Despite extensive efforts in elucidating direct protein interactions, our knowledge on the complete spectrum of protein associations remains limited. We therefore developed a new approach that detects protein associations from identifications obtained after re-processing of large-scale, public mass spectrometry-based proteomics data. Our approach infers protein association based on the co-occurrence of proteins across many different proteomics experiments, and provides information that is almost completely complementary to traditional direct protein interaction studies. We here present a web interface to query and explore the associations derived from this method, called the online Tabloid Proteome. The online Tabloid Proteome also integrates biological knowledge from several existing resources to annotate our derived protein associations. The online Tabloid Proteome is freely available through a user-friendly web interface, which provides intuitive navigation and data exploration options for the user at http://iomics.ugent.be/tabloidproteome. PMID:29040688

Comparative Proteomic and Transcriptomic Analysis of Follistatin-Induced Skeletal Muscle Hypertrophy.

PubMed

Barbé, Caroline; Bray, Fabrice; Gueugneau, Marine; Devassine, Stéphanie; Lause, Pascale; Tokarski, Caroline; Rolando, Christian; Thissen, Jean-Paul

2017-10-06

Skeletal muscle, the most abundant body tissue, plays vital roles in locomotion and metabolism. Myostatin is a negative regulator of skeletal muscle mass. In addition to increasing muscle mass, Myostatin inhibition impacts muscle contractility and energy metabolism. To decipher the mechanisms of action of the Myostatin inhibitors, we used proteomic and transcriptomic approaches to investigate the changes induced in skeletal muscles of transgenic mice overexpressing Follistatin, a physiological Myostatin inhibitor. Our proteomic workflow included a fractionation step to identify weakly expressed proteins and a comparison of fast versus slow muscles. Functional annotation of altered proteins supports the phenotypic changes induced by Myostatin inhibition, including modifications in energy metabolism, fiber type, insulin and calcium signaling, as well as membrane repair and regeneration. Less than 10% of the differentially expressed proteins were found to be also regulated at the mRNA level but the Biological Process annotation, and the KEGG pathways analysis of transcriptomic results shows a great concordance with the proteomic data. Thus this study describes the most extensive omics analysis of muscle overexpressing Follistatin, providing molecular-level insights to explain the observed muscle phenotypic changes.

Progression Rate Associated Peripheral Blood Biomarkers of Parkinson's Disease.

PubMed

Fan, Yanxia; Xiao, Shuping

2018-06-23

Parkinson disease (PD) is one of the most frequent neurodegenerative disorders. The aim of this study was to identify blood biomarkers capable to discriminate PD patients with different progression rates. Differentially expressed genes (DEGs) were acquired by comparing the expression profiles of PD patients with rapid and slow progression rates, using an expression dataset from Gene Expression Omnibus (GEO) under accession code of GSE80599. Altered biological processes and pathways were revealed by functional annotation. Potential biomarkers of PD were identified by protein-protein interaction (PPI) network analysis. Critical transcription factors (TFs) and miRNAs regulating DEGs were predicted by TF analysis and miRNA analysis. A total of 225 DEGs were identified between PD patients with rapid and slow progression profiles. These genes were significantly enriched in biological processes and pathways related to fatty acid metabolism. Among these DEGs, ZFAND4, SRMS, UBL4B, PVALB, DIRAS1, PDP2, LRCH1, and MYL4 were potential progression rate associated biomarkers of PD. Additionally, these DEGs may be regulated by miRNAs of the miR-30 family and TFs STAT1 and GRHL3. Our results may contribute to our understanding of the molecular mechanisms underlying different PD progression profiles.

Transcriptome differences between enrofloxacin-resistant and enrofloxacin-susceptible strains of Aeromonas hydrophila.

PubMed

Zhu, Fengjiao; Yang, Zongying; Zhang, Yiliu; Hu, Kun; Fang, Wenhong

2017-01-01

Enrofloxacin is the most commonly used antibiotic to control diseases in aquatic animals caused by A. hydrophila. This study conducted de novo transcriptome sequencing and compared the global transcriptomes of enrofloxacin-resistant and enrofloxacin-susceptible strains. We got a total of 4,714 unigenes were assembled. Of these, 4,122 were annotated. A total of 3,280 unigenes were assigned to GO, 3,388 unigenes were classified into Cluster of Orthologous Groups of proteins (COG) using BLAST and BLAST2GO software, and 2,568 were mapped onto pathways using the Kyoto Encyclopedia of Gene and Genomes Pathway database. Furthermore, 218 unigenes were deemed to be DEGs. After enrofloxacin treatment, 135 genes were upregulated and 83 genes were downregulated. The GO terms biological process (126 genes) and metabolic process (136 genes) were the most enriched, and the terms for protein folding, response to stress, and SOS response were also significantly enriched. This study identified enrofloxacin treatment affects multiple biological functions of A. hydrophila. Enrofloxacin resistance in A. hydrophila is closely related to the reduction of intracellular drug accumulation caused by ABC transporters and increased expression of topoisomerase IV.

Transcriptome differences between enrofloxacin-resistant and enrofloxacin-susceptible strains of Aeromonas hydrophila

PubMed Central

Yang, Zongying; Zhang, Yiliu; Hu, Kun; Fang, Wenhong

2017-01-01

Enrofloxacin is the most commonly used antibiotic to control diseases in aquatic animals caused by A. hydrophila. This study conducted de novo transcriptome sequencing and compared the global transcriptomes of enrofloxacin-resistant and enrofloxacin-susceptible strains. We got a total of 4,714 unigenes were assembled. Of these, 4,122 were annotated. A total of 3,280 unigenes were assigned to GO, 3,388 unigenes were classified into Cluster of Orthologous Groups of proteins (COG) using BLAST and BLAST2GO software, and 2,568 were mapped onto pathways using the Kyoto Encyclopedia of Gene and Genomes Pathway database. Furthermore, 218 unigenes were deemed to be DEGs. After enrofloxacin treatment, 135 genes were upregulated and 83 genes were downregulated. The GO terms biological process (126 genes) and metabolic process (136 genes) were the most enriched, and the terms for protein folding, response to stress, and SOS response were also significantly enriched. This study identified enrofloxacin treatment affects multiple biological functions of A. hydrophila. Enrofloxacin resistance in A. hydrophila is closely related to the reduction of intracellular drug accumulation caused by ABC transporters and increased expression of topoisomerase IV. PMID:28708867

The aquatic animals' transcriptome resource for comparative functional analysis.

PubMed

Chou, Chih-Hung; Huang, Hsi-Yuan; Huang, Wei-Chih; Hsu, Sheng-Da; Hsiao, Chung-Der; Liu, Chia-Yu; Chen, Yu-Hung; Liu, Yu-Chen; Huang, Wei-Yun; Lee, Meng-Lin; Chen, Yi-Chang; Huang, Hsien-Da

2018-05-09

Aquatic animals have great economic and ecological importance. Among them, non-model organisms have been studied regarding eco-toxicity, stress biology, and environmental adaptation. Due to recent advances in next-generation sequencing techniques, large amounts of RNA-seq data for aquatic animals are publicly available. However, currently there is no comprehensive resource exist for the analysis, unification, and integration of these datasets. This study utilizes computational approaches to build a new resource of transcriptomic maps for aquatic animals. This aquatic animal transcriptome map database dbATM provides de novo assembly of transcriptome, gene annotation and comparative analysis of more than twenty aquatic organisms without draft genome. To improve the assembly quality, three computational tools (Trinity, Oases and SOAPdenovo-Trans) were employed to enhance individual transcriptome assembly, and CAP3 and CD-HIT-EST software were then used to merge these three assembled transcriptomes. In addition, functional annotation analysis provides valuable clues to gene characteristics, including full-length transcript coding regions, conserved domains, gene ontology and KEGG pathways. Furthermore, all aquatic animal genes are essential for comparative genomics tasks such as constructing homologous gene groups and blast databases and phylogenetic analysis. In conclusion, we establish a resource for non model organism aquatic animals, which is great economic and ecological importance and provide transcriptomic information including functional annotation and comparative transcriptome analysis. The database is now publically accessible through the URL http://dbATM.mbc.nctu.edu.tw/ .

Next-generation sequencing identifies equine cartilage and subchondral bone miRNAs and suggests their involvement in osteochondrosis physiopathology.

PubMed

Desjardin, Clémence; Vaiman, Anne; Mata, Xavier; Legendre, Rachel; Laubier, Johan; Kennedy, Sean P; Laloe, Denis; Barrey, Eric; Jacques, Claire; Cribiu, Edmond P; Schibler, Laurent

2014-09-17

MicroRNAs (miRNAs) are an abundant class of small single-stranded non-coding RNA molecules ranging from 18 to 24 nucleotides. They negatively regulate gene expression at the post-transcriptional level and play key roles in many biological processes, including skeletal development and cartilage maturation. In addition, miRNAs involvement in osteoarticular diseases has been proved and some of them were identified as suitable biomarkers for pathological conditions. Equine osteochondrosis (OC) is one of the most prevalent juvenile osteoarticular disorders in horses and represents a major concern for animal welfare and economic reasons. Its etiology and pathology remain controversial and biological pathways as well as molecular mechanisms involved in the physiopathology are still unclear. This study aims to investigate the potential role of miRNAs in equine osteochondrosis (OC) physiopathology.Short-read NGS technology (SOLID™, Life Technologies) was used to establish a comprehensive repertoire of miRNA expressed in either equine cartilage or subchondral bone. Undamaged cartilage and subchondral bone samples from healthy (healthy samples) and OC-affected (predisposed samples) 10-month Anglo-Arabian foals were analysed. Samples were also subjected or not to an experimental mechanical loading to evaluate the role of miRNAs in the regulation of mechano-transduction pathways. Predicted targets of annotated miRNAs were identified using miRmap. Epiphyseal cartilage and subchondral bone miRNome were defined, including about 300 new miRNAs. Differentially expressed miRNAs were identified between bone and cartilage from healthy and OC foals, as well as after an experimental mechanical loading. In cartilage, functional annotation of their predicted targets suggests a role in the maintenance of cartilage integrity through the control of cell cycle and differentiation, energy production and metabolism as well as extracellular matrix structure and dynamics. In bone, miRNA predicited targets were associated with osteoblasts and osteoclasts differentiation, though the regulation of energy production, vesicle transport and some growth factor signaling pathways. Taken together, our results suggest a role of miRNAs in equine OC physiopathology and in the cellular response to biomechanical stress in cartilage and bone. In silico target prediction and functional enrichment analysis provides new insight into OC molecular physiopathology.

Genome annotation in a community college cell biology lab.

PubMed

Beagley, C Timothy

2013-01-01

The Biology Department at Salt Lake Community College has used the IMG-ACT toolbox to introduce a genome mapping and annotation exercise into the laboratory portion of its Cell Biology course. This project provides students with an authentic inquiry-based learning experience while introducing them to computational biology and contemporary learning skills. Additionally, the project strengthens student understanding of the scientific method and contributes to student learning gains in curricular objectives centered around basic molecular biology, specifically, the Central Dogma. Importantly, inclusion of this project in the laboratory course provides students with a positive learning environment and allows for the use of cooperative learning strategies to increase overall student success. Copyright © 2012 International Union of Biochemistry and Molecular Biology, Inc.

Integrating systems biology models and biomedical ontologies

PubMed Central

2011-01-01

Background Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. Results We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. Conclusions We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms. PMID:21835028

SEED Servers: High-Performance Access to the SEED Genomes, Annotations, and Metabolic Models

PubMed Central

Aziz, Ramy K.; Devoid, Scott; Disz, Terrence; Edwards, Robert A.; Henry, Christopher S.; Olsen, Gary J.; Olson, Robert; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Stevens, Rick L.; Vonstein, Veronika; Xia, Fangfang

2012-01-01

The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers (http://www.theseed.org/servers): four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users. PMID:23110173

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

Introducing meta-services for biomedical information extraction

PubMed Central

Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso

2008-01-01

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497

De novo assembly and characterization of the transcriptome in the desiccation-tolerant moss Syntrichia caninervis

PubMed Central

2014-01-01

Background Syntrichia caninervis is a desiccation-tolerant moss and the dominant bryophyte of the Biological Soil Crusts (BSCs) found in the Mojave and Gurbantunggut deserts. Next generation high throughput sequencing technologies offer an efficient and economic choice for characterizing non-model organism transcriptomes with little or no prior molecular information available. Results In this study, we employed next generation, high-throughput, Illumina RNA-Seq to analyze the poly-(A) + mRNA from hydrated, dehydrating and desiccated S. caninervis gametophores. Approximately 58.0 million paired-end short reads were obtained and 92,240 unigenes were assembled with an average size of 493 bp, N50 value of 662 bp and a total size of 45.48 Mbp. Sequence similarity searches against five public databases (NR, Swiss-Prot, COSMOSS, KEGG and COG) found 54,125 unigenes (58.7%) with significant similarity to an existing sequence (E-value ≤ 1e-5) and could be annotated. Gene Ontology (GO) annotation assigned 24,183 unigenes to the three GO terms: Biological Process, Cellular Component or Molecular Function. GO comparison between P. patens and S. caninervis demonstrated similar sequence enrichment across all three GO categories. 29,370 deduced polypeptide sequences were assigned Pfam domain information and categorized into 4,212 Pfam domains/families. Using the PlantTFDB, 778 unigenes were predicted to be involved in the regulation of transcription and were classified into 49 transcription factor families. Annotated unigenes were mapped to the KEGG pathways and further annotated using MapMan. Comparative genomics revealed that 44% of protein families are shared in common by S. caninervis, P. patens and Arabidopsis thaliana and that 80% are shared by both moss species. Conclusions This study is one of the first comprehensive transcriptome analyses of the moss S. caninervis. Our data extends our knowledge of bryophyte transcriptomes, provides an insight to plants adapted to the arid regions of central Asia, and continues the development of S. caninervis as a model for understanding the molecular aspects of desiccation-tolerance. PMID:25086984

LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data.

PubMed

Koelmel, Jeremy P; Kroeger, Nicholas M; Ulmer, Candice Z; Bowden, John A; Patterson, Rainey E; Cochran, Jason A; Beecher, Christopher W W; Garrett, Timothy J; Yost, Richard A

2017-07-10

Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications.

PubMed

Otero, José Manuel; Vongsangnak, Wanwipa; Asadollahi, Mohammad A; Olivares-Hernandes, Roberto; Maury, Jérôme; Farinelli, Laurent; Barlocher, Loïc; Osterås, Magne; Schalk, Michel; Clark, Anthony; Nielsen, Jens

2010-12-22

The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications

PubMed Central

2010-01-01

Background The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. Results In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. Conclusions With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk. PMID:21176163

A geographically-diverse collection of 418 human gut microbiome pathway genome databases

PubMed Central

Hahn, Aria S.; Altman, Tomer; Konwar, Kishori M.; Hanson, Niels W.; Kim, Dongjae; Relman, David A.; Dill, David L.; Hallam, Steven J.

2017-01-01

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GutCyc, a compendium of environmental pathway genome databases (ePGDBs) constructed from 418 assembled human microbiome datasets using MetaPathways, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the Pathway Tools software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GutCyc provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GutCyc data products are searchable online, or may be downloaded and explored locally using MetaPathways and Pathway Tools. PMID:28398290

PubSearch and PubFetch: a simple management system for semiautomated retrieval and annotation of biological information from the literature.

PubMed

Yoo, Danny; Xu, Iris; Berardini, Tanya Z; Rhee, Seung Yon; Narayanasamy, Vijay; Twigger, Simon

2006-03-01

For most systems in biology, a large body of literature exists that describes the complexity of the system based on experimental results. Manual review of this literature to extract targeted information into biological databases is difficult and time consuming. To address this problem, we developed PubSearch and PubFetch, which store literature, keyword, and gene information in a relational database, index the literature with keywords and gene names, and provide a Web user interface for annotating the genes from experimental data found in the associated literature. A set of protocols is provided in this unit for installing, populating, running, and using PubSearch and PubFetch. In addition, we provide support protocols for performing controlled vocabulary annotations. Intended users of PubSearch and PubFetch are database curators and biology researchers interested in tracking the literature and capturing information about genes of interest in a more effective way than with conventional spreadsheets and lab notebooks.

Dizeez: An Online Game for Human Gene-Disease Annotation

PubMed Central

Loguercio, Salvatore; Good, Benjamin M.; Su, Andrew I.

2013-01-01

Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org. PMID:23951102

RNA-Seq-based transcriptome analysis of dormant flower buds of Chinese cherry (Prunus pseudocerasus).

PubMed

Zhu, Youyin; Li, Yongqiang; Xin, Dedong; Chen, Wenrong; Shao, Xu; Wang, Yue; Guo, Weidong

2015-01-25

Bud dormancy is a critical biological process allowing Chinese cherry (Prunus pseudocerasus) to survive in winter. Due to the lake of genomic information, molecular mechanisms triggering endodormancy release in flower buds have remained unclear. Hence, we used Illumina RNA-Seq technology to carry out de novo transcriptome assembly and digital gene expression profiling of flower buds. Approximately 47million clean reads were assembled into 50,604 sequences with an average length of 837bp. A total of 37,650 unigene sequences were successfully annotated. 128 pathways were annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and metabolic, biosynthesis of second metabolite and plant hormone signal transduction accounted for higher percentage in flower bud. In critical period of endodormancy release, 1644, significantly differentially expressed genes (DEGs) were identified from expression profile. DEGs related to oxidoreductase activity were especially abundant in Gene Ontology (GO) molecular function category. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated that DEGs were involved in various metabolic processes, including phytohormone metabolism. Quantitative real-time PCR (qRT-PCR) analysis indicated that levels of DEGs for abscisic acid and gibberellin biosynthesis decreased while the abundance of DEGs encoding their degradation enzymes increased and GID1 was down-regulated. Concomitant with endodormancy release, MADS-box transcription factors including P. pseudocerasus dormancy-associated MADS-box (PpcDAM), Agamous-like2, and APETALA3-like genes, shown remarkably epigenetic roles. The newly generated transcriptome and gene expression profiling data provide valuable genetic information for revealing transcriptomic variation during bud dormancy in Chinese cherry. The uncovered data should be useful for future studies of bud dormancy in Prunus fruit trees lacking genomic information. Copyright © 2014 Elsevier B.V. All rights reserved.

Nuclear Receptor Signaling Atlas: Opening Access to the Biology of Nuclear Receptor Signaling Pathways

PubMed Central

Becnel, Lauren B.; Darlington, Yolanda F.; Ochsner, Scott A.; Easton-Marks, Jeremy R.; Watkins, Christopher M.; McOwiti, Apollo; Kankanamge, Wasula H.; Wise, Michael W.; DeHart, Michael; Margolis, Ronald N.; McKenna, Neil J.

2015-01-01

Signaling pathways involving nuclear receptors (NRs), their ligands and coregulators, regulate tissue-specific transcriptomes in diverse processes, including development, metabolism, reproduction, the immune response and neuronal function, as well as in their associated pathologies. The Nuclear Receptor Signaling Atlas (NURSA) is a Consortium focused around a Hub website (www.nursa.org) that annotates and integrates diverse ‘omics datasets originating from the published literature and NURSA-funded Data Source Projects (NDSPs). These datasets are then exposed to the scientific community on an Open Access basis through user-friendly data browsing and search interfaces. Here, we describe the redesign of the Hub, version 3.0, to deploy “Web 2.0” technologies and add richer, more diverse content. The Molecule Pages, which aggregate information relevant to NR signaling pathways from myriad external databases, have been enhanced to include resources for basic scientists, such as post-translational modification sites and targeting miRNAs, and for clinicians, such as clinical trials. A portal to NURSA’s Open Access, PubMed-indexed journal Nuclear Receptor Signaling has been added to facilitate manuscript submissions. Datasets and information on reagents generated by NDSPs are available, as is information concerning periodic new NDSP funding solicitations. Finally, the new website integrates the Transcriptomine analysis tool, which allows for mining of millions of richly annotated public transcriptomic data points in the field, providing an environment for dataset re-use and citation, bench data validation and hypothesis generation. We anticipate that this new release of the NURSA database will have tangible, long term benefits for both basic and clinical research in this field. PMID:26325041

PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

PubMed

Kaplan, Noam; Vaaknin, Avishay; Linial, Michal

2003-10-01

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks.

PubMed

Blatti, Charles; Sinha, Saurabh

2016-07-15

Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR. blatti@illinois.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Genetics of rheumatoid arthritis contributes to biology and drug discovery.

PubMed

Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, Akari; Yoshida, Shinji; Graham, Robert R; Manoharan, Arun; Ortmann, Ward; Bhangale, Tushar; Denny, Joshua C; Carroll, Robert J; Eyler, Anne E; Greenberg, Jeffrey D; Kremer, Joel M; Pappas, Dimitrios A; Jiang, Lei; Yin, Jian; Ye, Lingying; Su, Ding-Feng; Yang, Jian; Xie, Gang; Keystone, Ed; Westra, Harm-Jan; Esko, Tõnu; Metspalu, Andres; Zhou, Xuezhong; Gupta, Namrata; Mirel, Daniel; Stahl, Eli A; Diogo, Dorothée; Cui, Jing; Liao, Katherine; Guo, Michael H; Myouzen, Keiko; Kawaguchi, Takahisa; Coenen, Marieke J H; van Riel, Piet L C M; van de Laar, Mart A F J; Guchelaar, Henk-Jan; Huizinga, Tom W J; Dieudé, Philippe; Mariette, Xavier; Bridges, S Louis; Zhernakova, Alexandra; Toes, Rene E M; Tak, Paul P; Miceli-Richard, Corinne; Bang, So-Young; Lee, Hye-Soon; Martin, Javier; Gonzalez-Gay, Miguel A; Rodriguez-Rodriguez, Luis; Rantapää-Dahlqvist, Solbritt; Arlestig, Lisbeth; Choi, Hyon K; Kamatani, Yoichiro; Galan, Pilar; Lathrop, Mark; Eyre, Steve; Bowes, John; Barton, Anne; de Vries, Niek; Moreland, Larry W; Criswell, Lindsey A; Karlson, Elizabeth W; Taniguchi, Atsuo; Yamada, Ryo; Kubo, Michiaki; Liu, Jun S; Bae, Sang-Cheol; Worthington, Jane; Padyukov, Leonid; Klareskog, Lars; Gregersen, Peter K; Raychaudhuri, Soumya; Stranger, Barbara E; De Jager, Philip L; Franke, Lude; Visscher, Peter M; Brown, Matthew A; Yamanaka, Hisashi; Mimori, Tsuneyo; Takahashi, Atsushi; Xu, Huji; Behrens, Timothy W; Siminovitch, Katherine A; Momohara, Shigeki; Matsuda, Fumihiko; Yamamoto, Kazuhiko; Plenge, Robert M

2014-02-20

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Gene Ontology annotations at SGD: new data sources and annotation methods

PubMed Central

Hong, Eurie L.; Balakrishnan, Rama; Dong, Qing; Christie, Karen R.; Park, Julie; Binkley, Gail; Costanzo, Maria C.; Dwight, Selina S.; Engel, Stacia R.; Fisk, Dianna G.; Hirschman, Jodi E.; Hitz, Benjamin C.; Krieger, Cynthia J.; Livstone, Michael S.; Miyasato, Stuart R.; Nash, Robert S.; Oughtred, Rose; Skrzypek, Marek S.; Weng, Shuai; Wong, Edith D.; Zhu, Kathy K.; Dolinski, Kara; Botstein, David; Cherry, J. Michael

2008-01-01

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current. PMID:17982175

Making adjustments to event annotations for improved biological event extraction.

PubMed

Baek, Seung-Cheol; Park, Jong C

2016-09-16

Current state-of-the-art approaches to biological event extraction train statistical models in a supervised manner on corpora annotated with event triggers and event-argument relations. Inspecting such corpora, we observe that there is ambiguity in the span of event triggers (e.g., "transcriptional activity" vs. 'transcriptional'), leading to inconsistencies across event trigger annotations. Such inconsistencies make it quite likely that similar phrases are annotated with different spans of event triggers, suggesting the possibility that a statistical learning algorithm misses an opportunity for generalizing from such event triggers. We anticipate that adjustments to the span of event triggers to reduce these inconsistencies would meaningfully improve the present performance of event extraction systems. In this study, we look into this possibility with the corpora provided by the 2009 BioNLP shared task as a proof of concept. We propose an Informed Expectation-Maximization (EM) algorithm, which trains models using the EM algorithm with a posterior regularization technique, which consults the gold-standard event trigger annotations in a form of constraints. We further propose four constraints on the possible event trigger annotations to be explored by the EM algorithm. The algorithm is shown to outperform the state-of-the-art algorithm on the development corpus in a statistically significant manner and on the test corpus by a narrow margin. The analysis of the annotations generated by the algorithm shows that there are various types of ambiguity in event annotations, even though they could be small in number.

Improved Genome Assembly and Annotation for the Rock Pigeon (Columba livia).

PubMed

Holt, Carson; Campbell, Michael; Keays, David A; Edelman, Nathaniel; Kapusta, Aurélie; Maclary, Emily; T Domyan, Eric; Suh, Alexander; Warren, Wesley C; Yandell, Mark; Gilbert, M Thomas P; Shapiro, Michael D

2018-05-04

The domestic rock pigeon ( Columba livia ) is among the most widely distributed and phenotypically diverse avian species. C. livia is broadly studied in ecology, genetics, physiology, behavior, and evolutionary biology, and has recently emerged as a model for understanding the molecular basis of anatomical diversity, the magnetic sense, and other key aspects of avian biology. Here we report an update to the C. livia genome reference assembly and gene annotation dataset. Greatly increased scaffold lengths in the updated reference assembly, along with an updated annotation set, provide improved tools for evolutionary and functional genetic studies of the pigeon, and for comparative avian genomics in general. Copyright © 2018 Holt et al.

LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures.

PubMed

Ryan, Michael; Diekhans, Mark; Lien, Stephanie; Liu, Yun; Karchin, Rachel

2009-06-01

LS-SNP/PDB is a new WWW resource for genome-wide annotation of human non-synonymous (amino acid changing) SNPs. It serves high-quality protein graphics rendered with UCSF Chimera molecular visualization software. The system is kept up-to-date by an automated, high-throughput build pipeline that systematically maps human nsSNPs onto Protein Data Bank structures and annotates several biologically relevant features. LS-SNP/PDB is available at (http://ls-snp.icm.jhu.edu/ls-snp-pdb) and via links from protein data bank (PDB) biology and chemistry tabs, UCSC Genome Browser Gene Details and SNP Details pages and PharmGKB Gene Variants Downloads/Cross-References pages.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

PubMed

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

De Novo Transcriptomic Analysis of Peripheral Blood Lymphocytes from the Chinese Goose: Gene Discovery and Immune System Pathway Description

PubMed Central

Tariq, Mansoor; Chen, Rong; Yuan, Hongyu; Liu, Yanjie; Wu, Yanan; Wang, Junya; Xia, Chun

2015-01-01

Background The Chinese goose is one of the most economically important poultry birds and is a natural reservoir for many avian viruses. However, the nature and regulation of the innate and adaptive immune systems of this waterfowl species are not completely understood due to limited information on the goose genome. Recently, transcriptome sequencing technology was applied in the genomic studies focused on novel gene discovery. Thus, this study described the transcriptome of the goose peripheral blood lymphocytes to identify immunity relevant genes. Principal Findings De novo transcriptome assembly of the goose peripheral blood lymphocytes was sequenced by Illumina-Solexa technology. In total, 211,198 unigenes were assembled from the 69.36 million cleaned reads. The average length, N50 size and the maximum length of the assembled unigenes were 687 bp, 1,298 bp and 18,992 bp, respectively. A total of 36,854 unigenes showed similarity by BLAST search against the NCBI non-redundant (Nr) protein database. For functional classification, 163,161 unigenes were comprised of three Gene Ontology (Go) categories and 67 subcategories. A total of 15,334 unigenes were annotated into 25 eukaryotic orthologous groups (KOGs) categories. Kyoto Encyclopedia of Genes and Genomes (KEGG) database annotated 39,585 unigenes into six biological functional groups and 308 pathways. Among the 2,757 unigenes that participated in the 15 immune system KEGG pathways, 125 of the most important immune relevant genes were summarized and analyzed by STRING analysis to identify gene interactions and relationships. Moreover, 10 genes were confirmed by PCR and analyzed. Of these 125 unigenes, 109 unigenes, approximately 87%, were not previously identified in the goose. Conclusion This de novo transcriptome analysis could provide important Chinese goose sequence information and highlights the value of new gene discovery, pathways investigation and immune system gene identification, and comparison with other avian species as useful tools to understand the goose immune system. PMID:25816068

De Novo Assembly of Mud Loach (Misgurnus anguillicaudatus) Skin Transcriptome to Identify Putative Genes Involved in Immunity and Epidermal Mucus Secretion

PubMed Central

Long, Yong; Li, Qing; Zhou, Bolan; Song, Guili; Li, Tao; Cui, Zongbin

2013-01-01

Fish skin serves as the first line of defense against a wide variety of chemical, physical and biological stressors. Secretion of mucus is among the most prominent characteristics of fish skin and numerous innate immune factors have been identified in the epidermal mucus. However, molecular mechanisms underlying the mucus secretion and immune activities of fish skin remain largely unclear due to the lack of genomic and transcriptomic data for most economically important fish species. In this study, we characterized the skin transcriptome of mud loach using Illumia paired-end sequencing. A total of 40364 unigenes were assembled from 86.6 million (3.07 gigabases) filtered reads. The mean length, N50 size and maximum length of assembled transcripts were 387, 611 and 8670 bp, respectively. A total of 17336 (43.76%) unigenes were annotated by blast searches against the NCBI non-redundant protein database. Gene ontology mapping assigned a total of 108513 GO terms to 15369 (38.08%) unigenes. KEGG orthology mapping annotated 9337 (23.23%) unigenes. Among the identified KO categories, immune system is the largest category that contains various components of multiple immune pathways such as chemokine signaling, leukocyte transendothelial migration and T cell receptor signaling, suggesting the complexity of immune mechanisms in fish skin. As for mucin biosynthesis, 37 unigenes were mapped to 7 enzymes of the mucin type O-glycan biosynthesis pathway and 8 members of the polypeptide N-acetylgalactosaminyltransferase family were identified. Additionally, 38 unigenes were mapped to 23 factors of the SNARE interactions in vesicular transport pathway, indicating that the activity of this pathway is required for the processes of epidermal mucus storage and release. Moreover, 1754 simple sequence repeats (SSRs) were detected in 1564 unigenes and dinucleotide repeats represented the most abundant type. These findings have laid the foundation for further understanding the secretary processes and immune functions of loach skin mucus. PMID:23437293

De novo assembly of mud loach (Misgurnus anguillicaudatus) skin transcriptome to identify putative genes involved in immunity and epidermal mucus secretion.

PubMed

Long, Yong; Li, Qing; Zhou, Bolan; Song, Guili; Li, Tao; Cui, Zongbin

2013-01-01

Fish skin serves as the first line of defense against a wide variety of chemical, physical and biological stressors. Secretion of mucus is among the most prominent characteristics of fish skin and numerous innate immune factors have been identified in the epidermal mucus. However, molecular mechanisms underlying the mucus secretion and immune activities of fish skin remain largely unclear due to the lack of genomic and transcriptomic data for most economically important fish species. In this study, we characterized the skin transcriptome of mud loach using Illumia paired-end sequencing. A total of 40364 unigenes were assembled from 86.6 million (3.07 gigabases) filtered reads. The mean length, N50 size and maximum length of assembled transcripts were 387, 611 and 8670 bp, respectively. A total of 17336 (43.76%) unigenes were annotated by blast searches against the NCBI non-redundant protein database. Gene ontology mapping assigned a total of 108513 GO terms to 15369 (38.08%) unigenes. KEGG orthology mapping annotated 9337 (23.23%) unigenes. Among the identified KO categories, immune system is the largest category that contains various components of multiple immune pathways such as chemokine signaling, leukocyte transendothelial migration and T cell receptor signaling, suggesting the complexity of immune mechanisms in fish skin. As for mucin biosynthesis, 37 unigenes were mapped to 7 enzymes of the mucin type O-glycan biosynthesis pathway and 8 members of the polypeptide N-acetylgalactosaminyltransferase family were identified. Additionally, 38 unigenes were mapped to 23 factors of the SNARE interactions in vesicular transport pathway, indicating that the activity of this pathway is required for the processes of epidermal mucus storage and release. Moreover, 1754 simple sequence repeats (SSRs) were detected in 1564 unigenes and dinucleotide repeats represented the most abundant type. These findings have laid the foundation for further understanding the secretary processes and immune functions of loach skin mucus.

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

PubMed

Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

2012-07-15

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

Functional annotation of chemical libraries across diverse biological processes.

PubMed

Piotrowski, Jeff S; Li, Sheena C; Deshpande, Raamesh; Simpkins, Scott W; Nelson, Justin; Yashiroda, Yoko; Barber, Jacqueline M; Safizadeh, Hamid; Wilson, Erin; Okada, Hiroki; Gebre, Abraham A; Kubo, Karen; Torres, Nikko P; LeBlanc, Marissa A; Andrusiak, Kerry; Okamoto, Reika; Yoshimura, Mami; DeRango-Adem, Eva; van Leeuwen, Jolanda; Shirahige, Katsuhiko; Baryshnikova, Anastasia; Brown, Grant W; Hirano, Hiroyuki; Costanzo, Michael; Andrews, Brenda; Ohya, Yoshikazu; Osada, Hiroyuki; Yoshida, Minoru; Myers, Chad L; Boone, Charles

2017-09-01

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

High precision multi-genome scale reannotation of enzyme function by EFICAz

PubMed Central

Arakaki, Adrian K; Tian, Weidong; Skolnick, Jeffrey

2006-01-01

Background The functional annotation of most genes in newly sequenced genomes is inferred from similarity to previously characterized sequences, an annotation strategy that often leads to erroneous assignments. We have performed a reannotation of 245 genomes using an updated version of EFICAz, a highly precise method for enzyme function prediction. Results Based on our three-field EC number predictions, we have obtained lower-bound estimates for the average enzyme content in Archaea (29%), Bacteria (30%) and Eukarya (18%). Most annotations added in KEGG from 2005 to 2006 agree with EFICAz predictions made in 2005. The coverage of EFICAz predictions is significantly higher than that of KEGG, especially for eukaryotes. Thousands of our novel predictions correspond to hypothetical proteins. We have identified a subset of 64 hypothetical proteins with low sequence identity to EFICAz training enzymes, whose biochemical functions have been recently characterized and find that in 96% (84%) of the cases we correctly identified their three-field (four-field) EC numbers. For two of the 64 hypothetical proteins: PA1167 from Pseudomonas aeruginosa, an alginate lyase (EC 4.2.2.3) and Rv1700 of Mycobacterium tuberculosis H37Rv, an ADP-ribose diphosphatase (EC 3.6.1.13), we have detected annotation lag of more than two years in databases. Two examples are presented where EFICAz predictions act as hypothesis generators for understanding the functional roles of hypothetical proteins: FLJ11151, a human protein overexpressed in cancer that EFICAz identifies as an endopolyphosphatase (EC 3.6.1.10), and MW0119, a protein of Staphylococcus aureus strain MW2 that we propose as candidate virulence factor based on its EFICAz predicted activity, sphingomyelin phosphodiesterase (EC 3.1.4.12). Conclusion Our results suggest that we have generated enzyme function annotations of high precision and recall. These predictions can be mined and correlated with other information sources to generate biologically significant hypotheses and can be useful for comparative genome analysis and automated metabolic pathway reconstruction. PMID:17166279

Evidence-Based Annotation of Gene Function in Shewanella oneidensis MR-1 Using Genome-Wide Fitness Profiling across 121 Conditions

PubMed Central

Deutschbauer, Adam; Price, Morgan N.; Wetmore, Kelly M.; Shao, Wenjun; Baumohl, Jason K.; Xu, Zhuchen; Nguyen, Michelle; Tamse, Raquel; Davis, Ronald W.; Arkin, Adam P.

2011-01-01

Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes. PMID:22125499

Evolutionary interrogation of human biology in well-annotated genomic framework of rhesus macaque.

PubMed

Zhang, Shi-Jian; Liu, Chu-Jun; Yu, Peng; Zhong, Xiaoming; Chen, Jia-Yu; Yang, Xinzhuang; Peng, Jiguang; Yan, Shouyu; Wang, Chenqu; Zhu, Xiaotong; Xiong, Jingwei; Zhang, Yong E; Tan, Bertrand Chin-Ming; Li, Chuan-Yun

2014-05-01

With genome sequence and composition highly analogous to human, rhesus macaque represents a unique reference for evolutionary studies of human biology. Here, we developed a comprehensive genomic framework of rhesus macaque, the RhesusBase2, for evolutionary interrogation of human genes and the associated regulations. A total of 1,667 next-generation sequencing (NGS) data sets were processed, integrated, and evaluated, generating 51.2 million new functional annotation records. With extensive NGS annotations, RhesusBase2 refined the fine-scale structures in 30% of the macaque Ensembl transcripts, reporting an accurate, up-to-date set of macaque gene models. On the basis of these annotations and accurate macaque gene models, we further developed an NGS-oriented Molecular Evolution Gateway to access and visualize macaque annotations in reference to human orthologous genes and associated regulations (www.rhesusbase.org/molEvo). We highlighted the application of this well-annotated genomic framework in generating hypothetical link of human-biased regulations to human-specific traits, by using mechanistic characterization of the DIEXF gene as an example that provides novel clues to the understanding of digestive system reduction in human evolution. On a global scale, we also identified a catalog of 9,295 human-biased regulatory events, which may represent novel elements that have a substantial impact on shaping human transcriptome and possibly underpin recent human phenotypic evolution. Taken together, we provide an NGS data-driven, information-rich framework that will broadly benefit genomics research in general and serves as an important resource for in-depth evolutionary studies of human biology.

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

PubMed Central

Thomas, Reuben; Thomas, Russell S.; Auerbach, Scott S.; Portier, Christopher J.

2013-01-01

Background Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. Objectives To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Methods Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Results Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Conclusions Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species. PMID:23737943

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species.

PubMed

Thomas, Reuben; Thomas, Russell S; Auerbach, Scott S; Portier, Christopher J

2013-01-01

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Biodegradation of DDT by Stenotrophomonas sp. DDT-1: Characterization and genome functional analysis

PubMed Central

Pan, Xiong; Lin, Dunli; Zheng, Yuan; Zhang, Qian; Yin, Yuanming; Cai, Lin; Fang, Hua; Yu, Yunlong

2016-01-01

A novel bacterium capable of utilizing 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) as the sole carbon and energy source was isolated from a contaminated soil which was identified as Stenotrophomonas sp. DDT-1 based on morphological characteristics, BIOLOG GN2 microplate profile, and 16S rDNA phylogeny. Genome sequencing and functional annotation of the isolate DDT-1 showed a 4,514,569 bp genome size, 66.92% GC content, 4,033 protein-coding genes, and 76 RNA genes including 8 rRNA genes. Totally, 2,807 protein-coding genes were assigned to Clusters of Orthologous Groups (COGs), and 1,601 protein-coding genes were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The degradation half-lives of DDT increased with substrate concentration from 0.1 to 10.0 mg/l, whereas decreased with temperature from 15 °C to 35 °C. Neutral condition was the most favorable for DDT biodegradation. Based on genome annotation of DDT degradation genes and the metabolites detected by GC-MS, a mineralization pathway was proposed for DDT biodegradation in which it was orderly converted into DDE/DDD, DDMU, DDOH, and DDA via dechlorination, hydroxylation, and carboxylation, and ultimately mineralized to carbon dioxide. The results indicate that the isolate DDT-1 is a promising bacterial resource for the removal or detoxification of DDT residues in the environment. PMID:26888254

Sequencing, Annotation and Analysis of the Syrian Hamster (Mesocricetus auratus) Transcriptome

PubMed Central

Tchitchek, Nicolas; Safronetz, David; Rasmussen, Angela L.; Martens, Craig; Virtaneva, Kimmo; Porcella, Stephen F.; Feldmann, Heinz

2014-01-01

Background The Syrian hamster (golden hamster, Mesocricetus auratus) is gaining importance as a new experimental animal model for multiple pathogens, including emerging zoonotic diseases such as Ebola. Nevertheless there are currently no publicly available transcriptome reference sequences or genome for this species. Results A cDNA library derived from mRNA and snRNA isolated and pooled from the brains, lungs, spleens, kidneys, livers, and hearts of three adult female Syrian hamsters was sequenced. Sequence reads were assembled into 62,482 contigs and 111,796 reads remained unassembled (singletons). This combined contig/singleton dataset, designated as the Syrian hamster transcriptome, represents a total of 60,117,204 nucleotides. Our Mesocricetus auratus Syrian hamster transcriptome mapped to 11,648 mouse transcripts representing 9,562 distinct genes, and mapped to a similar number of transcripts and genes in the rat. We identified 214 quasi-complete transcripts based on mouse annotations. Canonical pathways involved in a broad spectrum of fundamental biological processes were significantly represented in the library. The Syrian hamster transcriptome was aligned to the current release of the Chinese hamster ovary (CHO) cell transcriptome and genome to improve the genomic annotation of this species. Finally, our Syrian hamster transcriptome was aligned against 14 other rodents, primate and laurasiatheria species to gain insights about the genetic relatedness and placement of this species. Conclusions This Syrian hamster transcriptome dataset significantly improves our knowledge of the Syrian hamster's transcriptome, especially towards its future use in infectious disease research. Moreover, this library is an important resource for the wider scientific community to help improve genome annotation of the Syrian hamster and other closely related species. Furthermore, these data provide the basis for development of expression microarrays that can be used in functional genomics studies. PMID:25398096

Incidence of genome structure, DNA asymmetry, and cell physiology on T-DNA integration in chromosomes of the phytopathogenic fungus Leptosphaeria maculans.

PubMed

Bourras, Salim; Meyer, Michel; Grandaubert, Jonathan; Lapalu, Nicolas; Fudal, Isabelle; Linglin, Juliette; Ollivier, Benedicte; Blaise, Françoise; Balesdent, Marie-Hélène; Rouxel, Thierry

2012-08-01

The ever-increasing generation of sequence data is accompanied by unsatisfactory functional annotation, and complex genomes, such as those of plants and filamentous fungi, show a large number of genes with no predicted or known function. For functional annotation of unknown or hypothetical genes, the production of collections of mutants using Agrobacterium tumefaciens-mediated transformation (ATMT) associated with genotyping and phenotyping has gained wide acceptance. ATMT is also widely used to identify pathogenicity determinants in pathogenic fungi. A systematic analysis of T-DNA borders was performed in an ATMT-mutagenized collection of the phytopathogenic fungus Leptosphaeria maculans to evaluate the features of T-DNA integration in its particular transposable element-rich compartmentalized genome. A total of 318 T-DNA tags were recovered and analyzed for biases in chromosome and genic compartments, existence of CG/AT skews at the insertion site, and occurrence of microhomologies between the T-DNA left border (LB) and the target sequence. Functional annotation of targeted genes was done using the Gene Ontology annotation. The T-DNA integration mainly targeted gene-rich, transcriptionally active regions, and it favored biological processes consistent with the physiological status of a germinating spore. T-DNA integration was strongly biased toward regulatory regions, and mainly promoters. Consistent with the T-DNA intranuclear-targeting model, the density of T-DNA insertion correlated with CG skew near the transcription initiation site. The existence of microhomologies between promoter sequences and the T-DNA LB flanking sequence was also consistent with T-DNA integration to host DNA mediated by homologous recombination based on the microhomology-mediated end-joining pathway.

De novo transcriptomic analysis and development of EST-SSRs for Sorbus pohuashanensis (Hance) Hedl.

PubMed Central

Guan, Xuelian; Fu, Qiang; Zhang, Ze; Hu, Zenghui; Zheng, Jian; Lu, Yizeng; Li, Wei

2017-01-01

Sorbus pohuashanensis is a native tree species of northern China that is used for a variety of ecological purposes. The species is often grown as an ornamental landscape tree because of its beautiful form, silver flowers in early summer, attractive pinnate leaves in summer, and red leaves and fruits in autumn. However, development and further utilization of the species are hindered by the lack of comprehensive genetic information, which impedes research into its genetics and molecular biology. Recent advances in de novo transcriptome sequencing (RNA-seq) technology have provided an effective means to obtain genomic information from non-model species. Here, we applied RNA-seq for sequencing S. pohuashanensis leaves and obtained a total of 137,506 clean reads. After assembly, 96,213 unigenes with an average length of 770 bp were obtained. We found that 64.5% of the unigenes could be annotated using bioinformatics tools to analyze gene function and alignment with the NCBI database. Overall, 59,089 unigenes were annotated using the Nr database(non-redundant protein database), 35,225 unigenes were annotated using the GO (Gene Ontology categories) database, and 33,168 unigenes were annotated using COG (Cluster of Orthologous Groups). Analysis of the unigenes using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database indicated that 13,953 unigenes were involved in 322 metabolic pathways. Finally, simple sequence repeat (SSR) site detection identified 6,604 unigenes that included EST-SSRs and a total of 7,473 EST-SSRs in the unigene sequences. Fifteen polymorphic SSRs were screened and found to be of use for future genetic research. These unigene sequences will provide important genetic resources for genetic improvement and investigation of biochemical processes in S. pohuashanensis. PMID:28614366

An integrative approach to inferring biologically meaningful gene modules.

PubMed

Cho, Ji-Hoon; Wang, Kai; Galas, David J

2011-07-26

The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO) annotation in construction of gene modules in order to gain better functional association. We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM) that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level.

From data to function: functional modeling of poultry genomics data.

PubMed

McCarthy, F M; Lyons, E

2013-09-01

One of the challenges of functional genomics is to create a better understanding of the biological system being studied so that the data produced are leveraged to provide gains for agriculture, human health, and the environment. Functional modeling enables researchers to make sense of these data as it reframes a long list of genes or gene products (mRNA, ncRNA, and proteins) by grouping based upon function, be it individual molecular functions or interactions between these molecules or broader biological processes, including metabolic and signaling pathways. However, poultry researchers have been hampered by a lack of functional annotation data, tools, and training to use these data and tools. Moreover, this lack is becoming more critical as new sequencing technologies enable us to generate data not only for an increasingly diverse range of species but also individual genomes and populations of individuals. We discuss the impact of these new sequencing technologies on poultry research, with a specific focus on what functional modeling resources are available for poultry researchers. We also describe key strategies for researchers who wish to functionally model their own data, providing background information about functional modeling approaches, the data and tools to support these approaches, and the strengths and limitations of each. Specifically, we describe methods for functional analysis using Gene Ontology (GO) functional summaries, functional enrichment analysis, and pathways and network modeling. As annotation efforts begin to provide the fundamental data that underpin poultry functional modeling (such as improved gene identification, standardized gene nomenclature, temporal and spatial expression data and gene product function), tool developers are incorporating these data into new and existing tools that are used for functional modeling, and cyberinfrastructure is being developed to provide the necessary extendibility and scalability for storing and analyzing these data. This process will support the efforts of poultry researchers to make sense of their functional genomics data sets, and we provide here a starting point for researchers who wish to take advantage of these tools.

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures.

PubMed

Mazandu, Gaston K; Mulder, Nicola J

2013-09-25

The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore these different GO similarity measure approaches and their biological applications. We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which incorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis.

AnnoLnc: a web server for systematically annotating novel human lncRNAs.

PubMed

Hou, Mei; Tang, Xing; Tian, Feng; Shi, Fangyuan; Liu, Fenglin; Gao, Ge

2016-11-16

Long noncoding RNAs (lncRNAs) have been shown to play essential roles in almost every important biological process through multiple mechanisms. Although the repertoire of human lncRNAs has rapidly expanded, their biological function and regulation remain largely elusive, calling for a systematic and integrative annotation tool. Here we present AnnoLnc ( http://annolnc.cbi.pku.edu.cn ), a one-stop portal for systematically annotating novel human lncRNAs. Based on more than 700 data sources and various tool chains, AnnoLnc enables a systematic annotation covering genomic location, secondary structure, expression patterns, transcriptional regulation, miRNA interaction, protein interaction, genetic association and evolution. An intuitive web interface is available for interactive analysis through both desktops and mobile devices, and programmers can further integrate AnnoLnc into their pipeline through standard JSON-based Web Service APIs. To the best of our knowledge, AnnoLnc is the only web server to provide on-the-fly and systematic annotation for newly identified human lncRNAs. Compared with similar tools, the annotation generated by AnnoLnc covers a much wider spectrum with intuitive visualization. Case studies demonstrate the power of AnnoLnc in not only rediscovering known functions of human lncRNAs but also inspiring novel hypotheses.

Year 2 Report: Protein Function Prediction Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, C E

2012-04-27

Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fullymore » automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.« less

The Representation of Heart Development in the Gene Ontology

PubMed Central

Khodiyar, Varsha K.; Hill, David P.; Howe, Doug; Berardini, Tanya Z.; Tweedie, Susan; Talmud, Philippa J.; Breckenridge, Ross; Bhattarcharya, Shoumo; Riley, Paul; Scambler, Peter; Lovering, Ruth C.

2012-01-01

An understanding of heart development is critical in any systems biology approach to cardiovascular disease. The interpretation of data generated from high-throughput technologies (such as microarray and proteomics) is also essential to this approach. However, characterizing the role of genes in the processes underlying heart development and cardiovascular disease involves the non-trivial task of data analysis and integration of previous knowledge. The Gene Ontology (GO) Consortium provides structured controlled biological vocabularies that are used to summarize previous functional knowledge for gene products across all species. One aspect of GO describes biological processes, such as development and signaling. In order to support high-throughput cardiovascular research, we have initiated an effort to fully describe heart development in GO; expanding the number of GO terms describing heart development from 12 to over 280. This new ontology describes heart morphogenesis, the differentiation of specific cardiac cell types, and the involvement of signaling pathways in heart development and aligns GO with the current views of the heart development research community and its representation in the literature. This extension of GO allows gene product annotators to comprehensively capture the genetic program leading to the developmental progression of the heart. This will enable users to integrate heart development data across species, resulting in the comprehensive retrieval of information about this subject. The revised GO structure, combined with gene product annotations, should improve the interpretation of data from high-throughput methods in a variety of cardiovascular research areas, including heart development, congenital cardiac disease, and cardiac stem cell research. Additionally, we invite the heart development community to contribute to the expansion of this important dataset for the benefit of future research in this area. PMID:21419760

Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.

PubMed

Yang, Qian; Wang, Shuyuan; Dai, Enyu; Zhou, Shunheng; Liu, Dianming; Liu, Haizhou; Meng, Qianqian; Jiang, Bin; Jiang, Wei

2017-08-16

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

easyDAS: Automatic creation of DAS servers

PubMed Central

2011-01-01

Background The Distributed Annotation System (DAS) has proven to be a successful way to publish and share biological data. Although there are more than 750 active registered servers from around 50 organizations, setting up a DAS server comprises a fair amount of work, making it difficult for many research groups to share their biological annotations. Given the clear advantage that the generalized sharing of relevant biological data is for the research community it would be desirable to facilitate the sharing process. Results Here we present easyDAS, a web-based system enabling anyone to publish biological annotations with just some clicks. The system, available at http://www.ebi.ac.uk/panda-srv/easydas is capable of reading different standard data file formats, process the data and create a new publicly available DAS source in a completely automated way. The created sources are hosted on the EBI systems and can take advantage of its high storage capacity and network connection, freeing the data provider from any network management work. easyDAS is an open source project under the GNU LGPL license. Conclusions easyDAS is an automated DAS source creation system which can help many researchers in sharing their biological data, potentially increasing the amount of relevant biological data available to the scientific community. PMID:21244646

Proteomic analysis of Taenia ovis metacestodes by high performance liquid chromatography-coupled tandem mass spectrometry.

PubMed

Zheng, Yadong

2017-03-15

Taenia ovis metacestodes reside in the muscle of sheep and goats, and may cause great economic loss due to condemnation of carcasses if not effectively controlled. Although advances have been made in the control of T. ovis infection, our knowledge of T. ovis biology is limited. Herein the protein profiling of T. ovis metacestodes was determined by liquid chromatography-linked tandem mass spectrometry. A total of 966 proteins were identified and 25.1% (188/748) were annotated to be associated with metabolic pathways. Consistently, GO analysis returned a metabolic process (16.27%) as one of two main biological process terms. Moreover, it was found that 24 proteins, including very low-density lipoprotein receptor, enolase, paramyosin and endophilin B1, were abundant in T. ovis metacestodes. These proteins may be associated with motility, metabolism, signaling, stress, drug resistance and immune responses. Furthermore, comparative analysis of 5 cestodes revealed the presence of Taenia-specific enolases. These data provide clues for better understanding of T. ovis biology, which is informative for effective control of infection. Copyright © 2017 Elsevier B.V. All rights reserved.

Pan-phylum Comparison of Nematode Metabolic Potential

PubMed Central

Tyagi, Rahul; Rosa, Bruce A.; Lewis, Warren G.; Mitreva, Makedonka

2015-01-01

Nematodes are among the most important causative pathogens of neglected tropical diseases. The increased availability of genomic and transcriptomic data for many understudied nematode species provides a great opportunity to investigate different aspects of their biology. Increasingly, metabolic potential of pathogens is recognized as a critical determinant governing their development, growth and pathogenicity. Comparing metabolic potential among species with distinct trophic ecologies can provide insights on overall biology or molecular adaptations. Furthermore, ascertaining gene expression at pathway level can help in understanding metabolic dynamics over development. Comparison of biochemical pathways (or subpathways, i.e. pathway modules) among related species can also retrospectively indicate potential mistakes in gene-calling and functional annotation. We show with numerous illustrative case studies that comparisons at the level of pathway modules have the potential to uncover biological insights while remaining computationally tractable. Here, we reconstruct and compare metabolic modules found in the deduced proteomes of 13 nematodes and 10 non-nematode species (including hosts of the parasitic nematode species). We observed that the metabolic potential is, in general, concomitant with phylogenetic and/or ecological similarity. Varied metabolic strategies are required among the nematodes, with only 8 out of 51 pathway modules being completely conserved. Enzyme comparison based on topology of metabolic modules uncovered diversification between parasite and host that can potentially guide therapeutic intervention. Gene expression data from 4 nematode species were used to study metabolic dynamics over their life cycles. We report unexpected differential metabolism between immature and mature microfilariae of the human filarial parasite Brugia malayi. A set of genes potentially important for parasitism is also reported, based on an analysis of gene expression in C. elegans and the human hookworm Necator americanus. We illustrate how analyzing and comparing metabolism at the level of pathway modules can improve existing knowledge of nematode metabolic potential and can provide parasitism related insights. Our reconstruction and comparison of nematode metabolic pathways at a pan-phylum and inter-phylum level enabled determination of phylogenetic restrictions and differential expression of pathways. A visualization of our results is available at http://nematode.net and the program for identification of module completeness (modDFS) is freely available at SourceForge. The methods reported will help biologists to predict biochemical potential of any organism with available deduced proteome, to direct experiments and test hypotheses. PMID:26000881

The Annotated Bibliography and Citation Behavior: Enhancing Student Scholarship in an Undergraduate Biology Course

PubMed Central

Rux, Erika M.; Flaspohler, John A.

2007-01-01

Contemporary undergraduates in the biological sciences have unprecedented access to scientific information. Although many of these students may be savvy technologists, studies from the field of library and information science consistently show that undergraduates often struggle to locate, evaluate, and use high-quality, reputable sources of information. This study demonstrates the efficacy and pedagogical value of a collaborative teaching approach designed to enhance information literacy competencies among undergraduate biology majors who must write a formal scientific research paper. We rely on the triangulation of assessment data to determine the effectiveness of a substantial research paper project completed by students enrolled in an upper-level biology course. After enhancing library-based instruction, adding an annotated bibliography requirement, and using multiple assessment techniques, we show fundamental improvements in students' library research abilities. Ultimately, these improvements make it possible for students to more independently and effectively complete this challenging science-based writing assignment. We document critical information literacy advances in several key areas: student source-type use, annotated bibliography enhancement, plagiarism reduction, as well as student and faculty/librarian satisfaction. PMID:18056306

Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB

PubMed Central

Bell, Michael J.; Collison, Matthew; Lord, Phillip

2013-01-01

A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge; during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over sentences. Over sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately are erroneous, whilst appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation. Source code and supplementary data are available from the authors website at http://homepages.cs.ncl.ac.uk/m.j.bell1/sentence_analysis/. PMID:24143170

The RCSB Protein Data Bank: views of structural biology for basic and applied research and education

PubMed Central

Rose, Peter W.; Prlić, Andreas; Bi, Chunxiao; Bluhm, Wolfgang F.; Christie, Cole H.; Dutta, Shuchismita; Green, Rachel Kramer; Goodsell, David S.; Westbrook, John D.; Woo, Jesse; Young, Jasmine; Zardecki, Christine; Berman, Helen M.; Bourne, Philip E.; Burley, Stephen K.

2015-01-01

The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine. PMID:25428375

Synergistic effects of arsenic trioxide combined with ascorbic acid in human osteosarcoma MG-63 cells: a systems biology analysis.

PubMed

Huang, X C; Maimaiti, X Y M; Huang, C W; Zhang, L; Li, Z B; Chen, Z G; Gao, X; Chen, T Y

2014-01-01

To further understand the synergistic mechanism of As2O3 and asscorbic acid (AA) in human osteosarcoma MG-63 cells by systems biology analysis. Human osteosarcoma MG-63 cells were treated by As2O3 (1 µmol/L), AA (62.5 µmol/L) and combined drugs (1 µmol/L As2O3 plus 62.5 µmol/L AA). Dynamic morphological characteristics were recorded by Cell-IQ system, and growth rate was calculated. Illumina beadchip assay was used to analyze the differential expression genes in different groups. Synergic effects on differential expression genes (DEGs) were analyzed by mixture linear model and singular value decomposition model. KEGG pathway annotations and GO enrichment analysis were performed to figure out the pathways involved in the synergic effects. We captured 1987 differential expression genes in combined therapy MG-63 cells. FAT1 gene was significantly upregulated in all three groups, which is a promising drug target as an important tumor suppressor analogue; meanwhile, HIST1H2BD gene was markedly downregulated in the As2O3 monotherapy group and the combined therapy group, which was found to be upregulated in prostatic cancer. These two genes might play critical roles in synergetic effects of AA and As2O3, although the exact mechanism needs further investigation. KEGG pathway analysis showed many DEGs were related with tight junction, and GO analysis also indicated that DEGs in the combined therapy cells gathered in occluding junction, apical junction complex, cell junction, and tight junction. AA potentiates the efficacy of As2O3 in MG-63 cells. Systems biology analysis showed the synergic effect on the DEGs.

Identification of MicroRNAs and their Targets Associated with Embryo Abortion during Chrysanthemum Cross Breeding via High-Throughput Sequencing.

PubMed

Zhang, Fengjiao; Dong, Wen; Huang, Lulu; Song, Aiping; Wang, Haibin; Fang, Weimin; Chen, Fadi; Teng, Nianjun

2015-01-01

MicroRNAs (miRNAs) are important regulators in plant development. They post-transcriptionally regulate gene expression during various biological and metabolic processes by binding to the 3'-untranslated region of target mRNAs to facilitate mRNA degradation or inhibit translation. Chrysanthemum (Chrysanthemum morifolium) is one of the most important ornamental flowers with increasing demand each year. However, embryo abortion is the main reason for chrysanthemum cross breeding failure. To date, there have been no experiments examining the expression of miRNAs associated with chrysanthemum embryo development. Therefore, we sequenced three small RNA libraries to identify miRNAs and their functions. Our results will provide molecular insights into chrysanthemum embryo abortion. Three small RNA libraries were built from normal chrysanthemum ovules at 12 days after pollination (DAP), and normal and abnormal chrysanthemum ovules at 18 DAP. We validated 228 miRNAs with significant changes in expression frequency during embryonic development. Comparative profiling revealed that 69 miRNAs exhibited significant differential expression between normal and abnormal embryos at 18 DAP. In addition, a total of 1037 miRNA target genes were predicted, and their annotations were defined by transcriptome data. Target genes associated with metabolic pathways were most highly represented according to the annotation. Moreover, 52 predicted target genes were identified to be associated with embryonic development, including 31 transcription factors and 21 additional genes. Gene ontology (GO) annotation also revealed that high-ranking miRNA target genes related to cellular processes and metabolic processes were involved in transcription regulation and the embryo developmental process. The present study generated three miRNA libraries and gained information on miRNAs and their targets in the chrysanthemum embryo. These results enrich the growing database of new miRNAs and lay the foundation for the further understanding of miRNA biological function in the regulation of chrysanthemum embryo abortion.

Organellar proteomics reveals hundreds of novel nuclear proteins in the malaria parasite Plasmodium falciparum

PubMed Central

2012-01-01

Background The post-genomic era of malaria research provided unprecedented insights into the biology of Plasmodium parasites. Due to the large evolutionary distance to model eukaryotes, however, we lack a profound understanding of many processes in Plasmodium biology. One example is the cell nucleus, which controls the parasite genome in a development- and cell cycle-specific manner through mostly unknown mechanisms. To study this important organelle in detail, we conducted an integrative analysis of the P. falciparum nuclear proteome. Results We combined high accuracy mass spectrometry and bioinformatic approaches to present for the first time an experimentally determined core nuclear proteome for P. falciparum. Besides a large number of factors implicated in known nuclear processes, one-third of all detected proteins carry no functional annotation, including many phylum- or genus-specific factors. Importantly, extensive experimental validation using 30 transgenic cell lines confirmed the high specificity of this inventory, and revealed distinct nuclear localization patterns of hitherto uncharacterized proteins. Further, our detailed analysis identified novel protein domains potentially implicated in gene transcription pathways, and sheds important new light on nuclear compartments and processes including regulatory complexes, the nucleolus, nuclear pores, and nuclear import pathways. Conclusion Our study provides comprehensive new insight into the biology of the Plasmodium nucleus and will serve as an important platform for dissecting general and parasite-specific nuclear processes in malaria parasites. Moreover, as the first nuclear proteome characterized in any protist organism, it will provide an important resource for studying evolutionary aspects of nuclear biology. PMID:23181666

Metabolism and Genetics of Helicobacter pylori: the Genome Era

PubMed Central

Marais, Armelle; Mendz, George L.; Hazell, Stuart L.; Mégraud, Francis

1999-01-01

The publication of the complete sequence of Helicobacter pylori 26695 in 1997 and more recently that of strain J99 has provided new insight into the biology of this organism. In this review, we attempt to analyze and interpret the information provided by sequence annotations and to compare these data with those provided by experimental analyses. After a brief description of the general features of the genomes of the two sequenced strains, the principal metabolic pathways are analyzed. In particular, the enzymes encoded by H. pylori involved in fermentative and oxidative metabolism, lipopolysaccharide biosynthesis, nucleotide biosynthesis, aerobic and anaerobic respiration, and iron and nitrogen assimilation are described, and the areas of controversy between the experimental data and those provided by the sequence annotation are discussed. The role of urease, particularly in pH homeostasis, and other specialized mechanisms developed by the bacterium to maintain its internal pH are also considered. The replicational, transcriptional, and translational apparatuses are reviewed, as is the regulatory network. The numerous findings on the metabolism of the bacteria and the paucity of gene expression regulation systems are indicative of the high level of adaptation to the human gastric environment. Arguments in favor of the diversity of H. pylori and molecular data reflecting possible mechanisms involved in this diversity are presented. Finally, we compare the numerous experimental data on the colonization factors and those provided from the genome sequence annotation, in particular for genes involved in motility and adherence of the bacterium to the gastric tissue. PMID:10477311

Gene Polymorphism Studies in a Teaching Laboratory

NASA Astrophysics Data System (ADS)

Shultz, Jeffry

2009-02-01

I present a laboratory procedure for illustrating transcription, post-transcriptional modification, gene conservation, and comparative genetics for use in undergraduate biology education. Students are individually assigned genes in a targeted biochemical pathway, for which they design and test polymerase chain reaction (PCR) primers. In this example, students used genes annotated for the steroid biosynthesis pathway in soybean. The authoritative Kyoto encyclopedia of genes and genomes (KEGG) interactive database and other online resources were used to design primers based first on soybean expressed sequence tags (ESTs), then on ESTs from an alternate organism if soybean sequence was unavailable. Students designed a total of 50 gene-based primer pairs (37 soybean, 13 alternative) and tested these for polymorphism state and similarity between two soybean and two pea lines. Student assessment was based on acquisition of laboratory skills and successful project completion. This simple procedure illustrates conservation of genes and is not limited to soybean or pea. Cost per student estimates are included, along with a detailed protocol and flow diagram of the procedure.

De Novo Assembly and Comparative Transcriptome Analysis Provide Insight into Lysine Biosynthesis in Toona sinensis Roem.

PubMed

Zhang, Xia; Song, Zhenqiao; Liu, Tian; Guo, Linlin; Li, Xingfeng

2016-01-01

Toona sinensis Roem is a popular leafy vegetable in Chinese cuisine and is also used as a traditional Chinese medicine. In this study, leaf samples were collected from the same plant on two development stages and then used for high-throughput Illumina RNA-sequencing (RNA-Seq). 125,884 transcripts and 54,628 unigenes were obtained through de novo assembly. A total of 25,570 could be annotated with known biological functions, which indicated that the T. sinensis leaves and shoots were undergoing multiple developmental processes especially for active metabolic processes. Analysis of differentially expressed unigenes between the two libraries showed that the lysine biosynthesis was an enriched KEGG pathway, and candidate genes involved in the lysine biosynthesis pathway in T. sinensis leaves and shoots were identified. Our results provide a primary analysis of the gene expression files of T. sinensis leaf and shoot on different development stages and afford a valuable resource for genetic and genomic research on plant lysine biosynthesis.

On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report

PubMed Central

Thomas, Paul D.; Wood, Valerie; Mungall, Christopher J.; Lewis, Suzanna E.; Blake, Judith A.

2012-01-01

A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the “functional similarity” between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the “ortholog conjecture” (or, more properly, the “ortholog functional conservation hypothesis”). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an “open world assumption” (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis. PMID:22359495

Transcript Analysis and Regulative Events during Flower Development in Olive (Olea europaea L.).

PubMed

Alagna, Fiammetta; Cirilli, Marco; Galla, Giulio; Carbone, Fabrizio; Daddiego, Loretta; Facella, Paolo; Lopez, Loredana; Colao, Chiara; Mariotti, Roberto; Cultrera, Nicolò; Rossi, Martina; Barcaccia, Gianni; Baldoni, Luciana; Muleo, Rosario; Perrotta, Gaetano

2016-01-01

The identification and characterization of transcripts involved in flower organ development, plant reproduction and metabolism represent key steps in plant phenotypic and physiological pathways, and may generate high-quality transcript variants useful for the development of functional markers. This study was aimed at obtaining an extensive characterization of the olive flower transcripts, by providing sound information on the candidate MADS-box genes related to the ABC model of flower development and on the putative genetic and molecular determinants of ovary abortion and pollen-pistil interaction. The overall sequence data, obtained by pyrosequencing of four cDNA libraries from flowers at different developmental stages of three olive varieties with distinct reproductive features (Leccino, Frantoio and Dolce Agogia), included approximately 465,000 ESTs, which gave rise to more than 14,600 contigs and approximately 92,000 singletons. As many as 56,700 unigenes were successfully annotated and provided gene ontology insights into the structural organization and putative molecular function of sequenced transcripts and deduced proteins in the context of their corresponding biological processes. Differentially expressed genes with potential regulatory roles in biosynthetic pathways and metabolic networks during flower development were identified. The gene expression studies allowed us to select the candidate genes that play well-known molecular functions in a number of biosynthetic pathways and specific biological processes that affect olive reproduction. A sound understanding of gene functions and regulatory networks that characterize the olive flower is provided.

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations

PubMed Central

Peng, Cheng; Shiu, Shin-Han

2016-01-01

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets. PMID:27935950

Transcript Analysis and Regulative Events during Flower Development in Olive (Olea europaea L.)

PubMed Central

Alagna, Fiammetta; Cirilli, Marco; Galla, Giulio; Carbone, Fabrizio; Daddiego, Loretta; Facella, Paolo; Lopez, Loredana; Colao, Chiara; Mariotti, Roberto; Cultrera, Nicolò; Rossi, Martina; Barcaccia, Gianni; Baldoni, Luciana; Muleo, Rosario; Perrotta, Gaetano

2016-01-01

The identification and characterization of transcripts involved in flower organ development, plant reproduction and metabolism represent key steps in plant phenotypic and physiological pathways, and may generate high-quality transcript variants useful for the development of functional markers. This study was aimed at obtaining an extensive characterization of the olive flower transcripts, by providing sound information on the candidate MADS-box genes related to the ABC model of flower development and on the putative genetic and molecular determinants of ovary abortion and pollen-pistil interaction. The overall sequence data, obtained by pyrosequencing of four cDNA libraries from flowers at different developmental stages of three olive varieties with distinct reproductive features (Leccino, Frantoio and Dolce Agogia), included approximately 465,000 ESTs, which gave rise to more than 14,600 contigs and approximately 92,000 singletons. As many as 56,700 unigenes were successfully annotated and provided gene ontology insights into the structural organization and putative molecular function of sequenced transcripts and deduced proteins in the context of their corresponding biological processes. Differentially expressed genes with potential regulatory roles in biosynthetic pathways and metabolic networks during flower development were identified. The gene expression studies allowed us to select the candidate genes that play well-known molecular functions in a number of biosynthetic pathways and specific biological processes that affect olive reproduction. A sound understanding of gene functions and regulatory networks that characterize the olive flower is provided. PMID:27077738

Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints

PubMed

Mohammadi Majd, Tahereh; Kalantari, Shiva; Raeisi Shahraki, Hadi; Nafar, Mohsen; Almasi, Afshin; Samavat, Shiva; Parvin, Mahmoud; Hashemian, Amirhossein

2018-03-10

IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study. Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis. Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN. SLDA and elastic net had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination.

Activity-Based Protein Profiling of Microbes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sadler, Natalie C.; Wright, Aaron T.

Activity-Based Protein Profiling (ABPP) in conjunction with multimodal characterization techniques has yielded impactful findings in microbiology, particularly in pathogen, bioenergy, drug discovery, and environmental research. Using small molecule chemical probes that react irreversibly with specific proteins or protein families in complex systems has provided insights in enzyme functions in central metabolic pathways, drug-protein interactions, and regulatory protein redox, for systems ranging from photoautotrophic cyanobacteria to mycobacteria, and combining live cell or cell extract ABPP with proteomics, molecular biology, modeling, and other techniques has greatly expanded our understanding of these systems. New opportunities for application of ABPP to microbial systems include:more » enhancing protein annotation, characterizing protein activities in myriad environments, and reveal signal transduction and regulatory mechanisms in microbial systems.« less

A guide to best practices for Gene Ontology (GO) manual annotation

PubMed Central

Balakrishnan, Rama; Harris, Midori A.; Huntley, Rachael; Van Auken, Kimberly; Cherry, J. Michael

2013-01-01

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374 000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all. Database URL: http://www.geneontology.org PMID:23842463

Determining Semantically Related Significant Genes.

PubMed

Taha, Kamal

2014-01-01

GO relation embodies some aspects of existence dependency. If GO term xis existence-dependent on GO term y, the presence of y implies the presence of x. Therefore, the genes annotated with the function of the GO term y are usually functionally and semantically related to the genes annotated with the function of the GO term x. A large number of gene set enrichment analysis methods have been developed in recent years for analyzing gene sets enrichment. However, most of these methods overlook the structural dependencies between GO terms in GO graph by not considering the concept of existence dependency. We propose in this paper a biological search engine called RSGSearch that identifies enriched sets of genes annotated with different functions using the concept of existence dependency. We observe that GO term xcannot be existence-dependent on GO term y, if x- and y- have the same specificity (biological characteristics). After encoding into a numeric format the contributions of GO terms annotating target genes to the semantics of their lowest common ancestors (LCAs), RSGSearch uses microarray experiment to identify the most significant LCA that annotates the result genes. We evaluated RSGSearch experimentally and compared it with five gene set enrichment systems. Results showed marked improvement.

Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci.

PubMed

Reid, Brett M; Permuth, Jennifer B; Chen, Y Ann; Teer, Jamie K; Monteiro, Alvaro N A; Chen, Zhihua; Tyrer, Jonathan; Berchuck, Andrew; Chenevix-Trench, Georgia; Doherty, Jennifer A; Goode, Ellen L; Iverson, Edwin S; Lawrenson, Kate; Pearce, Celeste L; Pharoah, Paul D; Phelan, Catherine M; Ramus, Susan J; Rossing, Mary Anne; Schildkraut, Joellen M; Cheng, Jin Q; Gayther, Simon A; Sellers, Thomas A

2017-01-01

Genome-wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P < 5 × 10 -8 ). Most risk SNPs lie within non-protein-encoding regions, and we hypothesize that long noncoding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets. Using imputed GWAS data from about 18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1 × 10 -5 > P > 5 × 10 -8 ) overlapping lncRNAs. Of 5,294 EOC-associated SNPs (P < 1.0 × 10 -5 ), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r 2 > 0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0 × 10 -4 ) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression. lncRNAs are significantly enriched at EOC risk regions, suggesting a mechanistic role for lncRNAs in driving predisposition to EOC. lncRNAs represent key candidates for integrative epidemiologic and functional studies. Further research on their biologic role in ovarian cancer is indicated. Cancer Epidemiol Biomarkers Prev; 26(1); 116-25. ©2016 AACR. ©2016 American Association for Cancer Research.

An integrative approach to inferring biologically meaningful gene modules

PubMed Central

2011-01-01

Background The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO) annotation in construction of gene modules in order to gain better functional association. Results We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM) that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. Conclusions The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level. PMID:21791051

Enabling locally-developed content for access through the infobutton by means of automated concept annotation.

PubMed

Hulse, Nathan C; Long, Jie; Xu, Xiaomin; Tao, Cui

2014-01-01

Infobuttons have proven to be an increasingly important resource in providing a standardized approach to integrating useful educational materials at the point of care in electronic health records (EHRs). They provide a simple, uniform pathway for both patients and providers to receive pertinent education materials in a quick fashion from within EHRs and Personalized Health Records (PHRs). In recent years, the international standards organization Health Level Seven has balloted and approved a standards-based pathway for requesting and receiving data for infobuttons, simplifying some of the barriers for their adoption in electronic medical records and amongst content providers. Local content, developed by the hosting organization themselves, still needs to be indexed and annotated with appropriate metadata and terminologies in order to be fully accessible via the infobutton. In this manuscript we present an approach for automating the annotation of internally-developed patient education sheets with standardized terminologies and compare and contrast the approach with manual approaches used previously. We anticipate that a combination of system-generated and human reviewed annotations will provide the most comprehensive and effective indexing strategy, thereby allowing best access to internally-created content via the infobutton.

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications.

PubMed

Lee, Byungwook; Kim, Taehyung; Kim, Seon-Kyu; Lee, Kwang H; Lee, Doheon

2007-01-01

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene-patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene-patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at http://www.patome.org/; the information is updated bimonthly.

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications

PubMed Central

Lee, Byungwook; Kim, Taehyung; Kim, Seon-Kyu; Lee, Kwang H.; Lee, Doheon

2007-01-01

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene–patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene–patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at ; the information is updated bimonthly. PMID:17085479

Fast gene ontology based clustering for microarray experiments.

PubMed

Ovaska, Kristian; Laakso, Marko; Hautaniemi, Sampsa

2008-11-21

Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

Experimental Strategies for Functional Annotation and Metabolism Discovery: Targeted Screening of Solute Binding Proteins and Unbiased Panning of Metabolomes

DOE PAGES

Vetting, Matthew W.; Al-Obaidi, Nawar; Zhao, Suwen; ...

2014-12-25

The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. Here in this paper, we describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of themore » library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of D-Ala-D-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.« less

High resolution Chromatin Immunoprecipitation (ChIP) sequencing reveals novel bindings targets and prognostic role for SOX11 in Mantle cell lymphoma

PubMed Central

Kuo, Pei-Yu; Leshchenko, Violetta V.; Fazzari, Melissa J.; Perumal, Deepak; Gellen, Tobias; He, Tianfang; Iqbal, Javeed; Baumgartner-Wennerholm, Stefanie; Nygren, Lina; Zhang, Fan; Zhang, Weijia; Suh, K. Stephen; Goy, Andre; Yang, David T.; Chan, Wing-Chung; Kahl, Brad S.; Verma, Amit K.; Gascoyne, Randy D.; Kimby, Eva; Sander, Birgitta; Ye, B. Hilda; Melnick, Ari M.; Parekh, Samir

2015-01-01

SOX11 (Sex determining region Y-box 11) expression is specific for MCL as compared to other Non-Hodgkin's lymphomas. However, the function and direct binding targets of SOX11 in MCL are largely unknown. We used high-resolution ChIP-Seq to identify the direct target genes of SOX11 in a genome-wide, unbiased manner and elucidate its functional significance. Pathway analysis identified WNT, PKA and TGF-beta signaling pathways as significantly enriched by SOX11 target genes. qCHIP and promoter reporter assays confirmed that SOX11 directly binds to individual genes and modulates their transcription activities in these pathways in MCL. Functional studies using RNA interference demonstrate that SOX11 directly regulates WNT in MCL. We analyzed SOX11 expression in three independent well-annotated tissue microarrays from the University of Wisconsin (UW), Karolinska Institute and British Columbia Cancer Agency (BCCA). Our findings suggest that high SOX11 expression is associated with improved survival in a subset of MCL patients, particularly those treated with intensive chemotherapy. Transcriptional regulation of WNT and other biological pathways affected by SOX11 target genes may help explain the impact of SOX11 expression on patient outcomes. PMID:24681958

Data integration in biological research: an overview.

PubMed

Lapatas, Vasileios; Stefanidakis, Michalis; Jimenez, Rafael C; Via, Allegra; Schneider, Maria Victoria

2015-12-01

Data sharing, integration and annotation are essential to ensure the reproducibility of the analysis and interpretation of the experimental findings. Often these activities are perceived as a role that bioinformaticians and computer scientists have to take with no or little input from the experimental biologist. On the contrary, biological researchers, being the producers and often the end users of such data, have a big role in enabling biological data integration. The quality and usefulness of data integration depend on the existence and adoption of standards, shared formats, and mechanisms that are suitable for biological researchers to submit and annotate the data, so it can be easily searchable, conveniently linked and consequently used for further biological analysis and discovery. Here, we provide background on what is data integration from a computational science point of view, how it has been applied to biological research, which key aspects contributed to its success and future directions.

De novo characterization of the pine aphid Cinara pinitabulaeformis Zhang et Zhang transcriptome and analysis of genes relevant to pesticides

PubMed Central

Rebeca, Carballar-Lejarazú; Zhu, Xiaoli; Guo, Yajie; Lin, Qiannan; Hu, Xia; Wang, Rong; Liang, Guanghong; Guan, Xiong

2017-01-01

The pine aphid Cinara pinitabulaeformis Zhang et Zhang is the main pine pest in China, it causes pine needles to produce dense dew (honeydew) which can lead to sooty mold (black filamentous saprophytic ascomycetes). Although common chemical and physical strategies are used to prevent the disease caused by C. pinitabulaeformis Zhang et Zhang, new strategies based on biological and/or genetic approaches are promising to control and eradicate the disease. However, there is no information about genomics, proteomics or transcriptomics to allow the design of new control strategies for this pine aphid. We used next generation sequencing technology to sequence the transcriptome of C. pinitabulaeformis Zhang et Zhang and built a transcriptome database. We identified 80,259 unigenes assigned for Gene Ontology (GO) terms and information for a total of 11,609 classified unigenes was obtained in the Clusters of Orthologous Groups (COGs). A total of 10,806 annotated unigenes were analyzed to identify the represented biological pathways, among them 8,845 unigenes matched with 228 KEGG pathways. In addition, our data describe propagative viruses, nutrition-related genes, detoxification related molecules, olfactory related receptors, stressed-related protein, putative insecticide resistance genes and possible insecticide targets. Moreover, this study provides valuable information about putative insecticide resistance related genes and for the design of new genetic/biological based strategies to manage and control C. pinitabulaeformis Zhang et Zhang populations. PMID:28570707

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures

PubMed Central

2013-01-01

Background The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore these different GO similarity measure approaches and their biological applications. Results We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which incorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. Conclusions The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis. PMID:24067102

Identification of Functionally Related Enzymes by Learning-to-Rank Methods.

PubMed

Stock, Michiel; Fober, Thomas; Hüllermeier, Eyke; Glinca, Serghei; Klebe, Gerhard; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2014-01-01

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.

Ouabain rescues rat nephrogenesis during intrauterine growth restriction by regulating the complement and coagulation cascades and calcium signaling pathway.

PubMed

Chen, L; Yue, J; Han, X; Li, J; Hu, Y

2016-02-01

Intrauterine growth restriction (IUGR) is associated with a reduction in the numbers of nephrons in neonates, which increases the risk of hypertension. Our previous study showed that ouabain protects the development of the embryonic kidney during IUGR. To explore this molecular mechanism, IUGR rats were induced by protein and calorie restriction throughout pregnancy, and ouabain was delivered using a mini osmotic pump. RNA sequencing technology was used to identify the differentially expressed genes (DEGs) of the embryonic kidneys. DEGs were submitted to the Database for Annotation and Visualization and Integrated Discovery, and gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted. Maternal malnutrition significantly reduced fetal weight, but ouabain treatment had no significant effect on body weight. A total of 322 (177 upregulated and 145 downregulated) DEGs were detected between control and the IUGR group. Meanwhile, 318 DEGs were found to be differentially expressed (180 increased and 138 decreased) between the IUGR group and the ouabain-treated group. KEGG pathway analysis indicated that maternal undernutrition mainly disrupts the complement and coagulation cascades and the calcium signaling pathway, which could be protected by ouabain treatment. Taken together, these two biological pathways may play an important role in nephrogenesis, indicating potential novel therapeutic targets against the unfavorable effects of IUGR.

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leung, Elo; Huang, Amy; Cadag, Eithon

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE PAGES

Leung, Elo; Huang, Amy; Cadag, Eithon; ...

2016-01-20

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

PubMed Central

Draper, John; Enot, David P; Parker, David; Beckmann, Manfred; Snowdon, Stuart; Lin, Wanchang; Zubair, Hassan

2009-01-01

Background Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI). Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data. PMID:19622150

ASGARD: an open-access database of annotated transcriptomes for emerging model arthropod species.

PubMed

Zeng, Victor; Extavour, Cassandra G

2012-01-01

The increased throughput and decreased cost of next-generation sequencing (NGS) have shifted the bottleneck genomic research from sequencing to annotation, analysis and accessibility. This is particularly challenging for research communities working on organisms that lack the basic infrastructure of a sequenced genome, or an efficient way to utilize whatever sequence data may be available. Here we present a new database, the Assembled Searchable Giant Arthropod Read Database (ASGARD). This database is a repository and search engine for transcriptomic data from arthropods that are of high interest to multiple research communities but currently lack sequenced genomes. We demonstrate the functionality and utility of ASGARD using de novo assembled transcriptomes from the milkweed bug Oncopeltus fasciatus, the cricket Gryllus bimaculatus and the amphipod crustacean Parhyale hawaiensis. We have annotated these transcriptomes to assign putative orthology, coding region determination, protein domain identification and Gene Ontology (GO) term annotation to all possible assembly products. ASGARD allows users to search all assemblies by orthology annotation, GO term annotation or Basic Local Alignment Search Tool. User-friendly features of ASGARD include search term auto-completion suggestions based on database content, the ability to download assembly product sequences in FASTA format, direct links to NCBI data for predicted orthologs and graphical representation of the location of protein domains and matches to similar sequences from the NCBI non-redundant database. ASGARD will be a useful repository for transcriptome data from future NGS studies on these and other emerging model arthropods, regardless of sequencing platform, assembly or annotation status. This database thus provides easy, one-stop access to multi-species annotated transcriptome information. We anticipate that this database will be useful for members of multiple research communities, including developmental biology, physiology, evolutionary biology, ecology, comparative genomics and phylogenomics. Database URL: asgard.rc.fas.harvard.edu.

Estrogen alters the profile of the transcriptome in river snail Bellamya aeruginosa.

PubMed

Lei, Kun; Liu, Ruizhi; An, Li-Hui; Luo, Ying-Feng; LeBlanc, Gerald A

2015-03-01

We evaluated the transcriptome dynamics of the freshwater river snail Bellamya aeruginosa exposed to 17β-estradiol (E2) using the Roche/454 GS-FLX platform. In total, 41,869 unigenes, with an average length of 586 bp, representing 36,181 contigs and 5,688 singlets were obtained. Among them, 18.08, 36.85, and 25.47 % matched sequences in the GenBank non-redundant nucleic acid database, non-redundant protein database, and Swiss protein database, respectively. Annotation of the unigenes with gene ontology, and then mapping them to biological pathways, revealed large groups of genes related to growth, development, reproduction, signal transduction, and defense mechanisms. Significant differences were found in gene expression in both liver and testicular tissues between control and E2-exposed organisms. These changes in gene expression will help in understanding the molecular mechanisms of the response to physiological stress in the river snail exposed to estrogen, and will facilitate research into biological processes and underlying physiological adaptations to xenoestrogen exposure in gastropods.

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

PubMed Central

Overbeek, Ross; Olson, Robert; Pusch, Gordon D.; Olsen, Gary J.; Davis, James J.; Disz, Terry; Edwards, Robert A.; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R.; Xia, Fangfang; Stevens, Rick

2014-01-01

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources. PMID:24293654

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST).

PubMed

Overbeek, Ross; Olson, Robert; Pusch, Gordon D; Olsen, Gary J; Davis, James J; Disz, Terry; Edwards, Robert A; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R; Xia, Fangfang; Stevens, Rick

2014-01-01

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules.

PubMed

Wassermann, Anne Mai; Lounkine, Eugen; Glick, Meir

2013-03-25

Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.

Classification of Chemical Compounds to Support Complex Queries in a Pathway Database

PubMed Central

Weidemann, Andreas; Kania, Renate; Peiss, Christian; Rojas, Isabel

2004-01-01

Data quality in biological databases has become a topic of great discussion. To provide high quality data and to deal with the vast amount of biochemical data, annotators and curators need to be supported by software that carries out part of their work in an (semi-) automatic manner. The detection of errors and inconsistencies is a part that requires the knowledge of domain experts, thus in most cases it is done manually, making it very expensive and time-consuming. This paper presents two tools to partially support the curation of data on biochemical pathways. The tool enables the automatic classification of chemical compounds based on their respective SMILES strings. Such classification allows the querying and visualization of biochemical reactions at different levels of abstraction, according to the level of detail at which the reaction participants are described. Chemical compounds can be classified in a flexible manner based on different criteria. The support of the process of data curation is provided by facilitating the detection of compounds that are identified as different but that are actually the same. This is also used to identify similar reactions and, in turn, pathways. PMID:18629066

Comparative Transcriptomic Characterization of the Early Development in Pacific White Shrimp Litopenaeus vannamei

PubMed Central

Wei, Jiankai; Zhang, Xiaojun; Yu, Yang; Huang, Hao; Li, Fuhua; Xiang, Jianhai

2014-01-01

Penaeid shrimp has a distinctive metamorphosis stage during early development. Although morphological and biochemical studies about this ontogeny have been developed for decades, researches on gene expression level are still scarce. In this study, we have investigated the transcriptomes of five continuous developmental stages in Pacific white shrimp (Litopenaeus vannamei) with high throughput Illumina sequencing technology. The reads were assembled and clustered into 66,815 unigenes, of which 32,398 have putative homologues in nr database, 14,981 have been classified into diverse functional categories by Gene Ontology (GO) annotation and 26,257 have been associated with 255 pathways by KEGG pathway mapping. Meanwhile, the differentially expressed genes (DEGs) between adjacent developmental stages were identified and gene expression patterns were clustered. By GO term enrichment analysis, KEGG pathway enrichment analysis and functional gene profiling, the physiological changes during shrimp metamorphosis could be better understood, especially histogenesis, diet transition, muscle development and exoskeleton reconstruction. In conclusion, this is the first study that characterized the integrated transcriptomic profiles during early development of penaeid shrimp, and these findings will serve as significant references for shrimp developmental biology and aquaculture research. PMID:25197823

De novo Transcriptome Assembly of Floral Buds of Pineapple and Identification of Differentially Expressed Genes in Response to Ethephon Induction

PubMed Central

Liu, Chuan-He; Fan, Chao

2016-01-01

A remarkable characteristic of pineapple is its ability to undergo floral induction in response to external ethylene stimulation. However, little information is available regarding the molecular mechanism underlying this process. In this study, the differentially expressed genes (DEGs) in plants exposed to 1.80 mL·L−1 (T1) or 2.40 mL·L−1 ethephon (T2) compared with Ct plants (control, cleaning water) were identified using RNA-seq and gene expression profiling. Illumina sequencing generated 65,825,224 high-quality reads that were assembled into 129,594 unigenes with an average sequence length of 1173 bp. Of these unigenes, 24,775 were assigned to specific KEGG pathways, of which metabolic pathways and biosynthesis of secondary metabolites were the most highly represented. Gene Ontology (GO) analysis of the annotated unigenes revealed that the majority were involved in metabolic and cellular processes, cell and cell part, catalytic activity and binding. Gene expression profiling analysis revealed 3788, 3062, and 758 DEGs in the comparisons of T1 with Ct, T2 with Ct, and T2 with T1, respectively. GO analysis indicated that these DEGs were predominantly annotated to metabolic and cellular processes, cell and cell part, catalytic activity, and binding. KEGG pathway analysis revealed the enrichment of several important pathways among the DEGs, including metabolic pathways, biosynthesis of secondary metabolites and plant hormone signal transduction. Thirteen DEGs were identified as candidate genes associated with the process of floral induction by ethephon, including three ERF-like genes, one ETR-like gene, one LTI-like gene, one FT-like gene, one VRN1-like gene, three FRI-like genes, one AP1-like gene, one CAL-like gene, and one AG-like gene. qPCR analysis indicated that the changes in the expression of these 13 candidate genes were consistent with the alterations in the corresponding RPKM values, confirming the accuracy and credibility of the RNA-seq and gene expression profiling results. Ethephon-mediated induction likely mimics the process of vernalization in the floral transition in pineapple by increasing LTI, FT, and VRN1 expression and promoting the up-regulation of floral meristem identity genes involved in flower development. The candidate genes screened can be used in investigations of the molecular mechanisms of the flowering pathway and of various other biological mechanisms in pineapple. PMID:26955375

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

PubMed Central

Zheng, Jie; Stoyanovich, Julia; Manduchi, Elisabetta; Liu, Junmin; Stoeckert, Christian J.

2011-01-01

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute. Database URL: http://www.cbil.upenn.edu/annotCompute/ PMID:22190598

Reading in Marine Science, A Partially Annotated Bibliography for Young Readers, Nonprofessionals, and Teachers.

ERIC Educational Resources Information Center

Oregon State Univ., Corvallis. Dept. of Oceanography.

Included is a partially annotated bibliography of mostly non-technical books for non-professional readers, young readers, and teachers. There are about 300 entries grouped into these subjects: general references, historical and exploration, biological oceanography, chemical oceanography, geological oceanography, and physical oceanography. (PR)

THE EFFECTS OF RADIATION AND RADIOISOTOPES ON THE LIFE PROCESSES. An Annotated Bibliography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pierce, C.M. comp.

Issued in two books and an Index. A total of 11,944 annotated references is presented to report and published literature concerning the effects of radiation on biological systems. Abstracts are included for many references. Author and subject indexes are included. (C.H.)

Teacher-to-Teacher: An Annotated Bibliography on DNA and Genetic Engineering.

ERIC Educational Resources Information Center

Mertens, Thomas R., Comp.

1984-01-01

Presented is an annotated bibliography of 24 books on DNA and genetic engineering. Areas considered in these books include: basic biological concepts to help understand advances in genetic engineering; applications of genetic engineering; social, legal, and moral issues of genetic engineering; and historical aspects leading to advances in…

Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions

DOE PAGES

Martí-Arbona, Ricardo; Mu, Fangping; Nowak-Lovato, Kristy L.; ...

2014-12-18

In this study, the clustering of genes in a pathway and the co-location of functionally related genes is widely recognized in prokaryotes. We used these characteristics to predict the metabolic involvement for a Transcriptional Regulator (TR) of unknown function, identified and confirmed its biological activity. software tool that identifies the genes encoded within a defined genomic neighborhood for the subject TR and its homologs was developed. The output lists of genes in the genetic neighborhoods, their annotated functions, the reactants/products, and identifies the metabolic pathway in which the encoded-proteins function. When a set of TRs of known function was analyzed,more » we observed that their homologs frequently had conserved genomic neighborhoods that co-located the metabolically related genes regulated by the subject TR. We postulate that TR effectors are metabolites in the identified pathways; indeed the known effectors were present. We analyzed Bxe_B3018 from Burkholderia xenovorans, a TR of unknown function and predicted that this TR was related to the glycine, threonine and serine degradation. We tested the binding of metabolites in these pathways and for those that bound, their ability to modulate TR binding to its specific DNA operator sequence. Using rtPCR, we confirmed that methylglyoxal was an effector of Bxe_3018. These studies provide the proof of concept and validation of a systematic approach to the discovery of the biological activity for proteins of unknown function, in this case a TR. Bxe_B3018 is a methylglyoxal responsive TR that controls the expression of an operon composed of a putative efflux system.« less

A comprehensive map of the influenza A virus replication cycle

PubMed Central

2013-01-01

Background Influenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets. Description We constructed a comprehensive map of the influenza A virus (‘IAV’) life cycle (‘FluMap’) by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap. Conclusion The FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/. PMID:24088197

A genome resource to address mechanisms of developmental programming: determination of the fetal sheep heart transcriptome.

PubMed

Cox, Laura A; Glenn, Jeremy P; Spradling, Kimberly D; Nijland, Mark J; Garcia, Roy; Nathanielsz, Peter W; Ford, Stephen P

2012-06-15

The pregnant sheep has provided seminal insights into reproduction related to animal and human development (ovarian function, fertility, implantation, fetal growth, parturition and lactation). Fetal sheep physiology has been extensively studied since 1950, contributing significantly to the basis for our understanding of many aspects of fetal development and behaviour that remain in use in clinical practice today. Understanding mechanisms requires the combination of systems approaches uniquely available in fetal sheep with the power of genomic studies. Absence of the full range of sheep genomic resources has limited the full realization of the power of this model, impeding progress in emerging areas of pregnancy biology such as developmental programming. We have examined the expressed fetal sheep heart transcriptome using high-throughput sequencing technologies. In so doing we identified 36,737 novel transcripts and describe genes, gene variants and pathways relevant to fundamental developmental mechanisms. Genes with the highest expression levels and with novel exons in the fetal heart transcriptome are known to play central roles in muscle development. We show that high-throughput sequencing methods can generate extensive transcriptome information in the absence of an assembled and annotated genome for that species. The gene sequence data obtained provide a unique genomic resource for sheep specific genetic technology development and, combined with the polymorphism data, augment annotation and assembly of the sheep genome. In addition, identification and pathway analysis of novel fetal sheep heart transcriptome splice variants is a first step towards revealing mechanisms of genetic variation and gene environment interactions during fetal heart development.

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

PubMed

Rue-Albrecht, Kévin; McGettigan, Paul A; Hernández, Belinda; Nalpas, Nicolas C; Magee, David A; Parnell, Andrew C; Gordon, Stephen V; MacHugh, David E

2016-03-11

Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

A genome resource to address mechanisms of developmental programming: determination of the fetal sheep heart transcriptome

PubMed Central

Cox, Laura A; Glenn, Jeremy P; Spradling, Kimberly D; Nijland, Mark J; Garcia, Roy; Nathanielsz, Peter W; Ford, Stephen P

2012-01-01

The pregnant sheep has provided seminal insights into reproduction related to animal and human development (ovarian function, fertility, implantation, fetal growth, parturition and lactation). Fetal sheep physiology has been extensively studied since 1950, contributing significantly to the basis for our understanding of many aspects of fetal development and behaviour that remain in use in clinical practice today. Understanding mechanisms requires the combination of systems approaches uniquely available in fetal sheep with the power of genomic studies. Absence of the full range of sheep genomic resources has limited the full realization of the power of this model, impeding progress in emerging areas of pregnancy biology such as developmental programming. We have examined the expressed fetal sheep heart transcriptome using high-throughput sequencing technologies. In so doing we identified 36,737 novel transcripts and describe genes, gene variants and pathways relevant to fundamental developmental mechanisms. Genes with the highest expression levels and with novel exons in the fetal heart transcriptome are known to play central roles in muscle development. We show that high-throughput sequencing methods can generate extensive transcriptome information in the absence of an assembled and annotated genome for that species. The gene sequence data obtained provide a unique genomic resource for sheep specific genetic technology development and, combined with the polymorphism data, augment annotation and assembly of the sheep genome. In addition, identification and pathway analysis of novel fetal sheep heart transcriptome splice variants is a first step towards revealing mechanisms of genetic variation and gene environment interactions during fetal heart development. PMID:22508961

Towards a complete map of the human long non-coding RNA transcriptome.

PubMed

Uszczynska-Ratajczak, Barbara; Lagarde, Julien; Frankish, Adam; Guigó, Roderic; Johnson, Rory

2018-05-23

Gene maps, or annotations, enable us to navigate the functional landscape of our genome. They are a resource upon which virtually all studies depend, from single-gene to genome-wide scales and from basic molecular biology to medical genetics. Yet present-day annotations suffer from trade-offs between quality and size, with serious but often unappreciated consequences for downstream studies. This is particularly true for long non-coding RNAs (lncRNAs), which are poorly characterized compared to protein-coding genes. Long-read sequencing technologies promise to improve current annotations, paving the way towards a complete annotation of lncRNAs expressed throughout a human lifetime.

A Comprehensive Approach to Sequence-oriented IsomiR annotation (CASMIR): demonstration with IsomiR profiling in colorectal neoplasia.

PubMed

Wu, Chung Wah; Evans, Jared M; Huang, Shengbing; Mahoney, Douglas W; Dukek, Brian A; Taylor, William R; Yab, Tracy C; Smyrk, Thomas C; Jen, Jin; Kisiel, John B; Ahlquist, David A

2018-05-25

MicroRNA (miRNA) profiling is an important step in studying biological associations and identifying marker candidates. miRNA exists in isoforms, called isomiRs, which may exhibit distinct properties. With conventional profiling methods, limitations in assay and analysis platforms may compromise isomiR interrogation. We introduce a comprehensive approach to sequence-oriented isomiR annotation (CASMIR) to allow unbiased identification of global isomiRs from small RNA sequencing data. In this approach, small RNA reads are maintained as independent sequences instead of being summarized under miRNA names. IsomiR features are identified through step-wise local alignment against canonical forms and precursor sequences. Through customizing the reference database, CASMIR is applicable to isomiR annotation across species. To demonstrate its application, we investigated isomiR profiles in normal and neoplastic human colorectal epithelia. We also ran miRDeep2, a popular miRNA analysis algorithm to validate isomiRs annotated by CASMIR. With CASMIR, specific and biologically relevant isomiR patterns could be identified. We note that specific isomiRs are often more abundant than their canonical forms. We identify isomiRs that are commonly up-regulated in both colorectal cancer and advanced adenoma, and illustrate advantages in targeting isomiRs as potential biomarkers over canonical forms. Studying miRNAs at the isomiR level could reveal new insight into miRNA biology and inform assay design for specific isomiRs. CASMIR facilitates comprehensive annotation of isomiR features in small RNA sequencing data for isomiR profiling and differential expression analysis.

TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes

PubMed Central

Leroy, Philippe; Guilhot, Nicolas; Sakai, Hiroaki; Bernard, Aurélien; Choulet, Frédéric; Theil, Sébastien; Reboux, Sébastien; Amano, Naoki; Flutre, Timothée; Pelegrin, Céline; Ohyanagi, Hajime; Seidel, Michael; Giacomoni, Franck; Reichstadt, Mathieu; Alaux, Michael; Gicquello, Emmanuelle; Legeai, Fabrice; Cerutti, Lorenzo; Numa, Hisataka; Tanaka, Tsuyoshi; Mayer, Klaus; Itoh, Takeshi; Quesneville, Hadi; Feuillet, Catherine

2012-01-01

In support of the international effort to obtain a reference sequence of the bread wheat genome and to provide plant communities dealing with large and complex genomes with a versatile, easy-to-use online automated tool for annotation, we have developed the TriAnnot pipeline. Its modular architecture allows for the annotation and masking of transposable elements, the structural, and functional annotation of protein-coding genes with an evidence-based quality indexing, and the identification of conserved non-coding sequences and molecular markers. The TriAnnot pipeline is parallelized on a 712 CPU computing cluster that can run a 1-Gb sequence annotation in less than 5 days. It is accessible through a web interface for small scale analyses or through a server for large scale annotations. The performance of TriAnnot was evaluated in terms of sensitivity, specificity, and general fitness using curated reference sequence sets from rice and wheat. In less than 8 h, TriAnnot was able to predict more than 83% of the 3,748 CDS from rice chromosome 1 with a fitness of 67.4%. On a set of 12 reference Mb-sized contigs from wheat chromosome 3B, TriAnnot predicted and annotated 93.3% of the genes among which 54% were perfectly identified in accordance with the reference annotation. It also allowed the curation of 12 genes based on new biological evidences, increasing the percentage of perfect gene prediction to 63%. TriAnnot systematically showed a higher fitness than other annotation pipelines that are not improved for wheat. As it is easily adaptable to the annotation of other plant genomes, TriAnnot should become a useful resource for the annotation of large and complex genomes in the future. PMID:22645565

The RCSB Protein Data Bank: views of structural biology for basic and applied research and education.

PubMed

Rose, Peter W; Prlić, Andreas; Bi, Chunxiao; Bluhm, Wolfgang F; Christie, Cole H; Dutta, Shuchismita; Green, Rachel Kramer; Goodsell, David S; Westbrook, John D; Woo, Jesse; Young, Jasmine; Zardecki, Christine; Berman, Helen M; Bourne, Philip E; Burley, Stephen K

2015-01-01

The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Analysis of disease-associated objects at the Rat Genome Database

PubMed Central

Wang, Shur-Jen; Laulederkind, Stanley J. F.; Hayman, G. T.; Smith, Jennifer R.; Petri, Victoria; Lowry, Timothy F.; Nigam, Rajni; Dwinell, Melinda R.; Worthey, Elizabeth A.; Munzenmaier, Diane H.; Shimoyama, Mary; Jacob, Howard J.

2013-01-01

The Rat Genome Database (RGD) is the premier resource for genetic, genomic and phenotype data for the laboratory rat, Rattus norvegicus. In addition to organizing biological data from rats, the RGD team focuses on manual curation of gene–disease associations for rat, human and mouse. In this work, we have analyzed disease-associated strains, quantitative trait loci (QTL) and genes from rats. These disease objects form the basis for seven disease portals. Among disease portals, the cardiovascular disease and obesity/metabolic syndrome portals have the highest number of rat strains and QTL. These two portals share 398 rat QTL, and these shared QTL are highly concentrated on rat chromosomes 1 and 2. For disease-associated genes, we performed gene ontology (GO) enrichment analysis across portals using RatMine enrichment widgets. Fifteen GO terms, five from each GO aspect, were selected to profile enrichment patterns of each portal. Of the selected biological process (BP) terms, ‘regulation of programmed cell death’ was the top enriched term across all disease portals except in the obesity/metabolic syndrome portal where ‘lipid metabolic process’ was the most enriched term. ‘Cytosol’ and ‘nucleus’ were common cellular component (CC) annotations for disease genes, but only the cancer portal genes were highly enriched with ‘nucleus’ annotations. Similar enrichment patterns were observed in a parallel analysis using the DAVID functional annotation tool. The relationship between the preselected 15 GO terms and disease terms was examined reciprocally by retrieving rat genes annotated with these preselected terms. The individual GO term–annotated gene list showed enrichment in physiologically related diseases. For example, the ‘regulation of blood pressure’ genes were enriched with cardiovascular disease annotations, and the ‘lipid metabolic process’ genes with obesity annotations. Furthermore, we were able to enhance enrichment of neurological diseases by combining ‘G-protein coupled receptor binding’ annotated genes with ‘protein kinase binding’ annotated genes. Database URL: http://rgd.mcw.edu PMID:23794737

ezTag: tagging biomedical concepts via interactive learning.

PubMed

Kwon, Dongseop; Kim, Sun; Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2018-05-18

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

PubMed Central

Cruz-Roa, Angel; Díaz, Gloria; Romero, Eduardo; González, Fabio A.

2011-01-01

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. PMID:22811960

PANNZER2: a rapid functional annotation web server.

PubMed

Törönen, Petri; Medlar, Alan; Holm, Liisa

2018-05-08

The unprecedented growth of high-throughput sequencing has led to an ever-widening annotation gap in protein databases. While computational prediction methods are available to make up the shortfall, a majority of public web servers are hindered by practical limitations and poor performance. Here, we introduce PANNZER2 (Protein ANNotation with Z-scoRE), a fast functional annotation web server that provides both Gene Ontology (GO) annotations and free text description predictions. PANNZER2 uses SANSparallel to perform high-performance homology searches, making bulk annotation based on sequence similarity practical. PANNZER2 can output GO annotations from multiple scoring functions, enabling users to see which predictions are robust across predictors. Finally, PANNZER2 predictions scored within the top 10 methods for molecular function and biological process in the CAFA2 NK-full benchmark. The PANNZER2 web server is updated on a monthly schedule and is accessible at http://ekhidna2.biocenter.helsinki.fi/sanspanz/. The source code is available under the GNU Public Licence v3.

Potential physiological effects of pharmaceutical compounds in Atlantic salmon (Salmo salar) implied by transcriptomic analysis.

PubMed

Hampel, Miriam; Alonso, Esteban; Aparicio, Irene; Bron, James E; Santos, Juan Luis; Taggart, John B; Leaver, Michael J

2010-05-01

Pharmaceuticals are emerging pollutants widely used in everyday urban activities which can be detected in surface, ground, and drinking waters. Their presence is derived from consumption of medicines, disposal of expired medications, release of treated and untreated urban effluents, and from the pharmaceutical industry. Their growing use has become an alarming environmental problem which potentially will become dangerous in the future. However, there is still a lack of knowledge about long-term effects in non-target organisms as well as for human health. Toxicity testing has indicated a relatively low acute toxicity to fish species, but no information is available on possible sublethal effects. This study provides data on the physiological pathways involved in the exposure of Atlantic salmon as representative test species to three pharmaceutical compounds found in ground, surface, and drinking waters based on the evaluation of the xenobiotic-induced impairment resulting in the activation and silencing of specific genes. Individuals of Atlantic salmon (Salmo salar) parr were exposed during 5 days to environmentally relevant concentrations of three representative pharmaceutical compounds with high consumption rates: the analgesic acetaminophen (54.77+/-34.67 microg L(-1)), the anticonvulsant carbamazepine (7.85+/-0.13 microg L(-1)), and the beta-blocker atenolol (11.08+/-7.98 microg L(-1)). Five immature males were selected for transcriptome analysis in brain tissues by means of a 17k salmon cDNA microarray. For this purpose, mRNA was isolated and reverse-transcribed into cDNA which was labeled with fluorescent dyes and hybridized against a common pool to the arrays. Lists of significantly up- and down-regulated candidate genes were submitted to KEGG (Kyoto Encyclopedia of Genes and Genomes) in order to analyze for induced pathways and to evaluate the usefulness of this method in cases of not completely annotated test organisms. Exposure during 5 days to environmentally relevant concentrations of the selected pharmaceutical compounds acetaminophen, carbamazepine, and atenolol produced differences in the expression of 659, 700, and 480 candidate genes, respectively. KEGG annotation numbers (KO annotations) were obtained for between 26.57% and 33.33% of these differently expressed genes per treatment in comparison to non-exposure conditions. Pathways that showed to be induced did not always follow previously reported targets or metabolic routes for the employed treatments; however, several other pathways have been found (four or more features) to be significantly induced. Energy-related pathways have been altered under exposure in all the selected treatments, indicating a possible energy budget leakage due to additional processes resulting from the exposure to environmental contaminants. Observed induction of pathways may indicate additional processes involved in the mode of action of the selected pharmaceuticals which may not have been detected with conventional methods like quantitative PCR in which only suspected features are analyzed punctually for effects. The employment of novel high-throughput screening techniques in combination with global pathway analysis methods, even if the organism is not completely annotated, allows the examination of a much broader range of candidates for potential effects of exposure at the gene level. The continuously growing number of annotations of representative species relevant for environmental quality testing is facilitating pathway analysis processes for not completely annotated organisms. KEGG has shown to be a useful tool for the analysis of induced pathways from data generated by microarray techniques with the selected pharmaceutical contaminants acetaminophen, carbamazepine, and atenolol, but further studies have to be carried out in order to determine if a similar expression pattern in terms of fold change quantity and pathways is observed after long-term exposure. Together with the information obtained in this study, it will then be possible to evaluate the potential risk that the continuous release of these compounds may have on the environment and ecosystem functioning.

Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

PubMed Central

Weniger, Markus; Engelmann, Julia C; Schultz, Jörg

2007-01-01

Background Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. Results We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at . Conclusion GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at . PMID:17543125

Creating a Structured Adverse Outcome Pathway Knowledgebase via Ontology-Based Annotations

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is increasingly used to integrate data based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. Herein, we present a comprehens...

Toward an Upgraded Honey Bee (Apis mellifera L.) Genome Annotation Using Proteogenomics.

PubMed

McAfee, Alison; Harpur, Brock A; Michaud, Sarah; Beavis, Ronald C; Kent, Clement F; Zayed, Amro; Foster, Leonard J

2016-02-05

The honey bee is a key pollinator in agricultural operations as well as a model organism for studying the genetics and evolution of social behavior. The Apis mellifera genome has been sequenced and annotated twice over, enabling proteomics and functional genomics methods for probing relevant aspects of their biology. One troubling trend that emerged from proteomic analyses is that honey bee peptide samples consistently result in lower peptide identification rates compared with other organisms. This suggests that the genome annotation can be improved, or atypical biological processes are interfering with the mass spectrometry workflow. First, we tested whether high levels of polymorphisms could explain some of the missed identifications by searching spectra against the reference proteome (OGSv3.2) versus a customized proteome of a single honey bee, but our results indicate that this contribution was minor. Likewise, error-tolerant peptide searches lead us to eliminate unexpected post-translational modifications as a major factor in missed identifications. We then used a proteogenomic approach with ~1500 raw files to search for missing genes and new exons, to revive discarded annotations and to identify over 2000 new coding regions. These results will contribute to a more comprehensive genome annotation and facilitate continued research on this important insect.

Toll-like receptor signaling in vertebrates: testing the integration of protein, complex, and pathway data in the protein ontology framework.

PubMed

Arighi, Cecilia; Shamovsky, Veronica; Masci, Anna Maria; Ruttenberg, Alan; Smith, Barry; Natale, Darren A; Wu, Cathy; D'Eustachio, Peter

2015-01-01

The Protein Ontology (PRO) provides terms for and supports annotation of species-specific protein complexes in an ontology framework that relates them both to their components and to species-independent families of complexes. Comprehensive curation of experimentally known forms and annotations thereof is expected to expose discrepancies, differences, and gaps in our knowledge. We have annotated the early events of innate immune signaling mediated by Toll-Like Receptor 3 and 4 complexes in human, mouse, and chicken. The resulting ontology and annotation data set has allowed us to identify species-specific gaps in experimental data and possible functional differences between species, and to employ inferred structural and functional relationships to suggest plausible resolutions of these discrepancies and gaps.

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks.

PubMed

Olivon, Florent; Allard, Pierre-Marie; Koval, Alexey; Righi, Davide; Genta-Jouve, Gregory; Neyts, Johan; Apel, Cécile; Pannecouque, Christophe; Nothias, Louis-Félix; Cachet, Xavier; Marcourt, Laurence; Roussi, Fanny; Katanaev, Vladimir L; Touboul, David; Wolfender, Jean-Luc; Litaudon, Marc

2017-10-20

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.

Solving the Problem: Genome Annotation Standards before the Data Deluge.

PubMed

Klimke, William; O'Donovan, Claire; White, Owen; Brister, J Rodney; Clark, Karen; Fedorov, Boris; Mizrachi, Ilene; Pruitt, Kim D; Tatusova, Tatiana

2011-10-15

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries.

Solving the Problem: Genome Annotation Standards before the Data Deluge

PubMed Central

Klimke, William; O'Donovan, Claire; White, Owen; Brister, J. Rodney; Clark, Karen; Fedorov, Boris; Mizrachi, Ilene; Pruitt, Kim D.; Tatusova, Tatiana

2011-01-01

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries. PMID:22180819

Annotation of the Asian Citrus Psyllid Genome Reveals a Reduced Innate Immune System

PubMed Central

Arp, Alex P.; Hunter, Wayne B.; Pelz-Stelinski, Kirsten S.

2016-01-01

Citrus production worldwide is currently facing significant losses due to citrus greening disease, also known as Huanglongbing. The citrus greening bacteria, Candidatus Liberibacter asiaticus (CLas), is a persistent propagative pathogen transmitted by the Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae). Hemipterans characterized to date lack a number of insect immune genes, including those associated with the Imd pathway targeting Gram-negative bacteria. The D. citri draft genome was used to characterize the immune defense genes present in D. citri. Predicted mRNAs identified by screening the published D. citri annotated draft genome were manually searched using a custom database of immune genes from previously annotated insect genomes. Toll and JAK/STAT pathways, general defense genes Dual oxidase, Nitric oxide synthase, prophenoloxidase, and cellular immune defense genes were present in D. citri. In contrast, D. citri lacked genes for the Imd pathway, most antimicrobial peptides, 1,3-β-glucan recognition proteins (GNBPs), and complete peptidoglycan recognition proteins. These data suggest that D. citri has a reduced immune capability similar to that observed in A. pisum, P. humanus, and R. prolixus. The absence of immune system genes from the D. citri genome may facilitate CLas infections, and is possibly compensated for by their relationship with their microbial endosymbionts. PMID:27965582

Transcriptomic analyses on muscle tissues of Litopenaeus vannamei provide the first profile insight into the response to low temperature stress.

PubMed

Huang, Wen; Ren, Chunhua; Li, Hongmei; Huo, Da; Wang, Yanhong; Jiang, Xiao; Tian, Yushun; Luo, Peng; Chen, Ting; Hu, Chaoqun

2017-01-01

The Pacific white shrimp (Litopenaeus vannamei) is an important cultured crustacean species worldwide. However, little is known about the molecular mechanism of this species involved in the response to cold stress. In this study, four separate RNA-Seq libraries of L. vannamei were generated from 13°C stress and control temperature. Total 29,662 of Unigenes and overall of 19,619 annotated genes were obtained. Three comparisons were carried out among the four libraries, in which 72 of the top 20% of differentially-expressed genes were obtained, 15 GO and 5 KEGG temperature-sensitive pathways were fished out. Catalytic activity (GO: 0003824) and Metabolic pathways (ko01100) were the most annotated GO and KEGG pathways in response to cold stress, respectively. In addition, Calcium, MAPK cascade, Transcription factor and Serine/threonine-protein kinase signal pathway were picked out and clustered. Serine/threonine-protein kinase signal pathway might play more important roles in cold adaptation, while other three signal pathway were not widely transcribed. Our results had summarized the differentially-expressed genes and suggested the major important signaling pathways and related genes. These findings provide the first profile insight into the molecular basis of L. vannamei response to cold stress.

Transcriptomic analyses on muscle tissues of Litopenaeus vannamei provide the first profile insight into the response to low temperature stress

PubMed Central

Huang, Wen; Ren, Chunhua; Li, Hongmei; Huo, Da; Wang, Yanhong; Jiang, Xiao; Tian, Yushun; Luo, Peng; Hu, Chaoqun

2017-01-01

The Pacific white shrimp (Litopenaeus vannamei) is an important cultured crustacean species worldwide. However, little is known about the molecular mechanism of this species involved in the response to cold stress. In this study, four separate RNA-Seq libraries of L. vannamei were generated from 13°C stress and control temperature. Total 29,662 of Unigenes and overall of 19,619 annotated genes were obtained. Three comparisons were carried out among the four libraries, in which 72 of the top 20% of differentially-expressed genes were obtained, 15 GO and 5 KEGG temperature-sensitive pathways were fished out. Catalytic activity (GO: 0003824) and Metabolic pathways (ko01100) were the most annotated GO and KEGG pathways in response to cold stress, respectively. In addition, Calcium, MAPK cascade, Transcription factor and Serine/threonine-protein kinase signal pathway were picked out and clustered. Serine/threonine-protein kinase signal pathway might play more important roles in cold adaptation, while other three signal pathway were not widely transcribed. Our results had summarized the differentially-expressed genes and suggested the major important signaling pathways and related genes. These findings provide the first profile insight into the molecular basis of L. vannamei response to cold stress. PMID:28575089

Brucella proteomes--a review.

PubMed

DelVecchio, Vito G; Wagner, Mary Ann; Eschenbrenner, Michel; Horn, Troy A; Kraycer, Jo Ann; Estock, Frank; Elzer, Phil; Mujer, Cesar V

2002-12-20

The proteomes of selected Brucella spp. have been extensively analyzed by utilizing current proteomic technology involving 2-DE and MALDI-MS. In Brucella melitensis, more than 500 proteins were identified. The rapid and large-scale identification of proteins in this organism was accomplished by using the annotated B. melitensis genome which is now available in the GenBank. Coupled with new and powerful tools for data analysis, differentially expressed proteins were identified and categorized into several classes. A global overview of protein expression patterns emerged, thereby facilitating the simultaneous analysis of different metabolic pathways in B. melitensis. Such a global characterization would not have been possible by using time consuming and traditional biochemical approaches. The era of post-genomic technology offers new and exciting opportunities to understand the complete biology of different Brucella species.

Identification of widespread adenosine nucleotide binding in Mycobacterium tuberculosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ansong, Charles; Ortega, Corrie; Payne, Samuel H.

The annotation of protein function is almost completely performed by in silico approaches. However, computational prediction of protein function is frequently incomplete and error prone. In Mycobacterium tuberculosis (Mtb), ~25% of all genes have no predicted function and are annotated as hypothetical proteins. This lack of functional information severely limits our understanding of Mtb pathogenicity. Current tools for experimental functional annotation are limited and often do not scale to entire protein families. Here, we report a generally applicable chemical biology platform to functionally annotate bacterial proteins by combining activity-based protein profiling (ABPP) and quantitative LC-MS-based proteomics. As an example ofmore » this approach for high-throughput protein functional validation and discovery, we experimentally annotate the families of ATP-binding proteins in Mtb. Our data experimentally validate prior in silico predictions of >250 ATPases and adenosine nucleotide-binding proteins, and reveal 73 hypothetical proteins as novel ATP-binding proteins. We identify adenosine cofactor interactions with many hypothetical proteins containing a diversity of unrelated sequences, providing a new and expanded view of adenosine nucleotide binding in Mtb. Furthermore, many of these hypothetical proteins are both unique to Mycobacteria and essential for infection, suggesting specialized functions in mycobacterial physiology and pathogenicity. Thus, we provide a generally applicable approach for high throughput protein function discovery and validation, and highlight several ways in which application of activity-based proteomics data can improve the quality of functional annotations to facilitate novel biological insights.« less

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Hybrid genome assembly and annotation of Paenibacillus pasadenensis strain R16 reveals insights on endophytic life style and antifungal activity

PubMed Central

Passera, Alessandro; Marcolungo, Luca; Brasca, Milena; Quaglino, Fabio; Cantaloni, Chiara; Delledonne, Massimo

2018-01-01

Bacteria of the Paenibacillus genus are becoming important in many fields of science, including agriculture, for their positive effects on the health of plants. However, there are little information available on this genus compared to other bacteria (such as Bacillus or Pseudomonas), especially when considering genomic information. Sequencing the genomes of plant-beneficial bacteria is a crucial step to identify the genetic elements underlying the adaptation to life inside a plant host and, in particular, which of these features determine the differences between a helpful microorganism and a pathogenic one. In this study, we have characterized the genome of Paenibacillus pasadenensis, strain R16, recently investigated for its antifungal activities and plant-associated features. An hybrid assembly approach was used integrating the very precise reads obtained by Illumina technology and long fragments acquired with Oxford Nanopore Technology (ONT) sequencing. De novo genome assembly based solely on Illumina reads generated a relatively fragmented assembly of 5.72 Mbp in 99 ungapped sequences with an N50 length of 544 Kbp; hybrid assembly, integrating Illumina and ONT reads, improved the assembly quality, generating a genome of 5.75 Mbp, organized in 6 contigs with an N50 length of 3.4 Mbp. Annotation of the latter genome identified 4987 coding sequences, of which 1610 are hypothetical proteins. Enrichment analysis identified pathways of particular interest for the endophyte biology, including the chitin-utilization pathway and the incomplete siderophore pathway which hints at siderophore parasitism. In addition the analysis led to the identification of genes for the production of terpenes, as for example farnesol, that was hypothesized as the main antifungal molecule produced by the strain. The functional analysis on the genome confirmed several plant-associated, plant-growth promotion, and biocontrol traits of strain R16, thus adding insights in the genetic bases of these complex features, and of the Paenibacillus genus in general. PMID:29351296

De novo transcriptome analysis of rose-scented geranium provides insights into the metabolic specificity of terpene and tartaric acid biosynthesis.

PubMed

Narnoliya, Lokesh K; Kaushal, Girija; Singh, Sudhir P; Sangwan, Rajender S

2017-01-13

Rose-scented geranium (Pelargonium sp.) is a perennial herb that produces a high value essential oil of fragrant significance due to the characteristic compositional blend of rose-oxide and acyclic monoterpenoids in foliage. Recently, the plant has also been shown to produce tartaric acid in leaf tissues. Rose-scented geranium represents top-tier cash crop in terms of economic returns and significance of the plant and plant products. However, there has hardly been any study on its metabolism and functional genomics, nor any genomic expression dataset resource is available in public domain. Therefore, to begin the gains in molecular understanding of specialized metabolic pathways of the plant, de novo sequencing of rose-scented geranium leaf transcriptome, transcript assembly, annotation, expression profiling as well as their validation were carried out. De novo transcriptome analysis resulted a total of 78,943 unique contigs (average length: 623 bp, and N50 length: 752 bp) from 15.44 million high quality raw reads. In silico functional annotation led to the identification of several putative genes representing terpene, ascorbic acid and tartaric acid biosynthetic pathways, hormone metabolism, and transcription factors. Additionally, a total of 6,040 simple sequence repeat (SSR) motifs were identified in 6.8% of the expressed transcripts. The highest frequency of SSR was of tri-nucleotides (50%). Further, transcriptome assembly was validated for randomly selected putative genes by standard PCR-based approach. In silico expression profile of assembled contigs were validated by real-time PCR analysis of selected transcripts. Being the first report on transcriptome analysis of rose-scented geranium the data sets and the leads and directions reflected in this investigation will serve as a foundation for pursuing and understanding molecular aspects of its biology, and specialized metabolic pathways, metabolic engineering, genetic diversity as well as molecular breeding.

In silico database screening of potential targets and pathways of compounds contained in plants used for psoriasis vulgaris.

PubMed

May, Brian H; Deng, Shiqiang; Zhang, Anthony L; Lu, Chuanjian; Xue, Charlie C L

2015-09-01

Reviews and meta-analyses of clinical trials identified plants used as traditional medicines (TMs) that show promise for psoriasis. These include Rehmannia glutinosa, Camptotheca acuminata, Indigo naturalis and Salvia miltiorrhiza. Compounds contained in these TMs have shown activities of relevance to psoriasis in experimental models. To further investigate the likely mechanisms of action of the multiple compounds in these TMs, we undertook a computer-based in silico investigation of the proteins known to be regulated by these compounds and their associated biological pathways. The proteins reportedly regulated by compounds in these four TMs were identified using the HIT (Herbal Ingredients' Targets) database. The resultant data were entered into the PANTHER (Protein ANnotation THrough Evolutionary Relationship) database to identify the pathways in which the proteins could be involved. The study identified 237 compounds in the TMs and these retrieved 287 proteins from HIT. These proteins identified 59 pathways in PANTHER with most proteins being located in the Apoptosis, Angiogenesis, Inflammation mediated by chemokine and cytokine, Gonadotropin releasing hormone receptor, and/or Interleukin signaling pathways. All four TMs contained compounds that had regulating effects on Apoptosis regulator BAX, Apoptosis regulator Bcl-2, Caspase-3, Tumor necrosis factor (TNF) or Prostaglandin G/H synthase 2 (COX2). The main proteins and pathways are primarily related to inflammation, proliferation and angiogenesis which are all processes involved in psoriasis. Experimental studies have reported that certain compounds from these TMs can regulate the expression of proteins involved in each of these pathways.

De Novo Characterization of the Spleen Transcriptome of the Large Yellow Croaker (Pseudosciaena crocea) and Analysis of the Immune Relevant Genes and Pathways Involved in the Antiviral Response

PubMed Central

Ding, Yang; Ao, Jingqun; Hu, Songnian; Chen, Xinhua

2014-01-01

The large yellow croaker (Pseudosciaena crocea) is an economically important marine fish in China. To understand the molecular basis for antiviral defense in this species, we used Illumia paired-end sequencing to characterize the spleen transcriptome of polyriboinosinic:polyribocytidylic acid [poly(I:C)]-induced large yellow croakers. The library produced 56,355,728 reads and assembled into 108,237 contigs. As a result, 15,192 unigenes were found from this transcriptome. Gene ontology analysis showed that 4,759 genes were involved in three major functional categories: biological process, cellular component, and molecular function. We further ascertained that numerous consensus sequences were homologous to known immune-relevant genes. Kyoto Encyclopedia of Genes and Genomes orthology mapping annotated 5,389 unigenes and identified numerous immune-relevant pathways. These immune-relevant genes and pathways revealed major antiviral immunity effectors, including but not limited to: pattern recognition receptors, adaptors and signal transducers, the interferons and interferon-stimulated genes, inflammatory cytokines and receptors, complement components, and B-cell and T-cell antigen activation molecules. Moreover, the partial genes of Toll-like receptor signaling pathway, RIG-I-like receptors signaling pathway, Janus kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signaling pathway, and T-cell receptor (TCR) signaling pathway were found to be changed after poly(I:C) induction by real-time polymerase chain reaction (PCR) analysis, suggesting that these signaling pathways may be regulated by poly(I:C), a viral mimic. Overall, the antivirus-related genes and signaling pathways that were identified in response to poly(I:C) challenge provide valuable leads for further investigation of the antiviral defense mechanism in the large yellow croaker. PMID:24820969

Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis.

PubMed

Grossmann, Steffen; Bauer, Sebastian; Robinson, Peter N; Vingron, Martin

2007-11-15

High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.

Genome-wide association study in Finnish twins highlights the connection between nicotine addiction and neurotrophin signaling pathway.

PubMed

Hällfors, Jenni; Palviainen, Teemu; Surakka, Ida; Gupta, Richa; Buchwald, Jadwiga; Raevuori, Anu; Ripatti, Samuli; Korhonen, Tellervo; Jousilahti, Pekka; Madden, Pamela A F; Kaprio, Jaakko; Loukola, Anu

2018-03-13

The heritability of nicotine dependence based on family studies is substantial. Nevertheless, knowledge of the underlying genetic architecture remains meager. Our aim was to identify novel genetic variants responsible for interindividual differences in smoking behavior. We performed a genome-wide association study on 1715 ever smokers ascertained from the population-based Finnish Twin Cohort enriched for heavy smoking. Data imputation used the 1000 Genomes Phase I reference panel together with a whole genome sequence-based Finnish reference panel. We analyzed three measures of nicotine addiction-smoking quantity, nicotine dependence and nicotine withdrawal. We annotated all genome-wide significant SNPs for their functional potential. First, we detected genome-wide significant association on 16p12 with smoking quantity (P = 8.5 × 10 -9 ), near CLEC19A. The lead-SNP stands 22 kb from a binding site for NF-κB transcription factors, which play a role in the neurotrophin signaling pathway. However, the signal was not replicated in an independent Finnish population-based sample, FINRISK (n = 6763). Second, nicotine withdrawal showed association on 2q21 in an intron of TMEM163 (P = 2.1 × 10 -9 ), and on 11p15 (P = 6.6 × 10 -8 ) in an intron of AP2A2, and P = 4.2 × 10 -7 for a missense variant in MUC6, both involved in the neurotrophin signaling pathway). Third, association was detected on 3p22.3 for maximum number of cigarettes smoked per day (P = 3.1 × 10 -8 ) near STAC. Associating CLEC19A and TMEM163 SNPs were annotated to influence gene expression or methylation. The neurotrophin signaling pathway has previously been associated with smoking behavior. Our findings further support the role in nicotine addiction. © 2018 The Authors. Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.

Combining computational models, semantic annotations and simulation experiments in a graph database

PubMed Central

Henkel, Ron; Wolkenhauer, Olaf; Waltemath, Dagmar

2015-01-01

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/ PMID:25754863

TrypsNetDB: An integrated framework for the functional characterization of trypanosomatid proteins

PubMed Central

Gazestani, Vahid H.; Yip, Chun Wai; Nikpour, Najmeh; Berghuis, Natasha

2017-01-01

Trypanosomatid parasites cause serious infections in humans and production losses in livestock. Due to the high divergence from other eukaryotes, such as humans and model organisms, the functional roles of many trypanosomatid proteins cannot be predicted by homology-based methods, rendering a significant portion of their proteins as uncharacterized. Recent technological advances have led to the availability of multiple systematic and genome-wide datasets on trypanosomatid parasites that are informative regarding the biological role(s) of their proteins. Here, we report TrypsNetDB (http://trypsNetDB.org), a web-based resource for the functional annotation of 16 different species/strains of trypanosomatid parasites. The database not only visualizes the network context of the queried protein(s) in an intuitive way but also examines the response of the represented network in more than 50 different biological contexts and its enrichment for various biological terms and pathways, protein sequence signatures, and potential RNA regulatory elements. The interactome core of the database, as of Jan 23, 2017, contains 101,187 interactions among 13,395 trypanosomatid proteins inferred from 97 genome-wide and focused studies on the interactome of these organisms. PMID:28158179

Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy?

PubMed Central

Piergiorge, Rafael Mina; de Miranda, Antonio Basílio; Catanho, Marcos

2017-01-01

Abstract Since enzymes catalyze almost all chemical reactions that occur in living organisms, it is crucial that genes encoding such activities are correctly identified and functionally characterized. Several studies suggest that the fraction of enzymatic activities in which multiple events of independent origin have taken place during evolution is substantial. However, this topic is still poorly explored, and a comprehensive investigation of the occurrence, distribution, and implications of these events has not been done so far. Fundamental questions, such as how analogous enzymes originate, why so many events of independent origin have apparently occurred during evolution, and what are the reasons for the coexistence in the same organism of distinct enzymatic forms catalyzing the same reaction, remain unanswered. Also, several isofunctional enzymes are still not recognized as nonhomologous, even with substantial evidence indicating different evolutionary histories. In this work, we begin to investigate the biological significance of the cooccurrence of nonhomologous isofunctional enzymes in human metabolism, characterizing functional analogous enzymes identified in metabolic pathways annotated in the human genome. Our hypothesis is that the coexistence of multiple enzymatic forms might not be interpreted as functional redundancy. Instead, these enzymatic forms may be implicated in distinct (and probably relevant) biological roles. PMID:28854631

PSI:Biology-Materials Repository: A Biologist’s Resource for Protein Expression Plasmids

PubMed Central

Cormier, Catherine Y.; Park, Jin G.; Fiacco, Michael; Steel, Jason; Hunter, Preston; Kramer, Jason; Singla, Rajeev; LaBaer, Joshua

2011-01-01

The Protein Structure Initiative:Biology-Materials Repository (PSI:Biology-MR; MR; http://psimr.asu.edu) sequence-verifies, annotates, stores, and distributes the protein expression plasmids and vectors created by the Protein Structure Initiative (PSI). The MR has developed an informatics and sample processing pipeline that manages this process for thousands of samples per month from nearly a dozen PSI centers. DNASU (http://dnasu.asu.edu), a freely searchable database, stores the plasmid annotations, which include the full-length sequence, vector information, and associated publications for over 130,000 plasmids created by our laboratory, by the PSI and other consortia, and by individual laboratories for distribution to researchers worldwide. Each plasmid links to external resources, including the PSI Structural Biology Knowledgebase (http://sbkb.org), which facilitates cross-referencing of a particular plasmid to additional protein annotations and experimental data. To expedite and simplify plasmid requests, the MR uses an expedited material transfer agreement (EP-MTA) network, where researchers from network institutions can order and receive PSI plasmids without institutional delays. Currently over 39,000 protein expression plasmids and 78 empty vectors from the PSI are available upon request from DNASU. Overall, the MR’s repository of expression-ready plasmids, its automated pipeline, and the rapid process for receiving and distributing these plasmids more effectively allows the research community to dissect the biological function of proteins whose structures have been studied by the PSI. PMID:21360289

An Annotated List of Disciplines and Sub-Disciplines in the Biological Sciences

ERIC Educational Resources Information Center

McDonald, Brandon

2008-01-01

Biology has become a large and diversified science. Current biological research areas transgress academic and professional boundaries to such a degree that the biological sciences could arguably be referred to as "all encompassing." In this article, the author describes how he compiled information on currently recognised disciplines and…

Immunosenescence-Related Transcriptomic and Immunologic Changes in Older Individuals Following Influenza Vaccination

PubMed Central

Kennedy, Richard B.; Ovsyannikova, Inna G.; Haralambieva, Iana H.; Oberg, Ann L.; Zimmermann, Michael T.; Grill, Diane E.; Poland, Gregory A.

2016-01-01

The goal of annual influenza vaccination is to reduce mortality and morbidity associated with this disease through the generation of protective immune responses. The objective of the current study was to examine markers of immunosenescence and identify immunosenescence-related differences in gene expression, gene regulation, cytokine secretion, and immunologic changes in an older study population receiving seasonal influenza A/H1N1 vaccination. Surprisingly, prior studies in this cohort revealed weak correlations between immunosenescence markers and humoral immune response to vaccination. In this report, we further examined the relationship of each immunosenescence marker (age, T cell receptor excision circle frequency, telomerase expression, percentage of CD28− CD4+ T cells, percentage of CD28− CD8+ T cells, and the CD4/CD8 T cell ratio) with additional markers of immune response (serum cytokine and chemokine expression) and measures of gene expression and/or regulation. Many of the immunosenescence markers indeed correlated with distinct sets of individual DNA methylation sites, miRNA expression levels, mRNA expression levels, serum cytokines, and leukocyte subsets. However, when the individual immunosenescence markers were grouped by pathways or functional terms, several shared biological functions were identified: antigen processing and presentation pathways, MAPK, mTOR, TCR, BCR, and calcium signaling pathways, as well as key cellular metabolic, proliferation and survival activities. Furthermore, the percent of CD4+ and/or CD8+ T cells lacking CD28 expression also correlated with miRNAs regulating clusters of genes known to be involved in viral infection. Integrated (DNA methylation, mRNA, miRNA, and protein levels) network biology analysis of immunosenescence-related pathways and genesets identified both known pathways (e.g., chemokine signaling, CTL, and NK cell activity), as well as a gene expression module not previously annotated with a known function. These results may improve our ability to predict immune responses to influenza and aid in new vaccine development, and highlight the need for additional studies to better define and characterize immunosenescence. PMID:27853459

DNASU plasmid and PSI:Biology-Materials repositories: resources to accelerate biological research

PubMed Central

Seiler, Catherine Y.; Park, Jin G.; Sharma, Amit; Hunter, Preston; Surapaneni, Padmini; Sedillo, Casey; Field, James; Algar, Rhys; Price, Andrea; Steel, Jason; Throop, Andrea; Fiacco, Michael; LaBaer, Joshua

2014-01-01

The mission of the DNASU Plasmid Repository is to accelerate research by providing high-quality, annotated plasmid samples and online plasmid resources to the research community through the curated DNASU database, website and repository (http://dnasu.asu.edu or http://dnasu.org). The collection includes plasmids from grant-funded, high-throughput cloning projects performed in our laboratory, plasmids from external researchers, and large collections from consortia such as the ORFeome Collaboration and the NIGMS-funded Protein Structure Initiative: Biology (PSI:Biology). Through DNASU, researchers can search for and access detailed information about each plasmid such as the full length gene insert sequence, vector information, associated publications, and links to external resources that provide additional protein annotations and experimental protocols. Plasmids can be requested directly through the DNASU website. DNASU and the PSI:Biology-Materials Repositories were previously described in the 2010 NAR Database Issue (Cormier, C.Y., Mohr, S.E., Zuo, D., Hu, Y., Rolfs, A., Kramer, J., Taycher, E., Kelley, F., Fiacco, M., Turnbull, G. et al. (2010) Protein Structure Initiative Material Repository: an open shared public resource of structural genomics plasmids for the biological community. Nucleic Acids Res., 38, D743–D749.). In this update we will describe the plasmid collection and highlight the new features in the website redesign, including new browse/search options, plasmid annotations and a dynamic vector mapping feature that was developed in collaboration with LabGenius. Overall, these plasmid resources continue to enable research with the goal of elucidating the role of proteins in both normal biological processes and disease. PMID:24225319

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

TGF-beta signaling proteins and the Protein Ontology.

PubMed

Arighi, Cecilia N; Liu, Hongfang; Natale, Darren A; Barker, Winona C; Drabkin, Harold; Blake, Judith A; Smith, Barry; Wu, Cathy H

2009-05-06

The Protein Ontology (PRO) is designed as a formal and principled Open Biomedical Ontologies (OBO) Foundry ontology for proteins. The components of PRO extend from a classification of proteins on the basis of evolutionary relationships at the homeomorphic level to the representation of the multiple protein forms of a gene, including those resulting from alternative splicing, cleavage and/or post-translational modifications. Focusing specifically on the TGF-beta signaling proteins, we describe the building, curation, usage and dissemination of PRO. PRO is manually curated on the basis of PrePRO, an automatically generated file with content derived from standard protein data sources. Manual curation ensures that the treatment of the protein classes and the internal and external relationships conform to the PRO framework. The current release of PRO is based upon experimental data from mouse and human proteins wherein equivalent protein forms are represented by single terms. In addition to the PRO ontology, the annotation of PRO terms is released as a separate PRO association file, which contains, for each given PRO term, an annotation from the experimentally characterized sub-types as well as the corresponding database identifiers and sequence coordinates. The annotations are added in the form of relationship to other ontologies. Whenever possible, equivalent forms in other species are listed to facilitate cross-species comparison. Splice and allelic variants, gene fusion products and modified protein forms are all represented as entities in the ontology. Therefore, PRO provides for the representation of protein entities and a resource for describing the associated data. This makes PRO useful both for proteomics studies where isoforms and modified forms must be differentiated, and for studies of biological pathways, where representations need to take account of the different ways in which the cascade of events may depend on specific protein modifications. PRO provides a framework for the formal representation of protein classes and protein forms in the OBO Foundry. It is designed to enable data retrieval and integration and machine reasoning at the molecular level of proteins, thereby facilitating cross-species comparisons, pathway analysis, disease modeling and the generation of new hypotheses.

Small molecule annotation for the Protein Data Bank

PubMed Central

Sen, Sanchayita; Young, Jasmine; Berrisford, John M.; Chen, Minyu; Conroy, Matthew J.; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P.; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A.

2014-01-01

The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100 000 structures contain more than 20 000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. PMID:25425036

Small molecule annotation for the Protein Data Bank.

PubMed

Sen, Sanchayita; Young, Jasmine; Berrisford, John M; Chen, Minyu; Conroy, Matthew J; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A

2014-01-01

The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100,000 structures contain more than 20,000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. © The Author(s) 2014. Published by Oxford University Press.

Comparative transcriptome analysis of microsclerotia development in Nomuraea rileyi.

PubMed

Song, Zhangyong; Yin, Youping; Jiang, Shasha; Liu, Juanjuan; Chen, Huan; Wang, Zhongkang

2013-06-19

Nomuraea rileyi is used as an environmental-friendly biopesticide. However, mass production and commercialization of this organism are limited due to its fastidious growth and sporulation requirements. When cultured in amended medium, we found that N. rileyi could produce microsclerotia bodies, replacing conidiophores as the infectious agent. However, little is known about the genes involved in microsclerotia development. In the present study, the transcriptomes were analyzed using next-generation sequencing technology to find the genes involved in microsclerotia development. A total of 4.69 Gb of clean nucleotides comprising 32,061 sequences was obtained, and 20,919 sequences were annotated (about 65%). Among the annotated sequences, only 5928 were annotated with 34 gene ontology (GO) functional categories, and 12,778 sequences were mapped to 165 pathways by searching against the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) database. Furthermore, we assessed the transcriptomic differences between cultures grown in minimal and amended medium. In total, 4808 sequences were found to be differentially expressed; 719 differentially expressed unigenes were assigned to 25 GO classes and 1888 differentially expressed unigenes were assigned to 161 KEGG pathways, including 25 enrichment pathways. Subsequently, we examined the up-regulation or uniquely expressed genes following amended medium treatment, which were also expressed on the enrichment pathway, and found that most of them participated in mediating oxidative stress homeostasis. To elucidate the role of oxidative stress in microsclerotia development, we analyzed the diversification of unigenes using quantitative reverse transcription-PCR (RT-qPCR). Our findings suggest that oxidative stress occurs during microsclerotia development, along with a broad metabolic activity change. Our data provide the most comprehensive sequence resource available for the study of N. rileyi. We believe that the transcriptome datasets will serve as an important public information platform to accelerate studies on N. rileyi microsclerotia.

Transcriptome analysis of the tea oil camellia (Camellia oleifera) reveals candidate drought stress genes.

PubMed

Dong, Bin; Wu, Bin; Hong, Wenhong; Li, Xiuping; Li, Zhuo; Xue, Li; Huang, Yongfang

2017-01-01

The tea-oil camellia (Camellia oleifera) is the most important oil plant in southern China, and has a strong resistance to drought and barren soil. Understanding the molecular mechanisms of drought tolerance would greatly promote its cultivation and molecular breeding. In total, we obtained 76,585 unigenes with an average length of 810 bp and an N50 of 1,092 bp. We mapped all the unigenes to the NCBI 'nr' (non-redundant), SwissProt, KEGG, and clusters of orthologous groups (COG) databases, where 52,531 (68.6%) unigenes were functionally annotated. According to the annotation, 46,171 (60.8%) unigenes belong to 338 KEGG pathways. We identified a series of unigenes that are related to the synthesis and regulation of abscisic acid (ABA), the activity of protective enzymes, vitamin B6 metabolism, the metabolism of osmolytes, and pathways related to the biosynthesis of secondary metabolites. After exposed to drought for 12 hours, the number of differentially-expressed genes (DEGs) between treated plants and control plants increased in the G4 cultivar, while there was no significant increase in the drought-tolerant C3 cultivar. DEGs associated with drought stress responsive pathways were identified by KEGG pathway enrichment analysis. Moreover, we found 789 DEGs related to transcription factors. Finally, according to the results of qRT-PCR, the expression levels of the 20 unigenes tested were consistent with the results of next-generation sequencing. In the present study, we identified a large set of cDNA unigenes from C. oleifera annotated using public databases. Further studies of DEGs involved in metabolic pathways related to drought stress and transcription will facilitate the discovery of novel genes involved in resistance to drought stress in this commercially important plant.

Transcriptome analysis of the tea oil camellia (Camellia oleifera) reveals candidate drought stress genes

PubMed Central

Wu, Bin; Hong, Wenhong; Li, Xiuping; Li, Zhuo; Xue, Li; Huang, Yongfang

2017-01-01

Background The tea-oil camellia (Camellia oleifera) is the most important oil plant in southern China, and has a strong resistance to drought and barren soil. Understanding the molecular mechanisms of drought tolerance would greatly promote its cultivation and molecular breeding. Results In total, we obtained 76,585 unigenes with an average length of 810 bp and an N50 of 1,092 bp. We mapped all the unigenes to the NCBI ‘nr’ (non-redundant), SwissProt, KEGG, and clusters of orthologous groups (COG) databases, where 52,531 (68.6%) unigenes were functionally annotated. According to the annotation, 46,171 (60.8%) unigenes belong to 338 KEGG pathways. We identified a series of unigenes that are related to the synthesis and regulation of abscisic acid (ABA), the activity of protective enzymes, vitamin B6 metabolism, the metabolism of osmolytes, and pathways related to the biosynthesis of secondary metabolites. After exposed to drought for 12 hours, the number of differentially-expressed genes (DEGs) between treated plants and control plants increased in the G4 cultivar, while there was no significant increase in the drought-tolerant C3 cultivar. DEGs associated with drought stress responsive pathways were identified by KEGG pathway enrichment analysis. Moreover, we found 789 DEGs related to transcription factors. Finally, according to the results of qRT-PCR, the expression levels of the 20 unigenes tested were consistent with the results of next-generation sequencing. Conclusions In the present study, we identified a large set of cDNA unigenes from C. oleifera annotated using public databases. Further studies of DEGs involved in metabolic pathways related to drought stress and transcription will facilitate the discovery of novel genes involved in resistance to drought stress in this commercially important plant. PMID:28759610

Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia

PubMed Central

Ong, Edison; He, Yongqun

2016-01-01

Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To support ontology visualization, distribution, and community-based annotation and development, we have developed Ontokiwi, an ontology extension to the MediaWiki software. Ontokiwi displays hierarchical classes and ontological axioms. Ontology classes and axioms can be edited and added using Ontokiwi form or MediaWiki source editor. Ontokiwi also inherits MediaWiki features such as Wikitext editing and version control. Based on the Ontokiwi/MediaWiki software package, we have developed Ontobedia, which targets to support community-based development and annotations of biological and biomedical ontologies. As demonstrations, we have loaded the Ontology of Adverse Events (OAE) and the Cell Line Ontology (CLO) into Ontobedia. Our studies showed that Ontobedia was able to achieve expected Ontokiwi features. PMID:27570653

Defining functional distance using manifold embeddings of gene ontology annotations

PubMed Central

Lerman, Gilad; Shakhnovich, Boris E.

2007-01-01

Although rigorous measures of similarity for sequence and structure are now well established, the problem of defining functional relationships has been particularly daunting. Here, we present several manifold embedding techniques to compute distances between Gene Ontology (GO) functional annotations and consequently estimate functional distances between protein domains. To evaluate accuracy, we correlate the functional distance to the well established measures of sequence, structural, and phylogenetic similarities. Finally, we show that manual classification of structures into folds and superfamilies is mirrored by proximity in the newly defined function space. We show how functional distances place structure–function relationships in biological context resulting in insight into divergent and convergent evolution. The methods and results in this paper can be readily generalized and applied to a wide array of biologically relevant investigations, such as accuracy of annotation transference, the relationship between sequence, structure, and function, or coherence of expression modules. PMID:17595300

Nitrogen and Phosphorus in Water, An Annotated Selected Bibliography of Their Biological Effects.

ERIC Educational Resources Information Center

Mackenthun, Kenneth M.

Included in this bibliography are annotations of quantitative data on the content or concentration of nitrogen and phosphorus in plants and animals, the contribution to water of these elements from various sources (soil, fertilizers, excretion, sewage, precipitation, urban run-off), and the effect their presence has on aquatic standing crops and…

Perigone Lobe Transcriptome Analysis Provides Insights into Rafflesia cantleyi Flower Development.

PubMed

Lee, Xin-Wei; Mat-Isa, Mohd-Noor; Mohd-Elias, Nur-Atiqah; Aizat-Juhari, Mohd Afiq; Goh, Hoe-Han; Dear, Paul H; Chow, Keng-See; Haji Adam, Jumaat; Mohamed, Rahmah; Firdaus-Raih, Mohd; Wan, Kiew-Lian

2016-01-01

Rafflesia is a biologically enigmatic species that is very rare in occurrence and possesses an extraordinary morphology. This parasitic plant produces a gigantic flower up to one metre in diameter with no leaves, stem or roots. However, little is known about the floral biology of this species especially at the molecular level. In an effort to address this issue, we have generated and characterised the transcriptome of the Rafflesia cantleyi flower, and performed a comparison with the transcriptome of its floral bud to predict genes that are expressed and regulated during flower development. Approximately 40 million sequencing reads were generated and assembled de novo into 18,053 transcripts with an average length of 641 bp. Of these, more than 79% of the transcripts had significant matches to annotated sequences in the public protein database. A total of 11,756 and 7,891 transcripts were assigned to Gene Ontology categories and clusters of orthologous groups respectively. In addition, 6,019 transcripts could be mapped to 129 pathways in Kyoto Encyclopaedia of Genes and Genomes Pathway database. Digital abundance analysis identified 52 transcripts with very high expression in the flower transcriptome of R. cantleyi. Subsequently, analysis of differential expression between developing flower and the floral bud revealed a set of 105 transcripts with potential role in flower development. Our work presents a deep transcriptome resource analysis for the developing flower of R. cantleyi. Genes potentially involved in the growth and development of the R. cantleyi flower were identified and provide insights into biological processes that occur during flower development.

Genomic survey, expression profile and co-expression network analysis of OsWD40 family in rice

PubMed Central

2012-01-01

Background WD40 proteins represent a large family in eukaryotes, which have been involved in a broad spectrum of crucial functions. Systematic characterization and co-expression analysis of OsWD40 genes enable us to understand the networks of the WD40 proteins and their biological processes and gene functions in rice. Results In this study, we identify and analyze 200 potential OsWD40 genes in rice, describing their gene structures, genome localizations, and evolutionary relationship of each member. Expression profiles covering the whole life cycle in rice has revealed that transcripts of OsWD40 were accumulated differentially during vegetative and reproductive development and preferentially up or down-regulated in different tissues. Under phytohormone treatments, 25 OsWD40 genes were differentially expressed with treatments of one or more of the phytohormone NAA, KT, or GA3 in rice seedlings. We also used a combined analysis of expression correlation and Gene Ontology annotation to infer the biological role of the OsWD40 genes in rice. The results suggested that OsWD40 genes may perform their diverse functions by complex network, thus were predictive for understanding their biological pathways. The analysis also revealed that OsWD40 genes might interact with each other to take part in metabolic pathways, suggesting a more complex feedback network. Conclusions All of these analyses suggest that the functions of OsWD40 genes are diversified, which provide useful references for selecting candidate genes for further functional studies. PMID:22429805

Muscle Research and Gene Ontology: New standards for improved data integration.

PubMed

Feltrin, Erika; Campanaro, Stefano; Diehl, Alexander D; Ehler, Elisabeth; Faulkner, Georgine; Fordham, Jennifer; Gardin, Chiara; Harris, Midori; Hill, David; Knoell, Ralph; Laveder, Paolo; Mittempergher, Lorenza; Nori, Alessandra; Reggiani, Carlo; Sorrentino, Vincenzo; Volpe, Pompeo; Zara, Ivano; Valle, Giorgio; Deegan, Jennifer

2009-01-29

The Gene Ontology Project provides structured controlled vocabularies for molecular biology that can be used for the functional annotation of genes and gene products. In a collaboration between the Gene Ontology (GO) Consortium and the muscle biology community, we have made large-scale additions to the GO biological process and cellular component ontologies. The main focus of this ontology development work concerns skeletal muscle, with specific consideration given to the processes of muscle contraction, plasticity, development, and regeneration, and to the sarcomere and membrane-delimited compartments. Our aims were to update the existing structure to reflect current knowledge, and to resolve, in an accommodating manner, the ambiguity in the language used by the community. The updated muscle terminologies have been incorporated into the GO. There are now 159 new terms covering critical research areas, and 57 existing terms have been improved and reorganized to follow their usage in muscle literature. The revised GO structure should improve the interpretation of data from high-throughput (e.g. microarray and proteomic) experiments in the area of muscle science and muscle disease. We actively encourage community feedback on, and gene product annotation with these new terms. Please visit the Muscle Community Annotation Wiki http://wiki.geneontology.org/index.php/Muscle_Biology.

Collaborative web-based annotation of video footage of deep-sea life, ecosystems and geological processes

NASA Astrophysics Data System (ADS)

Kottmann, R.; Ratmeyer, V.; Pop Ristov, A.; Boetius, A.

2012-04-01

More and more seagoing scientific expeditions use video-controlled research platforms such as Remote Operating Vehicles (ROV), Autonomous Underwater Vehicles (AUV), and towed camera systems. These produce many hours of video material which contains detailed and scientifically highly valuable footage of the biological, chemical, geological, and physical aspects of the oceans. Many of the videos contain unique observations of unknown life-forms which are rare, and which cannot be sampled and studied otherwise. To make such video material online accessible and to create a collaborative annotation environment the "Video Annotation and processing platform" (V-App) was developed. A first solely web-based installation for ROV videos is setup at the German Center for Marine Environmental Sciences (available at http://videolib.marum.de). It allows users to search and watch videos with a standard web browser based on the HTML5 standard. Moreover, V-App implements social web technologies allowing a distributed world-wide scientific community to collaboratively annotate videos anywhere at any time. It has several features fully implemented among which are: • User login system for fine grained permission and access control • Video watching • Video search using keywords, geographic position, depth and time range and any combination thereof • Video annotation organised in themes (tracks) such as biology and geology among others in standard or full screen mode • Annotation keyword management: Administrative users can add, delete, and update single keywords for annotation or upload sets of keywords from Excel-sheets • Download of products for scientific use This unique web application system helps making costly ROV videos online available (estimated cost range between 5.000 - 10.000 Euros per hour depending on the combination of ship and ROV). Moreover, with this system each expert annotation adds instantaneous available and valuable knowledge to otherwise uncharted material.

RNA-Seq analysis and annotation of a draft blueberry genome assembly identifies candidate genes involved in fruit ripening, biosynthesis of bioactive compounds, and stage-specific alternative splicing.

PubMed

Gupta, Vikas; Estrada, April D; Blakley, Ivory; Reid, Rob; Patel, Ketan; Meyer, Mason D; Andersen, Stig Uggerhøj; Brown, Allan F; Lila, Mary Ann; Loraine, Ann E

2015-01-01

Blueberries are a rich source of antioxidants and other beneficial compounds that can protect against disease. Identifying genes involved in synthesis of bioactive compounds could enable the breeding of berry varieties with enhanced health benefits. Toward this end, we annotated a previously sequenced draft blueberry genome assembly using RNA-Seq data from five stages of berry fruit development and ripening. Genome-guided assembly of RNA-Seq read alignments combined with output from ab initio gene finders produced around 60,000 gene models, of which more than half were similar to proteins from other species, typically the grape Vitis vinifera. Comparison of gene models to the PlantCyc database of metabolic pathway enzymes identified candidate genes involved in synthesis of bioactive compounds, including bixin, an apocarotenoid with potential disease-fighting properties, and defense-related cyanogenic glycosides, which are toxic. Cyanogenic glycoside (CG) biosynthetic enzymes were highly expressed in green fruit, and a candidate CG detoxification enzyme was up-regulated during fruit ripening. Candidate genes for ethylene, anthocyanin, and 400 other biosynthetic pathways were also identified. Homology-based annotation using Blast2GO and InterPro assigned Gene Ontology terms to around 15,000 genes. RNA-Seq expression profiling showed that blueberry growth, maturation, and ripening involve dynamic gene expression changes, including coordinated up- and down-regulation of metabolic pathway enzymes and transcriptional regulators. Analysis of RNA-seq alignments identified developmentally regulated alternative splicing, promoter use, and 3' end formation. We report genome sequence, gene models, functional annotations, and RNA-Seq expression data that provide an important new resource enabling high throughput studies in blueberry.

Constructing Proteome Reference Map of the Porcine Jejunal Cell Line (IPEC-J2) by Label-Free Mass Spectrometry.

PubMed

Kim, Sang Hoon; Pajarillo, Edward Alain B; Balolong, Marilen P; Lee, Ji Yoon; Kang, Dae-Kyung

2016-06-28

In this study, the global proteome of the IPEC-J2 cell line was evaluated using ultra-high performance liquid chromatography coupled to a quadrupole Q Exactive™ Orbitrap mass spectrometer. Proteins were isolated from highly confluent IPEC-J2 cells in biological replicates and analyzed by label-free mass spectrometry prior to matching against a porcine genomic dataset. The results identified 1,517 proteins, accounting for 7.35% of all genes in the porcine genome. The highly abundant proteins detected, such as actin, annexin A2, and AHNAK nucleoprotein, are involved in structural integrity, signaling mechanisms, and cellular homeostasis. The high abundance of heat shock proteins indicated their significance in cellular defenses, barrier function, and gut homeostasis. Pathway analysis and annotation using the Kyoto Encyclopedia of Genes and Genomes database resulted in a putative protein network map of the regulation of immunological responses and structural integrity in the cell line. The comprehensive proteome analysis of IPEC-J2 cells provides fundamental insights into overall protein expression and pathway dynamics that might be useful in cell adhesion studies and immunological applications.

ASD: a comprehensive database of allosteric proteins and modulators

PubMed Central

Huang, Zhimin; Zhu, Liang; Cao, Yan; Wu, Geng; Liu, Xinyi; Chen, Yingyi; Wang, Qi; Shi, Ting; Zhao, Yaxue; Wang, Yuefei; Li, Weihua; Li, Yixue; Chen, Haifeng; Chen, Guoqiang; Zhang, Jian

2011-01-01

Allostery is the most direct, rapid and efficient way of regulating protein function, ranging from the control of metabolic mechanisms to signal-transduction pathways. However, an enormous amount of unsystematic allostery information has deterred scientists who could benefit from this field. Here, we present the AlloSteric Database (ASD), the first online database that provides a central resource for the display, search and analysis of structure, function and related annotation for allosteric molecules. Currently, ASD contains 336 allosteric proteins from 101 species and 8095 modulators in three categories (activators, inhibitors and regulators). Proteins are annotated with a detailed description of allostery, biological process and related diseases, and modulators with binding affinity, physicochemical properties and therapeutic area. Integrating the information of allosteric proteins in ASD should allow for the identification of specific allosteric sites of a given subtype among proteins of the same family that can potentially serve as ideal targets for experimental validation. In addition, modulators curated in ASD can be used to investigate potent allosteric targets for the query compound, and also help chemists to implement structure modifications for novel allosteric drug design. Therefore, ASD could be a platform and a starting point for biologists and medicinal chemists for furthering allosteric research. ASD is freely available at http://mdl.shsmu.edu.cn/ASD/. PMID:21051350

Concordant gene regulation related to perturbations of three GDP-mannose-related genes.

PubMed

Törmä, Anssi; Pitkänen, Juha-Pekka; Huopaniemi, Laura; Mattila, Pirkko; Renkonen, Risto

2009-02-01

Glycosylation of proteins is one of the most crucial post-translational modifications. In order to access system-level and state-dependent data related to the regulation of glycosylation events, we cultivated yeast cell strains each harboring a selected conditional knockdown construct for a gene (either SEC53, VRG4 or DPM1) related to GDP-mannose synthesis or its utilization in glycan biosynthesis. In order to carry this out efficiently, we developed automated sampling from bioreactor cultivations, a collection of in silico workflows for data analysis as well as their integration into a large data warehouse. Using the above-mentioned approaches, we could show that conditional knocking down of transcripts related to GDP-mannose synthesis or transportation led to altered levels of over 300 transcripts. These transcripts and their corresponding proteins were characterized by their gene ontology (GO) annotations, and their putative transcriptional regulation was analyzed. Furthermore, novel pathways were generated indicating interactions between GO categories with common proteins, putative transcriptional regulators of such induced GO categories, and the large protein-protein interaction network among the proteins whose transcripts indicated altered expression levels. When these results are always added to an ever-expanding data warehouse as annotations, they will incrementally increase the knowledge of biological systems.

Chemical annotation of small and peptide-like molecules at the Protein Data Bank

PubMed Central

Young, Jasmine Y.; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M.

2013-01-01

Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org PMID:24291661

Chemical annotation of small and peptide-like molecules at the Protein Data Bank.

PubMed

Young, Jasmine Y; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M

2013-01-01

Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org.

Propagating annotations of molecular networks using in silico fragmentation

PubMed Central

da Silva, Ricardo R.; Wang, Mingxun; Fox, Evan; Balunas, Marcy J.; Klassen, Jonathan L.; Dorrestein, Pieter C.

2018-01-01

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp. PMID:29668671

Gene calling and bacterial genome annotation with BG7.

PubMed

Tobes, Raquel; Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Kovach, Evdokim; Alekhin, Alexey; Pareja, Eduardo

2015-01-01

New massive sequencing technologies are providing many bacterial genome sequences from diverse taxa but a refined annotation of these genomes is crucial for obtaining scientific findings and new knowledge. Thus, bacterial genome annotation has emerged as a key point to investigate in bacteria. Any efficient tool designed specifically to annotate bacterial genomes sequenced with massively parallel technologies has to consider the specific features of bacterial genomes (absence of introns and scarcity of nonprotein-coding sequence) and of next-generation sequencing (NGS) technologies (presence of errors and not perfectly assembled genomes). These features make it convenient to focus on coding regions and, hence, on protein sequences that are the elements directly related with biological functions. In this chapter we describe how to annotate bacterial genomes with BG7, an open-source tool based on a protein-centered gene calling/annotation paradigm. BG7 is specifically designed for the annotation of bacterial genomes sequenced with NGS. This tool is sequence error tolerant maintaining their capabilities for the annotation of highly fragmented genomes or for annotating mixed sequences coming from several genomes (as those obtained through metagenomics samples). BG7 has been designed with scalability as a requirement, with a computing infrastructure completely based on cloud computing (Amazon Web Services).

Propagating annotations of molecular networks using in silico fragmentation.

PubMed

da Silva, Ricardo R; Wang, Mingxun; Nothias, Louis-Félix; van der Hooft, Justin J J; Caraballo-Rodríguez, Andrés Mauricio; Fox, Evan; Balunas, Marcy J; Klassen, Jonathan L; Lopes, Norberto Peporine; Dorrestein, Pieter C

2018-04-01

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

Prediction of gene expression in embryonic structures of Drosophila melanogaster.

PubMed

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-07-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

PubMed Central

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-01-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. PMID:17658945

Tropical Biological Drawings with Notes.

ERIC Educational Resources Information Center

Mitchelmore, June A.

The annotated illustrations of biological specimens useful for illustrating the "tropical" topics dealt with in African secondary school biology courses are designed to serve a two-fold purpose. The diagrams are intended to show the pupil the structures he should be looking for in his laboratory work, with the textual material being an…

Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis

PubMed Central

Li, Dapeng; Baldwin, Ian T.; Gaquerel, Emmanuel

2015-01-01

Natural variation can be extremely useful in unraveling the determinants of phenotypic trait evolution but has rarely been analyzed with unbiased metabolic profiling to understand how its effects are organized at the level of biochemical pathways. Native populations of Nicotiana attenuata, a wild tobacco species, have been shown to be highly genetically diverse for traits important for their interactions with insects. To resolve the chemodiversity existing in these populations, we developed a metabolomics and computational pipeline to annotate leaf metabolic responses to Manduca sexta herbivory. We selected seeds from 43 accessions of different populations from the southwestern United States—including the well-characterized Utah 30th generation inbred accession—and grew 183 plants in the glasshouse for standardized herbivory elicitation. Metabolic profiles were generated from elicited leaves of each plant using a high-throughput ultra HPLC (UHPLC)-quadrupole TOFMS (qTOFMS) method, processed to systematically infer covariation patterns among biochemically related metabolites, as well as unknown ones, and finally assembled to map natural variation. Navigating this map revealed metabolic branch-specific variations that surprisingly only partly overlapped with jasmonate accumulation polymorphisms and deviated from canonical jasmonate signaling. Fragmentation analysis via indiscriminant tandem mass spectrometry (idMS/MS) was conducted with 10 accessions that spanned a large proportion of the variance found in the complete accession dataset, and compound spectra were computationally assembled into spectral similarity networks. The biological information captured by this networking approach facilitates the mining of the mass spectral data of unknowns with high natural variation, as demonstrated by the annotation of a strongly herbivory-inducible phenolic derivative, and can guide pathway analysis. PMID:26170304

Deep RNA-Seq to unlock the gene bank of floral development in Sinapis arvensis.

PubMed

Liu, Jia; Mei, Desheng; Li, Yunchang; Huang, Shunmou; Hu, Qiong

2014-01-01

Sinapis arvensis is a weed with strong biological activity. Despite being a problematic annual weed that contaminates agricultural crop yield, it is a valuable alien germplasm resource. It can be utilized for broadening the genetic background of Brassica crops with desirable agricultural traits like resistance to blackleg (Leptosphaeria maculans), stem rot (Sclerotinia sclerotium) and pod shatter (caused by FRUITFULL gene). However, few genetic studies of S. arvensis were reported because of the lack of genomic resources. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive dataset for S. arvensis for the first time. We used Illumina paired-end sequencing technology to sequence the S. arvensis flower transcriptome and generated 40,981,443 reads that were assembled into 131,278 transcripts. We de novo assembled 96,562 high quality unigenes with an average length of 832 bp. A total of 33,662 full-length ORF complete sequences were identified, and 41,415 unigenes were mapped onto 128 pathways using the KEGG Pathway database. The annotated unigenes were compared against Brassica rapa, B. oleracea, B. napus and Arabidopsis thaliana. Among these unigenes, 76,324 were identified as putative homologs of annotated sequences in the public protein databases, of which 1194 were associated with plant hormone signal transduction and 113 were related to gibberellin homeostasis/signaling. Unigenes that did not match any of those sequence datasets were considered to be unique to S. arvensis. Furthermore, 21,321 simple sequence repeats were found. Our study will enhance the currently available resources for Brassicaceae and will provide a platform for future genomic studies for genetic improvement of Brassica crops.

Genome sequences of Chlorella sorokiniana UTEX 1602 and Micractinium conductrix SAG 241.80: implications to maltose excretion by a green alga.

PubMed

Arriola, Matthew B; Velmurugan, Natarajan; Zhang, Ying; Plunkett, Mary H; Hondzo, Hanna; Barney, Brett M

2018-02-01

Green algae represent a key segment of the global species capable of photoautotrophic-driven biological carbon fixation. Algae partition fixed-carbon into chemical compounds required for biomass, while diverting excess carbon into internal storage compounds such as starch and lipids or, in certain cases, into targeted extracellular compounds. Two green algae were selected to probe for critical components associated with sugar production and release in a model alga. Chlorella sorokiniana UTEX 1602 - which does not release significant quantities of sugars to the extracellular space - was selected as a control to compare with the maltose-releasing Micractinium conductrix SAG 241.80 - which was originally isolated from an endosymbiotic association with the ciliate Paramecium bursaria. Both strains were subjected to three sequencing approaches to assemble their genomes and annotate their genes. This analysis was further complemented with transcriptional studies during maltose release by M. conductrix SAG 241.80 versus conditions where sugar release is minimal. The annotation revealed that both strains contain homologs for the key components of a putative pathway leading to cytosolic maltose accumulation, while transcriptional studies found few changes in mRNA levels for the genes associated with these established intracellular sugar pathways. A further analysis of potential sugar transporters found multiple homologs for SWEETs and tonoplast sugar transporters. The analysis of transcriptional differences revealed a lesser and more measured global response for M. conductrix SAG 241.80 versus C. sorokiniana UTEX 1602 during conditions resulting in sugar release, providing a catalog of genes that might play a role in extracellular sugar transport. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Deep RNA-Seq to Unlock the Gene Bank of Floral Development in Sinapis arvensis

PubMed Central

Liu, Jia; Mei, Desheng; Li, Yunchang; Huang, Shunmou; Hu, Qiong

2014-01-01

Sinapis arvensis is a weed with strong biological activity. Despite being a problematic annual weed that contaminates agricultural crop yield, it is a valuable alien germplasm resource. It can be utilized for broadening the genetic background of Brassica crops with desirable agricultural traits like resistance to blackleg (Leptosphaeria maculans), stem rot (Sclerotinia sclerotium) and pod shatter (caused by FRUITFULL gene). However, few genetic studies of S. arvensis were reported because of the lack of genomic resources. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive dataset for S. arvensis for the first time. We used Illumina paired-end sequencing technology to sequence the S. arvensis flower transcriptome and generated 40,981,443 reads that were assembled into 131,278 transcripts. We de novo assembled 96,562 high quality unigenes with an average length of 832 bp. A total of 33,662 full-length ORF complete sequences were identified, and 41,415 unigenes were mapped onto 128 pathways using the KEGG Pathway database. The annotated unigenes were compared against Brassica rapa, B. oleracea, B. napus and Arabidopsis thaliana. Among these unigenes, 76,324 were identified as putative homologs of annotated sequences in the public protein databases, of which 1194 were associated with plant hormone signal transduction and 113 were related to gibberellin homeostasis/signaling. Unigenes that did not match any of those sequence datasets were considered to be unique to S. arvensis. Furthermore, 21,321 simple sequence repeats were found. Our study will enhance the currently available resources for Brassicaceae and will provide a platform for future genomic studies for genetic improvement of Brassica crops. PMID:25192023

Osmoregulation in the Halophilic Bacterium Halomonas elongata: A Case Study for Integrative Systems Biology.

PubMed

Kindzierski, Viktoria; Raschke, Silvia; Knabe, Nicole; Siedler, Frank; Scheffer, Beatrix; Pflüger-Grau, Katharina; Pfeiffer, Friedhelm; Oesterhelt, Dieter; Marin-Sanguino, Alberto; Kunte, Hans-Jörg

2017-01-01

Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem. However, as more of these reconstructions become available, it becomes clear that processes predicted by genome annotation may not reflect the processes that are active in vivo. As a case in point, E. coli is unable to grow aerobically on citrate in spite of having all the necessary genes to do it. It has also been shown that the realization of this genetic potential into an actual capability to metabolize citrate is an extremely unlikely event under normal evolutionary conditions. Moreover, many marine bacteria seem to have the same pathways to metabolize glucose but each species uses a different one. In this work, a metabolic network inferred from genomic annotation of the halophilic bacterium Halomonas elongata and proteomic profiling experiments are used as a starting point to motivate targeted experiments in order to find out some of the defining features of the osmoregulatory strategies of this bacterium. This new information is then used to refine the network in order to describe the actual capabilities of H. elongata, rather than its genetic potential.

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

PubMed

Uppal, Karan; Soltow, Quinlyn A; Strobel, Frederick H; Pittard, W Stephen; Gernert, Kim M; Yu, Tianwei; Jones, Dean P

2013-01-16

Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

An optimization model for metabolic pathways.

PubMed

Planes, F J; Beasley, J E

2009-10-15

Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

Modeling central metabolism and energy biosynthesis across microbial life

DOE PAGES

Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal; ...

2016-08-08

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life.

PubMed

Edirisinghe, Janaka N; Weisenhorn, Pamela; Conrad, Neal; Xia, Fangfang; Overbeek, Ross; Stevens, Rick L; Henry, Christopher S

2016-08-08

Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Gut microbiome may contribute to insulin resistance and systemic inflammation in obese rodents: a meta-analysis.

PubMed

Jiao, Na; Baker, Susan S; Nugent, Colleen A; Tsompana, Maria; Cai, Liting; Wang, Yong; Buck, Michael J; Genco, Robert J; Baker, Robert D; Zhu, Ruixin; Zhu, Lixin

2018-04-01

A number of studies have associated obesity with altered gut microbiota, although results are discordant regarding compositional changes in the gut microbiota of obese animals. Herein we used a meta-analysis to obtain an unbiased evaluation of structural and functional changes of the gut microbiota in diet-induced obese rodents. The raw sequencing data of nine studies generated from high-fat diet (HFD)-induced obese rodent models were processed with QIIME to obtain gut microbiota compositions. Biological functions were predicted and annotated with KEGG pathways with PICRUSt. No significant difference was observed for alpha diversity and Bacteroidetes-to-Firmicutes ratio between obese and lean rodents. Bacteroidia, Clostridia, Bacilli, and Erysipelotrichi were dominant classes, but gut microbiota compositions varied among studies. Meta-analysis of the nine microbiome data sets identified 15 differential taxa and 57 differential pathways between obese and lean rodents. In obese rodents, increased abundance was observed for Dorea, Oscillospira, and Ruminococcus, known for fermenting polysaccharide into short chain fatty acids (SCFAs). Decreased Turicibacter and increased Lactococcus are consistent with elevated inflammation in the obese status. Differential functional pathways of the gut microbiome in obese rodents included enriched pyruvate metabolism, butanoate metabolism, propanoate metabolism, pentose phosphate pathway, fatty acid biosynthesis, and glycerolipid metabolism pathways. These pathways converge in the function of carbohydrate metabolism, SCFA metabolism, and biosynthesis of lipid. HFD-induced obesity results in structural and functional dysbiosis of gut microbiota. The altered gut microbiome may contribute to obesity development by promoting insulin resistance and systemic inflammation.

ChemBrowser: a flexible framework for mining chemical documents.

PubMed

Wu, Xian; Zhang, Li; Chen, Ying; Rhodes, James; Griffin, Thomas D; Boyer, Stephen K; Alba, Alfredo; Cai, Keke

2010-01-01

The ability to extract chemical and biological entities and relations from text documents automatically has great value to biochemical research and development activities. The growing maturity of text mining and artificial intelligence technologies shows promise in enabling such automatic chemical entity extraction capabilities (called "Chemical Annotation" in this paper). Many techniques have been reported in the literature, ranging from dictionary and rule-based techniques to machine learning approaches. In practice, we found that no single technique works well in all cases. A combinatorial approach that allows one to quickly compose different annotation techniques together for a given situation is most effective. In this paper, we describe the key challenges we face in real-world chemical annotation scenarios. We then present a solution called ChemBrowser which has a flexible framework for chemical annotation. ChemBrowser includes a suite of customizable processing units that might be utilized in a chemical annotator, a high-level language that describes the composition of various processing units that would form a chemical annotator, and an execution engine that translates the composition language to an actual annotator that can generate annotation results for a given set of documents. We demonstrate the impact of this approach by tailoring an annotator for extracting chemical names from patent documents and show how this annotator can be easily modified with simple configuration alone.

Genomics and expression profiles of the Hedgehog and Notch signaling pathways in sea urchin development.

PubMed

Walton, Katherine D; Croce, Jenifer C; Glenn, Thomas D; Wu, Shu-Yu; McClay, David R

2006-12-01

The Hedgehog (Hh) and Notch signal transduction pathways control a variety of developmental processes including cell fate choice, differentiation, proliferation, patterning and boundary formation. Because many components of these pathways are conserved, it was predicted and confirmed that pathway components are largely intact in the sea urchin genome. Spatial and temporal location of these pathways in the embryo, and their function in development offer added insight into their mechanistic contributions. Accordingly, all major components of both pathways were identified and annotated in the sea urchin Strongylocentrotus purpuratus genome and the embryonic expression of key components was explored. Relationships of the pathway components, and modifiers predicted from the annotation of S. purpuratus, were compared against cnidarians, arthropods, urochordates, and vertebrates. These analyses support the prediction that the pathways are highly conserved through metazoan evolution. Further, the location of these two pathways appears to be conserved among deuterostomes, and in the case of Notch at least, display similar capacities in endomesoderm gene regulatory networks. RNA expression profiles by quantitative PCR and RNA in situ hybridization reveal that Hedgehog is produced by the endoderm beginning just prior to invagination, and signals to the secondary mesenchyme-derived tissues at least until the pluteus larva stage. RNA in situ hybridization of Notch pathway members confirms that Notch functions sequentially in the vegetal-most secondary mesenchyme cells and later in the endoderm. Functional analyses in future studies will embed these pathways into the growing knowledge of gene regulatory networks that govern early specification and morphogenesis.

Looking at the stability of life-support microorganisms in space : the MELGEN activity highlights the cyanobacterium Arthrospira sp. PCC8005

NASA Astrophysics Data System (ADS)

Morin, Nicolas

The MELGEN activity (MELiSSA Genetic Stability Study) mainly covers the molecular aspects of the regenerative life-support system MELiSSA (Micro-Ecological Life Support System Alternative) of the European Space Agency (ESA). The general objective of MELGEN is to establish and validate methods and the related hardware in order to detect genetic instability and microbial contaminants in the MELISSA compartments. This includes (1) a genetic description of the MELISSA strains, (2) studies of microbial behavior and genetic stability in bioreactors and (3) the detection of chemical, genetical and biological contamination and their effect on microbial metabolism. Selected as oxygen producer and complementary food source, the cyanobacterium Arthrospira sp. PCC8005 plays a major role within the MELiSSA loop. As the genomic information on this organism was insufficient, sequencing of its genome was proposed at the French National Sequencing Center, Genoscope, as a joint effort between ESA and different laboratories. So far, a preliminary assembly of 16 contigs representing circa 6.3 million basepairs was obtained. Even though the finishing of the genome is on its way, automatic annotation of the contigs has already been performed on the MaGe annotation platform, and curation of the sequence is currently being carried out, with a special focus on biosynthesis pathways, photosynthesis, and maintenance processes of the cell. According to the index of repetitiveness described by Haubold and Wiehe (2006), we discovered that the genome of Arthrospira sp. is among the 50 most repeated bacterial genomes sequenced to date. Thanks to the sequencing project, we have identified and catalogued mobile genetics elements (MGEs) dispersed throughout the unique chromosome of this cyanobacterium. They represent a quite large proportion of the genome, as genes identified as putative transposases are indeed found in circa 5 Results : We currently have a first draft of the complete genome of Arthrospira sp. PCC 8005, fully annotated. This genomic information opens the gates to a better understanding of the biology of this cyanobacterium and will be a key to the development of appropriate derivatives that provide enhanced performances (e.g. radiation resistance, genetic stability, photosynthesis and nutritive properties).

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system.

PubMed

Chen, I-Min A; Markowitz, Victor M; Palaniappan, Krishna; Szeto, Ernest; Chu, Ken; Huang, Jinghua; Ratner, Anna; Pillay, Manoj; Hadjithomas, Michalis; Huntemann, Marcel; Mikhailova, Natalia; Ovchinnikova, Galina; Ivanova, Natalia N; Kyrpides, Nikos C

2016-04-26

The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existing IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.

snpGeneSets: An R Package for Genome-Wide Study Annotation

PubMed Central

Mei, Hao; Li, Lianna; Jiang, Fan; Simino, Jeannette; Griswold, Michael; Mosley, Thomas; Liu, Shijian

2016-01-01

Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/. PMID:27807048

Bio-crude transcriptomics: gene discovery and metabolic network reconstruction for the biosynthesis of the terpenome of the hydrocarbon oil-producing green alga, Botryococcus braunii race B (Showa).

PubMed

Molnár, István; Lopez, David; Wisecaver, Jennifer H; Devarenne, Timothy P; Weiss, Taylor L; Pellegrini, Matteo; Hackett, Jeremiah D

2012-10-30

Microalgae hold promise for yielding a biofuel feedstock that is sustainable, carbon-neutral, distributed, and only minimally disruptive for the production of food and feed by traditional agriculture. Amongst oleaginous eukaryotic algae, the B race of Botryococcus braunii is unique in that it produces large amounts of liquid hydrocarbons of terpenoid origin. These are comparable to fossil crude oil, and are sequestered outside the cells in a communal extracellular polymeric matrix material. Biosynthetic engineering of terpenoid bio-crude production requires identification of genes and reconstruction of metabolic pathways responsible for production of both hydrocarbons and other metabolites of the alga that compete for photosynthetic carbon and energy. A de novo assembly of 1,334,609 next-generation pyrosequencing reads form the Showa strain of the B race of B. braunii yielded a transcriptomic database of 46,422 contigs with an average length of 756 bp. Contigs were annotated with pathway, ontology, and protein domain identifiers. Manual curation allowed the reconstruction of pathways that produce terpenoid liquid hydrocarbons from primary metabolites, and pathways that divert photosynthetic carbon into tetraterpenoid carotenoids, diterpenoids, and the prenyl chains of meroterpenoid quinones and chlorophyll. Inventories of machine-assembled contigs are also presented for reconstructed pathways for the biosynthesis of competing storage compounds including triacylglycerol and starch. Regeneration of S-adenosylmethionine, and the extracellular localization of the hydrocarbon oils by active transport and possibly autophagy are also investigated. The construction of an annotated transcriptomic database, publicly available in a web-based data depository and annotation tool, provides a foundation for metabolic pathway and network reconstruction, and facilitates further omics studies in the absence of a genome sequence for the Showa strain of B. braunii, race B. Further, the transcriptome database empowers future biosynthetic engineering approaches for strain improvement and the transfer of desirable traits to heterologous hosts.

Bio-crude transcriptomics: Gene discovery and metabolic network reconstruction for the biosynthesis of the terpenome of the hydrocarbon oil-producing green alga, Botryococcus braunii race B (Showa)*

PubMed Central

2012-01-01

Background Microalgae hold promise for yielding a biofuel feedstock that is sustainable, carbon-neutral, distributed, and only minimally disruptive for the production of food and feed by traditional agriculture. Amongst oleaginous eukaryotic algae, the B race of Botryococcus braunii is unique in that it produces large amounts of liquid hydrocarbons of terpenoid origin. These are comparable to fossil crude oil, and are sequestered outside the cells in a communal extracellular polymeric matrix material. Biosynthetic engineering of terpenoid bio-crude production requires identification of genes and reconstruction of metabolic pathways responsible for production of both hydrocarbons and other metabolites of the alga that compete for photosynthetic carbon and energy. Results A de novo assembly of 1,334,609 next-generation pyrosequencing reads form the Showa strain of the B race of B. braunii yielded a transcriptomic database of 46,422 contigs with an average length of 756 bp. Contigs were annotated with pathway, ontology, and protein domain identifiers. Manual curation allowed the reconstruction of pathways that produce terpenoid liquid hydrocarbons from primary metabolites, and pathways that divert photosynthetic carbon into tetraterpenoid carotenoids, diterpenoids, and the prenyl chains of meroterpenoid quinones and chlorophyll. Inventories of machine-assembled contigs are also presented for reconstructed pathways for the biosynthesis of competing storage compounds including triacylglycerol and starch. Regeneration of S-adenosylmethionine, and the extracellular localization of the hydrocarbon oils by active transport and possibly autophagy are also investigated. Conclusions The construction of an annotated transcriptomic database, publicly available in a web-based data depository and annotation tool, provides a foundation for metabolic pathway and network reconstruction, and facilitates further omics studies in the absence of a genome sequence for the Showa strain of B. braunii, race B. Further, the transcriptome database empowers future biosynthetic engineering approaches for strain improvement and the transfer of desirable traits to heterologous hosts. PMID:23110428

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa.

PubMed

Nag, Ambarish; Karpinets, Tatiana V; Chang, Christopher H; Bar-Peled, Maor

2012-01-01

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa

PubMed Central

Nag, Ambarish; Karpinets, Tatiana V.; Chang, Christopher H.; Bar-Peled, Maor

2012-01-01

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES. PMID:22465851

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations.

PubMed

Agapito, Giuseppe; Milano, Marianna; Guzzi, Pietro Hiram; Cannataro, Mario

2016-01-01

Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

PubMed

Dowell, Karen G; Simons, Allen K; Bai, Hao; Kell, Braden; Wang, Zack Z; Yun, Kyuson; Hibbs, Matthew A

2014-05-01

Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation. © 2013 AlphaMed Press.

Towards an informative mutant phenotype for every bacterial gene

DOE PAGES

Deutschbauer, Adam; Price, Morgan N.; Wetmore, Kelly M.; ...

2014-08-11

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, inmore » Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.« less

Automatic extraction of angiogenesis bioprocess from text

PubMed Central

Wang, Xinglong; McKendrick, Iain; Barrett, Ian; Dix, Ian; French, Tim; Tsujii, Jun'ichi; Ananiadou, Sophia

2011-01-01

Motivation: Understanding key biological processes (bioprocesses) and their relationships with constituent biological entities and pharmaceutical agents is crucial for drug design and discovery. One way to harvest such information is searching the literature. However, bioprocesses are difficult to capture because they may occur in text in a variety of textual expressions. Moreover, a bioprocess is often composed of a series of bioevents, where a bioevent denotes changes to one or a group of cells involved in the bioprocess. Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find. Results: This article presents a range of methods for finding bioprocess terms and events. To facilitate the study, we built a gold standard corpus in which terms and events related to angiogenesis, a key biological process of the growth of new blood vessels, were annotated. Statistics of the annotated corpus revealed that over 36% of the text expressions that referred to angiogenesis appeared as events. The proposed methods respectively employed domain-specific vocabularies, a manually annotated corpus and unstructured domain-specific documents. Evaluation results showed that, while a supervised machine-learning model yielded the best precision, recall and F1 scores, the other methods achieved reasonable performance and less cost to develop. Availability: The angiogenesis vocabularies, gold standard corpus, annotation guidelines and software described in this article are available at http://text0.mib.man.ac.uk/~mbassxw2/angiogenesis/ Contact: xinglong.wang@gmail.com PMID:21821664

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deutschbauer, Adam; Price, Morgan N.; Wetmore, Kelly M.

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, inmore » Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.« less

Genome-Level Longitudinal Expression of Signaling Pathways and Gene Networks in Pediatric Septic Shock

PubMed Central

Shanley, Thomas P; Cvijanovich, Natalie; Lin, Richard; Allen, Geoffrey L; Thomas, Neal J; Doctor, Allan; Kalyanaraman, Meena; Tofil, Nancy M; Penfil, Scott; Monaco, Marie; Odoms, Kelli; Barnes, Michael; Sakthivel, Bhuvaneswari; Aronow, Bruce J; Wong, Hector R

2007-01-01

We have conducted longitudinal studies focused on the expression profiles of signaling pathways and gene networks in children with septic shock. Genome-level expression profiles were generated from whole blood-derived RNA of children with septic shock (n = 30) corresponding to day one and day three of septic shock, respectively. Based on sequential statistical and expression filters, day one and day three of septic shock were characterized by differential regulation of 2,142 and 2,504 gene probes, respectively, relative to controls (n = 15). Venn analysis demonstrated 239 unique genes in the day one dataset, 598 unique genes in the day three dataset, and 1,906 genes common to both datasets. Functional analyses demonstrated time-dependent, differential regulation of genes involved in multiple signaling pathways and gene networks primarily related to immunity and inflammation. Notably, multiple and distinct gene networks involving T cell- and MHC antigen-related biology were persistently downregulated on both day one and day three. Further analyses demonstrated large scale, persistent downregulation of genes corresponding to functional annotations related to zinc homeostasis. These data represent the largest reported cohort of patients with septic shock subjected to longitudinal genome-level expression profiling. The data further advance our genome-level understanding of pediatric septic shock and support novel hypotheses. PMID:17932561

The systematic annotation of the three main GPCR families in Reactome.

PubMed

Jassal, Bijay; Jupe, Steven; Caudy, Michael; Birney, Ewan; Stein, Lincoln; Hermjakob, Henning; D'Eustachio, Peter

2010-07-29

Reactome is an open-source, freely available database of human biological pathways and processes. A major goal of our work is to provide an integrated view of cellular signalling processes that spans from ligand-receptor interactions to molecular readouts at the level of metabolic and transcriptional events. To this end, we have built the first catalogue of all human G protein-coupled receptors (GPCRs) known to bind endogenous or natural ligands. The UniProt database has records for 797 proteins classified as GPCRs and sorted into families A/1, B/2 and C/3 on the basis of amino acid sequence. To these records we have added details from the IUPHAR database and our own manual curation of relevant literature to create reactions in which 563 GPCRs bind ligands and also interact with specific G-proteins to initiate signalling cascades. We believe the remaining 234 GPCRs are true orphans. The Reactome GPCR pathway can be viewed as a detailed interactive diagram and can be exported in many forms. It provides a template for the orthology-based inference of GPCR reactions for diverse model organism species, and can be overlaid with protein-protein interaction and gene expression datasets to facilitate overrepresentation studies and other forms of pathway analysis. Database URL: http://www.reactome.org.

Identification of critical paralog groups with indispensable roles in the regulation of signaling flow

PubMed Central

Modos, Dezso; Brooks, Johanne; Fazekas, David; Ari, Eszter; Vellai, Tibor; Csermely, Peter; Korcsmaros, Tamas; Lenti, Katalin

2016-01-01

Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis. PMID:27922122

SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data.

PubMed

Venkatesan, Aravind; Kim, Jee-Hyub; Talo, Francesco; Ide-Smith, Michele; Gobeill, Julien; Carter, Jacob; Batista-Navarro, Riza; Ananiadou, Sophia; Ruch, Patrick; McEntyre, Johanna

2016-01-01

The tremendous growth in biological data has resulted in an increase in the number of research papers being published. This presents a great challenge for scientists in searching and assimilating facts described in those papers. Particularly, biological databases depend on curators to add highly precise and useful information that are usually extracted by reading research articles. Therefore, there is an urgent need to find ways to improve linking literature to the underlying data, thereby minimising the effort in browsing content and identifying key biological concepts.   As part of the development of Europe PMC, we have developed a new platform, SciLite, which integrates text-mined annotations from different sources and overlays those outputs on research articles. The aim is to aid researchers and curators using Europe PMC in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data.

SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data

PubMed Central

Talo, Francesco; Ide-Smith, Michele; Gobeill, Julien; Carter, Jacob; Batista-Navarro, Riza; Ananiadou, Sophia; Ruch, Patrick; McEntyre, Johanna

2017-01-01

The tremendous growth in biological data has resulted in an increase in the number of research papers being published. This presents a great challenge for scientists in searching and assimilating facts described in those papers. Particularly, biological databases depend on curators to add highly precise and useful information that are usually extracted by reading research articles. Therefore, there is an urgent need to find ways to improve linking literature to the underlying data, thereby minimising the effort in browsing content and identifying key biological concepts.   As part of the development of Europe PMC, we have developed a new platform, SciLite, which integrates text-mined annotations from different sources and overlays those outputs on research articles. The aim is to aid researchers and curators using Europe PMC in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data. PMID:28948232

Sma3s: a three-step modular annotator for large sequence datasets.

PubMed

Muñoz-Mérida, Antonio; Viguera, Enrique; Claros, M Gonzalo; Trelles, Oswaldo; Pérez-Pulido, Antonio J

2014-08-01

Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

A Putative Gene Cluster from a Lyngbya wollei Bloom that Encodes Paralytic Shellfish Toxin Biosynthesis

PubMed Central

Mihali, Troco K.; Carmichael, Wayne W.; Neilan, Brett A.

2011-01-01

Saxitoxin and its analogs cause the paralytic shellfish-poisoning syndrome, adversely affecting human health and coastal shellfish industries worldwide. Here we report the isolation, sequencing, annotation, and predicted pathway of the saxitoxin biosynthetic gene cluster in the cyanobacterium Lyngbya wollei. The gene cluster spans 36 kb and encodes enzymes for the biosynthesis and export of the toxins. The Lyngbya wollei saxitoxin gene cluster differs from previously identified saxitoxin clusters as it contains genes that are unique to this cluster, whereby the carbamoyltransferase is truncated and replaced by an acyltransferase, explaining the unique toxin profile presented by Lyngbya wollei. These findings will enable the creation of toxin probes, for water monitoring purposes, as well as proof-of-concept for the combinatorial biosynthesis of these natural occurring alkaloids for the production of novel, biologically active compounds. PMID:21347365

ChemProt-2.0: visual navigation in a disease chemical biology database

PubMed Central

Kim Kjærulff, Sonny; Wich, Louis; Kringelum, Jens; Jacobsen, Ulrik P.; Kouskoumvekaki, Irene; Audouze, Karine; Lund, Ole; Brunak, Søren; Oprea, Tudor I.; Taboureau, Olivier

2013-01-01

ChemProt-2.0 (http://www.cbs.dtu.dk/services/ChemProt-2.0) is a public available compilation of multiple chemical–protein annotation resources integrated with diseases and clinical outcomes information. The database has been updated to >1.15 million compounds with 5.32 millions bioactivity measurements for 15 290 proteins. Each protein is linked to quality-scored human protein–protein interactions data based on more than half a million interactions, for studying diseases and biological outcomes (diseases, pathways and GO terms) through protein complexes. In ChemProt-2.0, therapeutic effects as well as adverse drug reactions have been integrated allowing for suggesting proteins associated to clinical outcomes. New chemical structure fingerprints were computed based on the similarity ensemble approach. Protein sequence similarity search was also integrated to evaluate the promiscuity of proteins, which can help in the prediction of off-target effects. Finally, the database was integrated into a visual interface that enables navigation of the pharmacological space for small molecules. Filtering options were included in order to facilitate and to guide dynamic search of specific queries. PMID:23185041

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Genome-wide DNA methylation patterns in wild samples of two morphotypes of threespine stickleback (Gasterosteus aculeatus).

PubMed

Smith, Gilbert; Smith, Carl; Kenny, John G; Chaudhuri, Roy R; Ritchie, Michael G

2015-04-01

Epigenetic marks such as DNA methylation play important biological roles in gene expression regulation and cellular differentiation during development. To examine whether DNA methylation patterns are potentially associated with naturally occurring phenotypic differences, we examined genome-wide DNA methylation within Gasterosteus aculeatus, using reduced representation bisulfite sequencing. First, we identified highly methylated regions of the stickleback genome, finding such regions to be located predominantly within genes, and associated with genes functioning in metabolism and biosynthetic processes, cell adhesion, signaling pathways, and blood vessel development. Next, we identified putative differentially methylated regions (DMRs) of the genome between complete and low lateral plate morphs of G. aculeatus. We detected 77 DMRs that were mainly located in intergenic regions. Annotations of genes associated with these DMRs revealed potential functions in a number of known divergent adaptive phenotypes between G. aculeatus ecotypes, including cardiovascular development, growth, and neuromuscular development. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Issues with RNA-seq analysis in non-model organisms: A salmonid example.

PubMed

Sundaram, Arvind; Tengs, Torstein; Grimholt, Unni

2017-10-01

High throughput sequencing (HTS) is useful for many purposes as exemplified by the other topics included in this special issue. The purpose of this paper is to look into the unique challenges of using this technology in non-model organisms where resources such as genomes, functional genome annotations or genome complexity provide obstacles not met in model organisms. To describe these challenges, we narrow our scope to RNA sequencing used to study differential gene expression in response to pathogen challenge. As a demonstration species we chose Atlantic salmon, which has a sequenced genome with poor annotation and an added complexity due to many duplicated genes. We find that our RNA-seq analysis pipeline deciphers between duplicates despite high sequence identity. However, annotation issues provide problems in linking differentially expressed genes to pathways. Also, comparing results between approaches and species are complicated due to lack of standardized annotation. Copyright © 2017 Elsevier Ltd. All rights reserved.

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

PubMed

Usadel, Björn; Nagel, Axel; Steinhauser, Dirk; Gibon, Yves; Bläsing, Oliver E; Redestig, Henning; Sreenivasulu, Nese; Krall, Leonard; Hannah, Matthew A; Poree, Fabien; Fernie, Alisdair R; Stitt, Mark

2006-12-18

Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Functional Analysis of Metabolomics Data.

PubMed

Chagoyen, Mónica; López-Ibáñez, Javier; Pazos, Florencio

2016-01-01

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

Muscle Research and Gene Ontology: New standards for improved data integration

PubMed Central

Feltrin, Erika; Campanaro, Stefano; Diehl, Alexander D; Ehler, Elisabeth; Faulkner, Georgine; Fordham, Jennifer; Gardin, Chiara; Harris, Midori; Hill, David; Knoell, Ralph; Laveder, Paolo; Mittempergher, Lorenza; Nori, Alessandra; Reggiani, Carlo; Sorrentino, Vincenzo; Volpe, Pompeo; Zara, Ivano; Valle, Giorgio; Deegan née Clark, Jennifer

2009-01-01

Background The Gene Ontology Project provides structured controlled vocabularies for molecular biology that can be used for the functional annotation of genes and gene products. In a collaboration between the Gene Ontology (GO) Consortium and the muscle biology community, we have made large-scale additions to the GO biological process and cellular component ontologies. The main focus of this ontology development work concerns skeletal muscle, with specific consideration given to the processes of muscle contraction, plasticity, development, and regeneration, and to the sarcomere and membrane-delimited compartments. Our aims were to update the existing structure to reflect current knowledge, and to resolve, in an accommodating manner, the ambiguity in the language used by the community. Results The updated muscle terminologies have been incorporated into the GO. There are now 159 new terms covering critical research areas, and 57 existing terms have been improved and reorganized to follow their usage in muscle literature. Conclusion The revised GO structure should improve the interpretation of data from high-throughput (e.g. microarray and proteomic) experiments in the area of muscle science and muscle disease. We actively encourage community feedback on, and gene product annotation with these new terms. Please visit the Muscle Community Annotation Wiki . PMID:19178689

Integrated pathway-based approach identifies association between genomic regions at CTCF and CACNB2 and schizophrenia.

PubMed

Juraeva, Dilafruz; Haenisch, Britta; Zapatka, Marc; Frank, Josef; Witt, Stephanie H; Mühleisen, Thomas W; Treutlein, Jens; Strohmaier, Jana; Meier, Sandra; Degenhardt, Franziska; Giegling, Ina; Ripke, Stephan; Leber, Markus; Lange, Christoph; Schulze, Thomas G; Mössner, Rainald; Nenadic, Igor; Sauer, Heinrich; Rujescu, Dan; Maier, Wolfgang; Børglum, Anders; Ophoff, Roel; Cichon, Sven; Nöthen, Markus M; Rietschel, Marcella; Mattheisen, Manuel; Brors, Benedikt

2014-06-01

In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.

Discriminating response groups in metabolic and regulatory pathway networks.

PubMed

Van Hemert, John L; Dickerson, Julie A

2012-04-01

Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. julied@iastate.edu Supplementary data are available at Bioinformatics online.

Toward Understanding the Genetic Basis of Yak Ovary Reproduction: A Characterization and Comparative Analyses of Estrus Ovary Transcriptiome in Yak and Cattle.

PubMed

Lan, Daoliang; Xiong, Xianrong; Huang, Cai; Mipam, Tserang Donko; Li, Jian

2016-01-01

Yaks (Bos grunniens) are endemic species that can adapt well to thin air, cold temperatures, and high altitude. These species can survive in harsh plateau environments and are major source of animal production for local residents, being an important breed in the Qinghai-Tibet Plateau. However, compared with ordinary cattle that live in the plains, yaks generally have lower fertility. Investigating the basic physiological molecular features of yak ovary and identifying the biological events underlying the differences between the ovaries of yak and plain cattle is necessary to understand the specificity of yak reproduction. Therefore, RNA-seq technology was applied to analyze transcriptome data comparatively between the yak and plain cattle estrous ovaries. After deep sequencing, 3,653,032 clean reads with a total of 4,828,772,880 base pairs were obtained from yak ovary library. Alignment analysis showed that 16992 yak genes mapped to the yak genome, among which, 12,731 and 14,631 genes were assigned to Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, comparison of yak and cattle ovary transcriptome data revealed that 1307 genes were significantly and differentially expressed between the two libraries, wherein 661 genes were upregulated and 646 genes were downregulated in yak ovary. Functional analysis showed that the differentially expressed genes were involved in various Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO annotations indicated that the genes related to "cell adhesion," "hormonal" biological processes, and "calcium ion binding," "cation transmembrane transport" molecular events were significantly active. KEGG pathway analysis showed that the "complement and coagulation cascade" pathway was the most enriched in yak ovary transcriptome data, followed by the "cytochrome P450" related and "ECM-receptor interaction" pathways. Moreover, several novel pathways, such as "circadian rhythm," were significantly enriched despite having no evident associations with the reproductive function. Our findings provide a molecular resource for further investigation of the general molecular mechanism of yak ovary and offer new insights to understand comprehensively the specificity of yak reproduction.

Sequencing and comparative genomic analysis of 1227 Felis catus cDNA sequences enriched for developmental, clinical and nutritional phenotypes

PubMed Central

2012-01-01

Background The feline genome is valuable to the veterinary and model organism genomics communities because the cat is an obligate carnivore and a model for endangered felids. The initial public release of the Felis catus genome assembly provided a framework for investigating the genomic basis of feline biology. However, the entire set of protein coding genes has not been elucidated. Results We identified and characterized 1227 protein coding feline sequences, of which 913 map to public sequences and 314 are novel. These sequences have been deposited into NCBI's genbank database and complement public genomic resources by providing additional protein coding sequences that fill in some of the gaps in the feline genome assembly. Through functional and comparative genomic analyses, we gained an understanding of the role of these sequences in feline development, nutrition and health. Specifically, we identified 104 orthologs of human genes associated with Mendelian disorders. We detected negative selection within sequences with gene ontology annotations associated with intracellular trafficking, cytoskeleton and muscle functions. We detected relatively less negative selection on protein sequences encoding extracellular networks, apoptotic pathways and mitochondrial gene ontology annotations. Additionally, we characterized feline cDNA sequences that have mouse orthologs associated with clinical, nutritional and developmental phenotypes. Together, this analysis provides an overview of the value of our cDNA sequences and enhances our understanding of how the feline genome is similar to, and different from other mammalian genomes. Conclusions The cDNA sequences reported here expand existing feline genomic resources by providing high-quality sequences annotated with comparative genomic information providing functional, clinical, nutritional and orthologous gene information. PMID:22257742

Grouping annotations on the subcellular layered interactome demonstrates enhanced autophagy activity in a recurrent experimental autoimmune uveitis T cell line.

PubMed

Jia, Xiuzhi; Li, Jingbo; Shi, Dejing; Zhao, Yu; Dong, Yucui; Ju, Huanyu; Yang, Jinfeng; Sun, Jianhua; Li, Xia; Ren, Huan

2014-01-01

Human uveitis is a type of T cell-mediated autoimmune disease that often shows relapse-remitting courses affecting multiple biological processes. As a cytoplasmic process, autophagy has been seen as an adaptive response to cell death and survival, yet the link between autophagy and T cell-mediated autoimmunity is not certain. In this study, based on the differentially expressed genes (GSE19652) between the recurrent versus monophasic T cell lines, whose adoptive transfer to susceptible animals may result in respective recurrent or monophasic uveitis, we proposed grouping annotations on a subcellular layered interactome framework to analyze the specific bioprocesses that are linked to the recurrence of T cell autoimmunity. That is, the subcellular layered interactome was established by the Cytoscape and Cerebral plugin based on differential expression, global interactome, and subcellular localization information. Then, the layered interactomes were grouping annotated by the ClueGO plugin based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The analysis showed that significant bioprocesses with autophagy were orchestrated in the cytoplasmic layered interactome and that mTOR may have a regulatory role in it. Furthermore, by setting up recurrent and monophasic uveitis in Lewis rats, we confirmed by transmission electron microscopy that, in comparison to the monophasic disease, recurrent uveitis in vivo showed significantly increased autophagy activity and extended lymphocyte infiltration to the affected retina. In summary, our framework methodology is a useful tool to disclose specific bioprocesses and molecular targets that can be attributed to a certain disease. Our results indicated that targeted inhibition of autophagy pathways may perturb the recurrence of uveitis.

Transcriptomic analysis of koi (Cyprinus carpio) spleen tissue upon cyprinid herpesvirus 3 (CyHV3) infection using next generation sequencing.

PubMed

Lee, Xuezhu; Yi, Yang; Weng, Shaoping; Zeng, Jie; Zhang, Hetong; He, Jianguo; Dong, Chuanfu

2016-02-01

Cyprinid Herpesvirus 3 (CyHV-3) can infect and specifically cause a huge economic loss in both common carp (Cyprinus carpio) and its ornamental koi variety. The molecular mechanisms underlying CyHV-3 infection are not well understood. In this study, koi spleen tissues of both mock and CyHV-3 infection groups were collected, and high-throughput sequencing technology was used to analyze the differentially expressed genes (DEGs) at the transcriptome level. A total of 105,356,188 clean reads from two libraries were obtained. After the de novo assembly of the transcripts, 129,314 unigenes were generated. Of these unigenes, 70,655 unigenes were matched to the known proteins in the database, while 2190 unigenes were predicted by ESTScan software. Comparing the infection group to the mock group, a total of 23,029 significantly differentially expressed unigenes were identified, including 10,493 up-regulated DEGs and 12,536 down-regulated DEGs. GO (Gene Ontology) annotation and functional enrichment analysis indicated that all of the DEGs were annotated into GO terms in three main GO categories: biological process, cellular component and molecular function. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis of the DEGs showed that a total of 12,002 DEG unigenes were annotated into 256 pathways classified into 6 main categories. Additionally, 20 differentially expressed genes were validated by quantitative real-time PCR. As the first report of a transcriptome analysis of koi carp with CyHV-3 infection, the data presented here provide knowledge of the innate immune response against CyHV-3 in koi carp and useful data for further research of the molecular mechanism of CyHV-3 infection. Copyright © 2015 Elsevier Ltd. All rights reserved.

De Novo Transcriptome Analysis of Two Seahorse Species (Hippocampus erectus and H. mohnikei) and the Development of Molecular Markers for Population Genetics

PubMed Central

Lin, Qiang; Luo, Wei; Wan, Shiming; Gao, Zexia

2016-01-01

Seahorse conservation has been performed utilizing various strategies for many decades, and the deeper understanding of genomic information is necessary to more efficiently protect the germplasm resources of seahorse species. However, little genetic information about seahorses currently exists in the public databases. In this study, high-throughput RNA sequencing for two seahorse species, Hippocampus erectus and H. mohnikei, was carried out, and de novo assembly generated 37,506 unigenes for H. erectus and 36,113 unigenes for H. mohnikei. Among them, 17,338 (46.23%) unigenes for H. erectus and 17,900 (49.57%) for H. mohnikei were successfully annotated based on the information available from the public databases. Through comparing the unigenes of two seahorse species, 7,802 candidate orthologous genes were identified and 5,268 genes among them could be annotated. In addition, gene ontology analysis of two species was similarly performed on biological processes, cellular components, and molecular functions. Twenty-four and twenty-one unigenes in H. erectus and H. mohnikei were annotated in the biosynthesis of unsaturated fatty acids pathways, and both seahorses lacked the Δ12 and Δ15 desaturases. Total of 8,992 and 9,116 SSR loci were obtained from H. erectus and H. mohnikei unigenes, respectively. Dozens of SSR were developed and then applied to assess the population genetic diversity, as well as cross-amplified in a related species, H. trimaculatus. The HO and HE values of the tested populations for H. erectus, H. mohnikei, and H. trimaculatus were medium. These resources would facilitate the conservation of the species through a better understanding of the genomics and comparative genome analysis within the Hippocampus genus. PMID:27128031

De Novo Transcriptome Analysis of Two Seahorse Species (Hippocampus erectus and H. mohnikei) and the Development of Molecular Markers for Population Genetics.

PubMed

Lin, Qiang; Luo, Wei; Wan, Shiming; Gao, Zexia

2016-01-01

Seahorse conservation has been performed utilizing various strategies for many decades, and the deeper understanding of genomic information is necessary to more efficiently protect the germplasm resources of seahorse species. However, little genetic information about seahorses currently exists in the public databases. In this study, high-throughput RNA sequencing for two seahorse species, Hippocampus erectus and H. mohnikei, was carried out, and de novo assembly generated 37,506 unigenes for H. erectus and 36,113 unigenes for H. mohnikei. Among them, 17,338 (46.23%) unigenes for H. erectus and 17,900 (49.57%) for H. mohnikei were successfully annotated based on the information available from the public databases. Through comparing the unigenes of two seahorse species, 7,802 candidate orthologous genes were identified and 5,268 genes among them could be annotated. In addition, gene ontology analysis of two species was similarly performed on biological processes, cellular components, and molecular functions. Twenty-four and twenty-one unigenes in H. erectus and H. mohnikei were annotated in the biosynthesis of unsaturated fatty acids pathways, and both seahorses lacked the Δ12 and Δ15 desaturases. Total of 8,992 and 9,116 SSR loci were obtained from H. erectus and H. mohnikei unigenes, respectively. Dozens of SSR were developed and then applied to assess the population genetic diversity, as well as cross-amplified in a related species, H. trimaculatus. The HO and HE values of the tested populations for H. erectus, H. mohnikei, and H. trimaculatus were medium. These resources would facilitate the conservation of the species through a better understanding of the genomics and comparative genome analysis within the Hippocampus genus.

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts.

PubMed

Xu, Lijing; Furlotte, Nicholas; Lin, Yunyue; Heinrich, Kevin; Berry, Michael W; George, Ebenezer O; Homayouni, Ramin

2011-04-14

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. GCAT is freely available at http://binf1.memphis.edu/gcat.

RNA-Seq Analysis of Cocos nucifera: Transcriptome Sequencing and De Novo Assembly for Subsequent Functional Genomics Approaches

PubMed Central

Xia, Wei; Mason, Annaliese S.; Xia, Zhihui; Qiao, Fei; Zhao, Songlin; Tang, Haoru

2013-01-01

Background Cocos nucifera (coconut), a member of the Arecaceae family, is an economically important woody palm grown in tropical regions. Despite its agronomic importance, previous germplasm assessment studies have relied solely on morphological and agronomical traits. Molecular biology techniques have been scarcely used in assessment of genetic resources and for improvement of important agronomic and quality traits in Cocos nucifera, mostly due to the absence of available sequence information. Methodology/Principal Findings To provide basic information for molecular breeding and further molecular biological analysis in Cocos nucifera, we applied RNA-seq technology and de novo assembly to gain a global overview of the Cocos nucifera transcriptome from mixed tissue samples. Using Illumina sequencing, we obtained 54.9 million short reads and conducted de novo assembly to obtain 57,304 unigenes with an average length of 752 base pairs. Sequence comparison between assembled unigenes and released cDNA sequences of Cocos nucifera and Elaeis guineensis indicated that the assembled sequences were of high quality. Approximately 99.9% of unigenes were novel compared to the released coconut EST sequences. Using BLASTX, 68.2% of unigenes were successfully annotated based on the Genbank non-redundant (Nr) protein database. The annotated unigenes were then further classified using the Gene Ontology (GO), Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Conclusions/Significance Our study provides a large quantity of novel genetic information for Cocos nucifera. This information will act as a valuable resource for further molecular genetic studies and breeding in coconut, as well as for isolation and characterization of functional genes involved in different biochemical pathways in this important tropical crop species. PMID:23555859

RNA-Seq analysis of Cocos nucifera: transcriptome sequencing and de novo assembly for subsequent functional genomics approaches.

PubMed

Fan, Haikuo; Xiao, Yong; Yang, Yaodong; Xia, Wei; Mason, Annaliese S; Xia, Zhihui; Qiao, Fei; Zhao, Songlin; Tang, Haoru

2013-01-01

Cocos nucifera (coconut), a member of the Arecaceae family, is an economically important woody palm grown in tropical regions. Despite its agronomic importance, previous germplasm assessment studies have relied solely on morphological and agronomical traits. Molecular biology techniques have been scarcely used in assessment of genetic resources and for improvement of important agronomic and quality traits in Cocos nucifera, mostly due to the absence of available sequence information. To provide basic information for molecular breeding and further molecular biological analysis in Cocos nucifera, we applied RNA-seq technology and de novo assembly to gain a global overview of the Cocos nucifera transcriptome from mixed tissue samples. Using Illumina sequencing, we obtained 54.9 million short reads and conducted de novo assembly to obtain 57,304 unigenes with an average length of 752 base pairs. Sequence comparison between assembled unigenes and released cDNA sequences of Cocos nucifera and Elaeis guineensis indicated that the assembled sequences were of high quality. Approximately 99.9% of unigenes were novel compared to the released coconut EST sequences. Using BLASTX, 68.2% of unigenes were successfully annotated based on the Genbank non-redundant (Nr) protein database. The annotated unigenes were then further classified using the Gene Ontology (GO), Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Our study provides a large quantity of novel genetic information for Cocos nucifera. This information will act as a valuable resource for further molecular genetic studies and breeding in coconut, as well as for isolation and characterization of functional genes involved in different biochemical pathways in this important tropical crop species.

Identification of functional elements and regulatory circuits by Drosophila modENCODE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roy, Sushmita; Ernst, Jason; Kharchenko, Peter V.

2010-12-22

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- andmore » tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation. Several years after the complete genetic sequencing of many species, it is still unclear how to translate genomic information into a functional map of cellular and developmental programs. The Encyclopedia of DNA Elements (ENCODE) (1) and model organism ENCODE (modENCODE) (2) projects use diverse genomic assays to comprehensively annotate the Homo sapiens (human), Drosophila melanogaster (fruit fly), and Caenorhabditis elegans (worm) genomes, through systematic generation and computational integration of functional genomic data sets. Previous genomic studies in flies have made seminal contributions to our understanding of basic biological mechanisms and genome functions, facilitated by genetic, experimental, computational, and manual annotation of the euchromatic and heterochromatic genome (3), small genome size, short life cycle, and a deep knowledge of development, gene function, and chromosome biology. The functions of {approx}40% of the protein and nonprotein-coding genes [FlyBase 5.12 (4)] have been determined from cDNA collections (5, 6), manual curation of gene models (7), gene mutations and comprehensive genome-wide RNA interference screens (8-10), and comparative genomic analyses (11, 12). The Drosophila modENCODE project has generated more than 700 data sets that profile transcripts, histone modifications and physical nucleosome properties, general and specific transcription factors (TFs), and replication programs in cell lines, isolated tissues, and whole organisms across several developmental stages (Fig. 1). Here, we computationally integrate these data sets and report (i) improved and additional genome annotations, including full-length proteincoding genes and peptides as short as 21 amino acids; (ii) noncoding transcripts, including 132 candidate structural RNAs and 1608 nonstructural transcripts; (iii) additional Argonaute (Ago)-associated small RNA genes and pathways, including new microRNAs (miRNAs) encoded within protein-coding exons and endogenous small interfering RNAs (siRNAs) from 3-inch untranslated regions; (iv) chromatin 'states' defined by combinatorial patterns of 18 chromatin marks that are associated with distinct functions and properties; (v) regions of high TF occupancy and replication activity with likely epigenetic regulation; (vi)mixed TF and miRNA regulatory networks with hierarchical structure and enriched feed-forward loops; (vii) coexpression- and co-regulation-based functional annotations for nearly 3000 genes; (viii) stage- and tissue-specific regulators; and (ix) predictive models of gene expression levels and regulator function.« less

Transcriptomic resources for the medicinal legume Mucuna pruriens: de novo transcriptome assembly, annotation, identification and validation of EST-SSR markers.

PubMed

Sathyanarayana, N; Pittala, Ranjith Kumar; Tripathi, Pankaj Kumar; Chopra, Ratan; Singh, Heikham Russiachand; Belamkar, Vikas; Bhardwaj, Pardeep Kumar; Doyle, Jeff J; Egan, Ashley N

2017-05-25

The medicinal legume Mucuna pruriens (L.) DC. has attracted attention worldwide as a source of the anti-Parkinson's drug L-Dopa. It is also a popular green manure cover crop that offers many agronomic benefits including high protein content, nitrogen fixation and soil nutrients. The plant currently lacks genomic resources and there is limited knowledge on gene expression, metabolic pathways, and genetics of secondary metabolite production. Here, we present transcriptomic resources for M. pruriens, including a de novo transcriptome assembly and annotation, as well as differential transcript expression analyses between root, leaf, and pod tissues. We also develop microsatellite markers and analyze genetic diversity and population structure within a set of Indian germplasm accessions. One-hundred ninety-one million two hundred thirty-three thousand two hundred forty-two bp cleaned reads were assembled into 67,561 transcripts with mean length of 626 bp and N50 of 987 bp. Assembled sequences were annotated using BLASTX against public databases with over 80% of transcripts annotated. We identified 7,493 simple sequence repeat (SSR) motifs, including 787 polymorphic repeats between the parents of a mapping population. 134 SSRs from expressed sequenced tags (ESTs) were screened against 23 M. pruriens accessions from India, with 52 EST-SSRs retained after quality control. Population structure analysis using a Bayesian framework implemented in fastSTRUCTURE showed nearly similar groupings as with distance-based (neighbor-joining) and principal component analyses, with most of the accessions clustering per geographical origins. Pair-wise comparison of transcript expression in leaves, roots and pods identified 4,387 differentially expressed transcripts with the highest number occurring between roots and leaves. Differentially expressed transcripts were enriched with transcription factors and transcripts annotated as belonging to secondary metabolite pathways. The M. pruriens transcriptomic resources generated in this study provide foundational resources for gene discovery and development of molecular markers. Polymorphic SSRs identified can be used for genetic diversity, marker-trait analyses, and development of functional markers for crop improvement. The results of differential expression studies can be used to investigate genes involved in L-Dopa synthesis and other key metabolic pathways in M. pruriens.

Caenorhabditis elegans chemical biology: lessons from small molecules

USDA-ARS?s Scientific Manuscript database

How can we complement Caenorhabditis elegans genomics and proteomics with a comprehensive structural and functional annotation of its metabolome? Several lines of evidence indicate that small molecules of largely undetermined structure play important roles in C. elegans biology, including key pathw...

AGORA : Organellar genome annotation from the amino acid and nucleotide references.

PubMed

Jung, Jaehee; Kim, Jong Im; Jeong, Young-Sik; Yi, Gangman

2018-03-29

Next-generation sequencing (NGS) technologies have led to the accumulation of highthroughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals.We have developed a web application AGORA for the fast, user-friendly, and improved annotations of organellar genomes. AGORA annotates genes based on a BLAST-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon-intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence, and visualization of gene map by OGDRAW. Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/.The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers. gangman@dongguk.edu.

Ontological interpretation of biomedical database content.

PubMed

Santana da Silva, Filipe; Jansen, Ludger; Freitas, Fred; Schulz, Stefan

2017-06-26

Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models.

Deciphering the pharmacological mechanism of the Chinese formula Huanglian-Jie-Du decoction in the treatment of ischemic stroke using a systems biology-based strategy

PubMed Central

Zhang, Yan-qiong; Wang, Song-song; Zhu, Wei-liang; Ma, Yan; Zhang, Fang-bo; Liang, Ri-xin; Xu, Hai-yu; Yang, Hong-jun

2015-01-01

Aim: Huanglian-Jie-Du decoction (HLJDD) is an important multiherb remedy in TCM, which is recently demonstrated to be effective to treat ischemic stroke. Here, we aimed to investigate the pharmacological mechanisms of HLJDD in the treatment of ischemic stroke using systems biology approaches. Methods: Putative targets of HLJDD were predicted using MetaDrug. An interaction network of putative HLJDD targets and known therapeutic targets for the treatment of ischemic stroke was then constructed, and candidate HLJDD targets were identified by calculating topological features, including 'Degree', 'Node-betweenness', 'Closeness', and 'K-coreness'. The binding efficiencies of the candidate HLJDD targets with the corresponding compositive compounds were further validated by a molecular docking simulation. Results: A total of 809 putative targets were obtained for 168 compositive compounds in HLJDD. Additionally, 39 putative targets were common to all four herbs of HLJDD. Next, 49 major nodes were identified as candidate HLJDD targets due to their network topological importance. The enrichment analysis based on the Gene Ontology (GO) annotation system and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway demonstrated that candidate HLJDD targets were more frequently involved in G-protein-coupled receptor signaling pathways, neuroactive ligand-receptor interactions and gap junctions, which all played important roles in the progression of ischemic stroke. Finally, the molecular docking simulation showed that 170 pairs of chemical components and candidate HLJDD targets had strong binding efficiencies. Conclusion: This study has developed for the first time a comprehensive systems approach integrating drug target prediction, network analysis and molecular docking simulation to reveal the relationships between the herbs contained in HLJDD and their putative targets and ischemic stroke-related pathways. PMID:25937634

ReprOlive: a database with linked data for the olive tree (Olea europaea L.) reproductive transcriptome

PubMed Central

Carmona, Rosario; Zafra, Adoración; Seoane, Pedro; Castro, Antonio J.; Guerrero-Fernández, Darío; Castillo-Castillo, Trinidad; Medina-García, Ana; Cánovas, Francisco M.; Aldana-Montes, José F.; Navas-Delgado, Ismael; Alché, Juan de Dios; Claros, M. Gonzalo

2015-01-01

Plant reproductive transcriptomes have been analyzed in different species due to the agronomical and biotechnological importance of plant reproduction. Here we presented an olive tree reproductive transcriptome database with samples from pollen and pistil at different developmental stages, and leaf and root as control vegetative tissues http://reprolive.eez.csic.es). It was developed from 2,077,309 raw reads to 1,549 Sanger sequences. Using a pre-defined workflow based on open-source tools, sequences were pre-processed, assembled, mapped, and annotated with expression data, descriptions, GO terms, InterPro signatures, EC numbers, KEGG pathways, ORFs, and SSRs. Tentative transcripts (TTs) were also annotated with the corresponding orthologs in Arabidopsis thaliana from TAIR and RefSeq databases to enable Linked Data integration. It results in a reproductive transcriptome comprising 72,846 contigs with average length of 686 bp, of which 63,965 (87.8%) included at least one functional annotation, and 55,356 (75.9%) had an ortholog. A minimum of 23,568 different TTs was identified and 5,835 of them contain a complete ORF. The representative reproductive transcriptome can be reduced to 28,972 TTs for further gene expression studies. Partial transcriptomes from pollen, pistil, and vegetative tissues as control were also constructed. ReprOlive provides free access and download capability to these results. Retrieval mechanisms for sequences and transcript annotations are provided. Graphical localization of annotated enzymes into KEGG pathways is also possible. Finally, ReprOlive has included a semantic conceptualisation by means of a Resource Description Framework (RDF) allowing a Linked Data search for extracting the most updated information related to enzymes, interactions, allergens, structures, and reactive oxygen species. PMID:26322066

ISEScan: automated identification of insertion sequence elements in prokaryotic genomes.

PubMed

Xie, Zhiqun; Tang, Haixu

2017-11-01

The insertion sequence (IS) elements are the smallest but most abundant autonomous transposable elements in prokaryotic genomes, which play a key role in prokaryotic genome organization and evolution. With the fast growing genomic data, it is becoming increasingly critical for biology researchers to be able to accurately and automatically annotate ISs in prokaryotic genome sequences. The available automatic IS annotation systems are either providing only incomplete IS annotation or relying on the availability of existing genome annotations. Here, we present a new IS elements annotation pipeline to address these issues. ISEScan is a highly sensitive software pipeline based on profile hidden Markov models constructed from manually curated IS elements. ISEScan performs better than existing IS annotation systems when tested on prokaryotic genomes with curated annotations of IS elements. Applying it to 2784 prokaryotic genomes, we report the global distribution of IS families across taxonomic clades in Archaea and Bacteria. ISEScan is implemented in Python and released as an open source software at https://github.com/xiezhq/ISEScan. hatang@indiana.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

dbWFA: a web-based database for functional annotation of Triticum aestivum transcripts

PubMed Central

Vincent, Jonathan; Dai, Zhanwu; Ravel, Catherine; Choulet, Frédéric; Mouzeyar, Said; Bouzidi, M. Fouad; Agier, Marie; Martre, Pierre

2013-01-01

The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L.). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L.) Heynh. and Oryza sativa L., was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families). The use of dbWFA is illustrated here with several query examples. We were able to assign a putative function to 45% of the UniGenes and 81% of the full-length coding sequences from TriFLDB. Moreover, comparison of the annotation of the whole T. aestivum UniGene set along with curated annotations of the two model species assessed the accuracy of the annotation provided by dbWFA. To further illustrate the use of dbWFA, genes specifically expressed during the early cell division or late storage polymer accumulation phases of T. aestivum grain development were identified using a clustering analysis and then annotated using dbWFA. The annotation of these two sets of genes was consistent with previous analyses of T. aestivum grain transcriptomes and proteomes. Database URL: urgi.versailles.inra.fr/dbWFA/ PMID:23660284

CHEMICAL STRUCTURE INDEXING OF TOXICITY DATA ON ...

EPA Pesticide Factsheets

Standardized chemical structure annotation of public toxicity databases and information resources is playing an increasingly important role in the 'flattening' and integration of diverse sets of biological activity data on the Internet. This review discusses public initiatives that are accelerating the pace of this transformation, with particular reference to toxicology-related chemical information. Chemical content annotators, structure locator services, large structure/data aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) codes, toxicity data models and public chemical/biological activity profiling initiatives are all playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. An example of this integration of data is provided by the collaboration among researchers involved with the Distributed Structure-Searchable Toxicity (DSSTox) project, the Carcinogenic Potency Project, projects at the National Cancer Institute and the PubChem database. Standardizing chemical structure annotation of public toxicity databases

Functional Annotation of the Arabidopsis Genome Using Controlled Vocabularies1

PubMed Central

Berardini, Tanya Z.; Mundodi, Suparna; Reiser, Leonore; Huala, Eva; Garcia-Hernandez, Margarita; Zhang, Peifen; Mueller, Lukas A.; Yoon, Jungwoon; Doyle, Aisling; Lander, Gabriel; Moseyko, Nick; Yoo, Danny; Xu, Iris; Zoeckler, Brandon; Montoya, Mary; Miller, Neil; Weems, Dan; Rhee, Seung Y.

2004-01-01

Controlled vocabularies are increasingly used by databases to describe genes and gene products because they facilitate identification of similar genes within an organism or among different organisms. One of The Arabidopsis Information Resource's goals is to associate all Arabidopsis genes with terms developed by the Gene Ontology Consortium that describe the molecular function, biological process, and subcellular location of a gene product. We have also developed terms describing Arabidopsis anatomy and developmental stages and use these to annotate published gene expression data. As of March 2004, we used computational and manual annotation methods to make 85,666 annotations representing 26,624 unique loci. We focus on associating genes to controlled vocabulary terms based on experimental data from the literature and use The Arabidopsis Information Resource-developed PubSearch software to facilitate this process. Each annotation is tagged with a combination of evidence codes, evidence descriptions, and references that provide a robust means to assess data quality. Annotation of all Arabidopsis genes will allow quantitative comparisons between sets of genes derived from sources such as microarray experiments. The Arabidopsis annotation data will also facilitate annotation of newly sequenced plant genomes by using sequence similarity to transfer annotations to homologous genes. In addition, complete and up-to-date annotations will make unknown genes easy to identify and target for experimentation. Here, we describe the process of Arabidopsis functional annotation using a variety of data sources and illustrate several ways in which this information can be accessed and used to infer knowledge about Arabidopsis and other plant species. PMID:15173566

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna; ...

2016-04-26

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Evaluation of genome-wide association study results through development of ontology fingerprints

PubMed Central

Tsoi, Lam C.; Boehnke, Michael; Klein, Richard L.; Zheng, W. Jim

2009-01-01

Motivation: Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important. Results: We describe an approach to characterize genes or biological concepts (phenotypes, pathways, diseases, etc.) by ontology fingerprint—the set of Gene Ontology (GO) terms that are overrepresented among the PubMed abstracts discussing the gene or biological concept together with the enrichment p-value of these terms generated from a hypergeometric enrichment test. We then quantify the relevance of genes to the trait from a GWA study by calculating similarity scores between their ontology fingerprints using enrichment p-values. We validate this approach by correctly identifying corresponding genes for biological pathways with a 90% average area under the ROC curve (AUC). We applied this approach to rank genes identified through a GWA study that are associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, lipoprotein lipase (LPL) and cholesterol ester transfer protein, plasma for high-density lipoprotein; low-density lipoprotein receptor, APOE and APOB for low-density lipoprotein; and LPL, APOA1 and APOB for triglyceride. In addition, we identified genes relevant to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation. Contact: zhengw@musc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19349285

Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses

PubMed Central

Kogelman, Lisette J. A.; Pant, Sameer D.; Fredholm, Merete; Kadarmideen, Haja N.

2014-01-01

Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie it. PMID:25071839

Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes

PubMed Central

Nayfach, Stephen; Bradley, Patrick H.; Wyman, Stacia K.; Laurent, Timothy J.; Williams, Alex; Eisen, Jonathan A.; Pollard, Katherine S.; Sharpton, Thomas J.

2015-01-01

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease. PMID:26565399

Transcriptome analysis of Ruditapes philippinarum hepatopancreas provides insights into immune signaling pathways under Vibrio anguillarum infection.

PubMed

Ren, Yipeng; Xue, Junli; Yang, Huanhuan; Pan, Baoping; Bu, Wenjun

2017-05-01

The Manila clam, Ruditapes philippinarum, is one of the most economically important aquatic clams that are harvested on a large scale by the mariculture industry in China. However, increasing reports of bacterial pathogenic diseases have had a negative effect on the aquaculture industry of R. philippinarum. In the present study, the two transcriptome libraries of untreated (termed H) and challenged Vibrio anguillarum (termed HV) hepatopancreas were constructed and sequenced from Manila clam using an Illumina-based paired-end sequencing platform. In total, 75,302,886 and 66,578,976 high-quality clean reads were assembled from 101,080,746 and 99,673,538 raw data points from the two transcriptome libraries described above, respectively. Furthermore, 156,116 unigenes were generated from 210,685 transcripts, with an N50 length of 1125 bp, and from the annotated SwissProt, NR, NT, KO, GO, KOG and KEGG databases. Moreover, a total of 4071 differentially expressed unigenes (HV vs H) were detected, including 903 up-regulated and 3168 down-regulated genes. Among these differentially expressed unigenes, 226 unigenes were annotated using KEGG annotation in 16 immune-related signaling pathways, including Toll-like receptor, NF-kappa B, MAPK, NOD-like receptor, RIG-I-like receptor, and the TNF and chemokine signaling pathways. Finally, 20,341 simple sequence repeats (SSRs) and 214,430 potential single nucleotide polymorphisms (SNPs) were detected from the H and HV transcriptome libraries. In conclusion, these studies identified many candidate immune-related genes and signaling pathways and conducted a comparative analysis of the differentially expressed unigenes from Manila clam hepatopancreas in response to V. anguillarum stimulation. These data laid the foundation for studying the innate immune systems and defense mechanisms in R. philippinarum. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Portal into Biology Education: An Annotated List of Commonly Encountered Terms

ERIC Educational Resources Information Center

Miller, Sarah; Tanner, Kimberly D.

2015-01-01

In an introductory biology course, undergraduate students are expected to become familiar with, and be able to use, hundreds of new terms to navigate the complex ideas in biology. And this is just in the introductory course! Juxtapose this student situation with the common frustration expressed by biologists that there is "just too much…

An Innovative Plant Genomics and Gene Annotation Program for High School, Community College, and University Faculty

ERIC Educational Resources Information Center

Hacisalihoglu, Gokhan; Hilgert, Uwe; Nash, E. Bruce; Micklos, David A.

2008-01-01

Today's biology educators face the challenge of training their students in modern molecular biology techniques including genomics and bioinformatics. The Dolan DNA Learning Center (DNALC) of Cold Spring Harbor Laboratory has developed and disseminated a bench- and computer-based plant genomics curriculum for biology faculty. In 2007, a five-day…

ChemiRs: a web application for microRNAs and chemicals.

PubMed

Su, Emily Chia-Yu; Chen, Yu-Sing; Tien, Yun-Cheng; Liu, Jeff; Ho, Bing-Ching; Yu, Sung-Liang; Singh, Sher

2016-04-18

MicroRNAs (miRNAs) are about 22 nucleotides, non-coding RNAs that affect various cellular functions, and play a regulatory role in different organisms including human. Until now, more than 2500 mature miRNAs in human have been discovered and registered, but still lack of information or algorithms to reveal the relations among miRNAs, environmental chemicals and human health. Chemicals in environment affect our health and daily life, and some of them can lead to diseases by inferring biological pathways. We develop a creditable online web server, ChemiRs, for predicting interactions and relations among miRNAs, chemicals and pathways. The database not only compares gene lists affected by chemicals and miRNAs, but also incorporates curated pathways to identify possible interactions. Here, we manually retrieved associations of miRNAs and chemicals from biomedical literature. We developed an online system, ChemiRs, which contains miRNAs, diseases, Medical Subject Heading (MeSH) terms, chemicals, genes, pathways and PubMed IDs. We connected each miRNA to miRBase, and every current gene symbol to HUGO Gene Nomenclature Committee (HGNC) for genome annotation. Human pathway information is also provided from KEGG and REACTOME databases. Information about Gene Ontology (GO) is queried from GO Online SQL Environment (GOOSE). With a user-friendly interface, the web application is easy to use. Multiple query results can be easily integrated and exported as report documents in PDF format. Association analysis of miRNAs and chemicals can help us understand the pathogenesis of chemical components. ChemiRs is freely available for public use at http://omics.biol.ntnu.edu.tw/ChemiRs .

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Standardized description of scientific evidence using the Evidence Ontology (ECO)

PubMed Central

Chibucos, Marcus C.; Mungall, Christopher J.; Balakrishnan, Rama; Christie, Karen R.; Huntley, Rachael P.; White, Owen; Blake, Judith A.; Lewis, Suzanna E.; Giglio, Michelle

2014-01-01

The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources. Database URL: Evidence Ontology Web site: http://evidenceontology.org PMID:25052702

PlantRNA, a database for tRNAs of photosynthetic eukaryotes.

PubMed

Cognat, Valérie; Pawlak, Gaël; Duchêne, Anne-Marie; Daujat, Magali; Gigant, Anaïs; Salinas, Thalia; Michaud, Morgane; Gutmann, Bernard; Giegé, Philippe; Gobert, Anthony; Maréchal-Drouard, Laurence

2013-01-01

PlantRNA database (http://plantrna.ibmp.cnrs.fr/) compiles transfer RNA (tRNA) gene sequences retrieved from fully annotated plant nuclear, plastidial and mitochondrial genomes. The set of annotated tRNA gene sequences has been manually curated for maximum quality and confidence. The novelty of this database resides in the inclusion of biological information relevant to the function of all the tRNAs entered in the library. This includes 5'- and 3'-flanking sequences, A and B box sequences, region of transcription initiation and poly(T) transcription termination stretches, tRNA intron sequences, aminoacyl-tRNA synthetases and enzymes responsible for tRNA maturation and modification. Finally, data on mitochondrial import of nuclear-encoded tRNAs as well as the bibliome for the respective tRNAs and tRNA-binding proteins are also included. The current annotation concerns complete genomes from 11 organisms: five flowering plants (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Medicago truncatula and Brachypodium distachyon), a moss (Physcomitrella patens), two green algae (Chlamydomonas reinhardtii and Ostreococcus tauri), one glaucophyte (Cyanophora paradoxa), one brown alga (Ectocarpus siliculosus) and a pennate diatom (Phaeodactylum tricornutum). The database will be regularly updated and implemented with new plant genome annotations so as to provide extensive information on tRNA biology to the research community.

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network.

PubMed

Deng, Lei; Wu, Hongjie; Liu, Chuyao; Zhan, Weihua; Zhang, Jingpu

2018-06-01

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage. Copyright © 2018 Elsevier Ltd. All rights reserved.

Microarray analysis of differentially expressed genes engaged in fruit development between Prunus mume and Prunus armeniaca.

PubMed

Li, Xiaoying; Korir, Nicholas Kibet; Liu, Lili; Shangguan, Lingfei; Wang, Yuzhu; Han, Jian; Chen, Ming; Fang, Jinggui

2012-11-15

Microarray analysis is a technique that can be employed to provide expression profiles of single genes and new insights to elucidate the biological mechanisms responsible for fruit development. To evaluate expression of genes mostly engaged in fruit development between Prunus mume and Prunus armeniaca, we first identified differentially expressed transcripts along the entire fruit life cycle by using microarrays spotted with 10,641 ESTs collected from P. mume and other Prunus EST sequences. A total of 1418 ESTs were selected after quality control of microarray spots and analysis for differential gene expression patterns during fruit development of P. mume and P. Armeniaca. From these, 707 up-regulated and 711 down-regulated genes showing more than two-fold differences in expression level were annotated by GO based on biological processes, molecular functions and cellular components. These differentially expressed genes were found to be involved in several important pathways of carbohydrate, galactose, and starch and sucrose metabolism as well as in biosynthesis of other secondary metabolites via KEGG. This could provide detailed information on the fruit quality differences during development and ripening of these two species. With the results obtained, we provide a practical database for comprehensive understanding of molecular events during fruit development and also lay a theoretical foundation for the cloning of genes regulating in a series of important rate-limiting enzymes involved in vital metabolic pathways during fruit development. Copyright © 2012 Elsevier GmbH. All rights reserved.

Identification of differentially expressed genes in childhood asthma.

PubMed

Zhang, Nian-Zhen; Chen, Xiu-Juan; Mu, Yu-Hua; Wang, Hewen

2018-05-01

Asthma has been the most common chronic disease in children that places a major burden for affected people and their families.An integrated analysis of microarrays studies was performed to identify differentially expressed genes (DEGs) in childhood asthma compared with normal control. We also obtained the differentially methylated genes (DMGs) in childhood asthma according to GEO. The genes that were both differentially expressed and differentially methylated were identified. Functional annotation and protein-protein interaction network construction were performed to interpret biological functions of DEGs. We performed q-RT-PCR to verify the expression of selected DEGs.One DNA methylation and 3 gene expression datasets were obtained. Four hundred forty-one DEGs and 1209 DMGs in childhood asthma were identified. Among which, 16 genes were both differentially expressed and differentially methylated in childhood asthma. Natural killer cell mediated cytotoxicity pathway, Jak-STAT signaling pathway, and Wnt signaling pathway were 3 significantly enriched pathways in childhood asthma according to our KEGG enrichment analysis. The PPI network of top 20 up- and downregulated DEGs consisted of 822 nodes and 904 edges and 2 hub proteins (UBQLN4 and MID2) were identified. The expression of 8 DEGs (GZMB, FGFBP2, CLC, TBX21, ALOX15, IL12RB2, UBQLN4) was verified by qRT-PCR and only the expression of GZMB and FGFBP2 was inconsistent with our integrated analysis.Our finding was helpful to elucidate the underlying mechanism of childhood asthma and develop new potential diagnostic biomarker and provide clues for drug design.

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

PubMed

Papanikolaou, Nikolas; Pafilis, Evangelos; Nikolaou, Stavros; Ouzounis, Christos A; Iliopoulos, Ioannis; Promponas, Vasilis J

2011-12-01

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. http://biotextquest.biol.ucy.ac.cy vprobon@ucy.ac.cy; iliopj@med.uoc.gr Supplementary data are available at Bioinformatics online.

First insights into the giant panda (Ailuropoda melanoleuca) blood transcriptome: a resource for novel gene loci and immunogenetics.

PubMed

Du, Lianming; Li, Wujiao; Fan, Zhenxin; Shen, Fujun; Yang, Mingyu; Wang, Zili; Jian, Zuoyi; Hou, Rong; Yue, Bisong; Zhang, Xiuyue

2015-07-01

The giant panda (Ailuropoda melanoleuca) is one of the most famous flagship species for conservation, and its draft genome has recently been assembled. However, the transcriptome is not yet available. In this study, the blood transcriptomes of three pandas were characterized and about 160 million sequencing reads were generated using Illumina HiSeq 2000 paired-end sequencing technology. The assembly yielded 92 598 transcripts with an average length of 1626 bp and N50 length of 2842 bp. Based on a sequence similarity search against nonredundant (nr) protein database, a total of 38 522 (41.6%) transcripts were annotated. Of these annotated transcripts, 25 142 and 8272 transcripts were assigned to gene ontology terms and clusters of orthologous group, respectively. A search against the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG) indicated that 9098 (9.83%) transcripts mapped to 324 KEGG pathways, and the best represented functional categories of pathways were signal transduction and immune system. We have also identified 23 460 microsatellites, 43 560 SNPs as well as 21 456 alternative splicing events in the assembly. Additionally, a total of 24 341 complete open reading frames (ORFs) were detected from the assembly where 1492 ORFs were found to be novel gene loci as these have not been annotated so far in any public database. © 2014 John Wiley & Sons Ltd.

Comparative transcriptome analysis of microsclerotia development in Nomuraea rileyi

PubMed Central

2013-01-01

Background Nomuraea rileyi is used as an environmental-friendly biopesticide. However, mass production and commercialization of this organism are limited due to its fastidious growth and sporulation requirements. When cultured in amended medium, we found that N. rileyi could produce microsclerotia bodies, replacing conidiophores as the infectious agent. However, little is known about the genes involved in microsclerotia development. In the present study, the transcriptomes were analyzed using next-generation sequencing technology to find the genes involved in microsclerotia development. Results A total of 4.69 Gb of clean nucleotides comprising 32,061 sequences was obtained, and 20,919 sequences were annotated (about 65%). Among the annotated sequences, only 5928 were annotated with 34 gene ontology (GO) functional categories, and 12,778 sequences were mapped to 165 pathways by searching against the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) database. Furthermore, we assessed the transcriptomic differences between cultures grown in minimal and amended medium. In total, 4808 sequences were found to be differentially expressed; 719 differentially expressed unigenes were assigned to 25 GO classes and 1888 differentially expressed unigenes were assigned to 161 KEGG pathways, including 25 enrichment pathways. Subsequently, we examined the up-regulation or uniquely expressed genes following amended medium treatment, which were also expressed on the enrichment pathway, and found that most of them participated in mediating oxidative stress homeostasis. To elucidate the role of oxidative stress in microsclerotia development, we analyzed the diversification of unigenes using quantitative reverse transcription-PCR (RT-qPCR). Conclusion Our findings suggest that oxidative stress occurs during microsclerotia development, along with a broad metabolic activity change. Our data provide the most comprehensive sequence resource available for the study of N. rileyi. We believe that the transcriptome datasets will serve as an important public information platform to accelerate studies on N. rileyi microsclerotia. PMID:23777366

PUFA diets alter the microRNA expression profiles in an inflammation rat model

PubMed Central

ZHENG, ZHENG; GE, YINLIN; ZHANG, JINYU; XUE, MEILAN; LI, QUAN; LIN, DONGLIANG; MA, WENHUI

2015-01-01

Omega-3 and -6 polyunsaturated fatty acids (PUFAs) can directly or indirectly regulate immune homeostasis via inflammatory pathways, and components of these pathways are crucial targets of microRNAs (miRNAs). However, no study has examined the changes in the miRNA transcriptome during PUFA-regulated inflammatory processes. Here, we established PUFA diet-induced autoimmune-prone (AP) and autoimmune-averse (AA) rat models, and studied their physical characteristics and immune status. Additionally, miRNA expression patterns in the rat models were compared using microarray assays and bioinformatic methods. A total of 54 miRNAs were differentially expressed in common between the AP and the AA rats, and the changes in rno-miR-19b-3p, -146b-5p and -183-5p expression were validated using stem-loop reverse transcription-quantitative polymerase chain reaction. To better understand the mechanisms underlying PUFA-regulated miRNA changes during inflammation, computational algorithms and biological databases were used to identify the target genes of the three validated miRNAs. Furthermore, Gene Ontology (GO) term annotation and KEGG pathway analyses of the miRNA targets further allowed to explore the potential implication of the miRNAs in inflammatory pathways. The predicted PUFA-regulated inflammatory pathways included the Toll-like receptor (TLR), T cell receptor (TCR), NOD-like receptor (NLR), RIG-I-like receptor (RLR), mitogen-activated protein kinase (MAPK) and the transforming growth factor-β (TGF-β) pathway. This study is the first report, to the best of our knowledge, on in vivo comparative profiling of miRNA transcriptomes in PUFA diet-induced inflammatory rat models using a microarray approach. The results provide a useful resource for future investigation of the role of PUFA-regulated miRNAs in immune homeostasis. PMID:25672643

Plasma Glycoproteomics Reveals Sepsis Outcomes Linked to Distinct Proteins in Common Pathways

PubMed Central

DeLeon-Pennell, Kristine Y.; Nguyen, Nguyen T.; de Castro Brás, Lisandra E.; Flynn, Elizabeth R.; Cannon, Presley L.; Griswold, Michael E.; Jin, Yu-Fang; Puskarich, Michael A.; Jones, Alan E.; Lindsey, Merry L.

2015-01-01

Objective Sepsis remains a predominant cause of mortality in the ICU, yet strategies to increase survival have proved largely unsuccessful. This study aimed to identify proteins linked to sepsis outcomes using a glycoproteomic approach to target extracellular proteins that trigger downstream pathways and direct patient outcomes. Design Plasma was obtained from the LacTATEs cohort. N-linked plasma glycopeptides were quantified by solid-phase extraction coupled with mass spectrometry. Glycopeptides were assigned to proteins using RefSeq and visualized in a heat map. Protein differences were validated by immunoblotting, and proteins were mapped for biological processes using Database for Annotation, Visualization and Integrated Discovery and for functional pathways using Kyoto Encyclopedia of Genes and Genomes databases. Setting Hospitalized care. Measurements and Main Results A total of 501 glycopeptides corresponding to 234 proteins were identified. Of these, 66 glycopeptides were unique to the survivor group and corresponded to 54 proteins, 60 were unique to the nonsurvivor group and corresponded to 43 proteins, and 375 were common responses between groups and corresponded to 137 proteins. Immunoblotting showed that nonsurvivors had increased total kininogen; decreased total cathepsin-L1, vascular cell adhesion molecule, periostin, and neutrophil gelatinase–associated lipocalin; and a two-fold decrease in glycosylated clusterin (all p < 0.05). Kyoto Encyclopedia of Genes and Genomes analysis identified six enriched pathways. Interestingly, survivors relied on the extrinsic pathway of the complement and coagulation cascade, whereas nonsurvivors relied on the intrinsic pathway. Conclusion This study identifies proteins linked to patient outcomes and provides insight into unexplored mechanisms that can be investigated for the identification of novel therapeutic targets. (Crit Care Med 2015; XX:00–00) PMID:26086942

The Mouse Tumor Biology Database: A Comprehensive Resource for Mouse Models of Human Cancer.

PubMed

Krupke, Debra M; Begley, Dale A; Sundberg, John P; Richardson, Joel E; Neuhauser, Steven B; Bult, Carol J

2017-11-01

Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology. The Mouse Tumor Biology (MTB) database is an expertly curated, comprehensive compendium of mouse models of human cancer. Through the enforcement of nomenclature and related annotation standards, MTB supports aggregation of data about a cancer model from diverse sources and assessment of how genetic background of a mouse strain influences the biological properties of a specific tumor type and model utility. Cancer Res; 77(21); e67-70. ©2017 AACR . ©2017 American Association for Cancer Research.

Hawaii Geothermal Project annotated bibliography: Biological resources of the geothermal subzones, the transmission corridors and the Puna District, Island of Hawaii

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, S.E.; Burgett, J.M.

1993-10-01

Task 1 of the Hawaii Geothermal Project Interagency Agreement between the Fish and Wildlife Service and the Department of Energy-Oak Ridge National Laboratory (DOE) includes an annotated bibliography of published and unpublished documents that cover biological issues related to the lowland rain forest in Puna, adjacent areas, transmission corridors, and in the proposed Hawaii Geothermal Project (HGP). The 51 documents reviewed in this report cover the main body of biological information for these projects. The full table of contents and bibliography for each document is included along with two copies (as requested in the Interagency Agreement) of the biological sectionsmore » of each document. The documents are reviewed in five main categories: (1) geothermal subzones (29 documents); (2) transmission cable routes (8 documents); (3) commercial satellite launching facility (Spaceport; 1 document); (4) manganese nodule processing facility (2 documents); (5) water resource development (1 document); and (6) ecosystem stability and introduced species (11 documents).« less

Physical properties of biological entities: an introduction to the ontology of physics for biology.

PubMed

Cook, Daniel L; Bookstein, Fred L; Gennari, John H

2011-01-01

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration. © 2011 Cook et al.

Current challenges in genome annotation through structural biology and bioinformatics.

PubMed

Furnham, Nicholas; de Beer, Tjaart A P; Thornton, Janet M

2012-10-01

With the huge volume in genomic sequences being generated from high-throughout sequencing projects the requirement for providing accurate and detailed annotations of gene products has never been greater. It is proving to be a huge challenge for computational biologists to use as much information as possible from experimental data to provide annotations for genome data of unknown function. A central component to this process is to use experimentally determined structures, which provide a means to detect homology that is not discernable from just the sequence and permit the consequences of genomic variation to be realized at the molecular level. In particular, structures also form the basis of many bioinformatics methods for improving the detailed functional annotations of enzymes in combination with similarities in sequence and chemistry. Copyright © 2012. Published by Elsevier Ltd.

Transcriptomic Analysis of the Endangered Neritid Species Clithon retropictus: De Novo Assembly, Functional Annotation, and Marker Discovery

PubMed Central

Park, So Young; Patnaik, Bharat Bhusan; Kang, Se Won; Hwang, Hee-Ju; Chung, Jong Min; Song, Dae Kwon; Sang, Min Kyu; Patnaik, Hongray Howrelia; Lee, Jae Bong; Noh, Mi Young; Kim, Changmu; Kim, Soonok; Park, Hong Seog; Lee, Jun Sang; Han, Yeon Soo; Lee, Yong Seok

2016-01-01

An aquatic gastropod belonging to the family Neritidae, Clithon retropictus is listed as an endangered class II species in South Korea. The lack of information on its genomic background limits the ability to obtain functional data resources and inhibits informed conservation planning for this species. In the present study, the transcriptomic sequencing and de novo assembly of C. retropictus generated a total of 241,696,750 high-quality reads. These assembled to 282,838 unigenes with mean and N50 lengths of 736.9 and 1201 base pairs, respectively. Of these, 125,616 unigenes were subjected to annotation analysis with known proteins in Protostome DB, COG, GO, and KEGG protein databases (BLASTX; E ≤ 0.00001) and with known nucleotides in the Unigene database (BLASTN; E ≤ 0.00001). The GO analysis indicated that cellular process, cell, and catalytic activity are the predominant GO terms in the biological process, cellular component, and molecular function categories, respectively. In addition, 2093 unigenes were distributed in 107 different KEGG pathways. Furthermore, 49,280 simple sequence repeats were identified in the unigenes (>1 kilobase sequences). This is the first report on the identification of transcriptomic and microsatellite resources for C. retropictus, which opens up the possibility of exploring traits related to the adaptation and acclimatization of this species. PMID:27455329

Enriching Genomic Resources and Marker Development from Transcript Sequences of Jatropha curcas for Microgravity Studies

PubMed Central

Tian, Wenlan; Paudel, Dev

2017-01-01

Jatropha (Jatropha curcas L.) is an economically important species with a great potential for biodiesel production. To enrich the jatropha genomic databases and resources for microgravity studies, we sequenced and annotated the transcriptome of jatropha and developed SSR and SNP markers from the transcriptome sequences. In total 1,714,433 raw reads with an average length of 441.2 nucleotides were generated. De novo assembling and clustering resulted in 115,611 uniquely assembled sequences (UASs) including 21,418 full-length cDNAs and 23,264 new jatropha transcript sequences. The whole set of UASs were fully annotated, out of which 59,903 (51.81%) were assigned with gene ontology (GO) term, 12,584 (10.88%) had orthologs in Eukaryotic Orthologous Groups (KOG), and 8,822 (7.63%) were mapped to 317 pathways in six different categories in Kyoto Encyclopedia of Genes and Genome (KEGG) database, and it contained 3,588 putative transcription factors. From the UASs, 9,798 SSRs were discovered with AG/CT as the most frequent (45.8%) SSR motif type. Further 38,693 SNPs were detected and 7,584 remained after filtering. This UAS set has enriched the current jatropha genomic databases and provided a large number of genetic markers, which can facilitate jatropha genetic improvement and many other genetic and biological studies. PMID:28154822

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

Gene discovery using next-generation pyrosequencing to develop ESTs for Phalaenopsis orchids

PubMed Central

2011-01-01

Background Orchids are one of the most diversified angiosperms, but few genomic resources are available for these non-model plants. In addition to the ecological significance, Phalaenopsis has been considered as an economically important floriculture industry worldwide. We aimed to use massively parallel 454 pyrosequencing for a global characterization of the Phalaenopsis transcriptome. Results To maximize sequence diversity, we pooled RNA from 10 samples of different tissues, various developmental stages, and biotic- or abiotic-stressed plants. We obtained 206,960 expressed sequence tags (ESTs) with an average read length of 228 bp. These reads were assembled into 8,233 contigs and 34,630 singletons. The unigenes were searched against the NCBI non-redundant (NR) protein database. Based on sequence similarity with known proteins, these analyses identified 22,234 different genes (E-value cutoff, e-7). Assembled sequences were annotated with Gene Ontology, Gene Family and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Among these annotations, over 780 unigenes encoding putative transcription factors were identified. Conclusion Pyrosequencing was effective in identifying a large set of unigenes from Phalaenopsis. The informative EST dataset we developed constitutes a much-needed resource for discovery of genes involved in various biological processes in Phalaenopsis and other orchid species. These transcribed sequences will narrow the gap between study of model organisms with many genomic resources and species that are important for ecological and evolutionary studies. PMID:21749684

Annotation of phenotypic diversity: decoupling data curation and ontology curation using Phenex.

PubMed

Balhoff, James P; Dahdul, Wasila M; Dececchi, T Alexander; Lapp, Hilmar; Mabee, Paula M; Vision, Todd J

2014-01-01

Phenex (http://phenex.phenoscape.org/) is a desktop application for semantically annotating the phenotypic character matrix datasets common in evolutionary biology. Since its initial publication, we have added new features that address several major bottlenecks in the efficiency of the phenotype curation process: allowing curators during the data curation phase to provisionally request terms that are not yet available from a relevant ontology; supporting quality control against annotation guidelines to reduce later manual review and revision; and enabling the sharing of files for collaboration among curators. We decoupled data annotation from ontology development by creating an Ontology Request Broker (ORB) within Phenex. Curators can use the ORB to request a provisional term for use in data annotation; the provisional term can be automatically replaced with a permanent identifier once the term is added to an ontology. We added a set of annotation consistency checks to prevent common curation errors, reducing the need for later correction. We facilitated collaborative editing by improving the reliability of Phenex when used with online folder sharing services, via file change monitoring and continual autosave. With the addition of these new features, and in particular the Ontology Request Broker, Phenex users have been able to focus more effectively on data annotation. Phenoscape curators using Phenex have reported a smoother annotation workflow, with much reduced interruptions from ontology maintenance and file management issues.

Expression and regulation of long noncoding RNAs during the osteogenic differentiation of periodontal ligament stem cells in the inflammatory microenvironment.

PubMed

Zhang, Qingbin; Chen, Li; Cui, Shiman; Li, Yan; Zhao, Qi; Cao, Wei; Lai, Shixiang; Yin, Sanjun; Zuo, Zhixiang; Ren, Jian

2017-10-25

Although long noncoding RNAs (lncRNAs) have been emerging as critical regulators in various tissues and biological processes, little is known about their expression and regulation during the osteogenic differentiation of periodontal ligament stem cells (PDLSCs) in inflammatory microenvironment. In this study, we have identified 63 lncRNAs that are not annotated in previous database. These novel lncRNAs were not randomly located in the genome but preferentially located near protein-coding genes related to particular functions and diseases, such as stem cell maintenance and differentiation, development disorders and inflammatory diseases. Moreover, we have identified 650 differentially expressed lncRNAs among different subsets of PDLSCs. Pathway enrichment analysis for neighboring protein-coding genes of these differentially expressed lncRNAs revealed stem cell differentiation related functions. Many of these differentially expressed lncRNAs function as competing endogenous RNAs that regulate protein-coding transcripts through competing shared miRNAs.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission.

PubMed

Geib, Scott M; Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle; Sim, Sheina B

2018-04-01

One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI's annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline. The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission

PubMed Central

Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle

2018-01-01

Abstract Background One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI’s annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. Findings The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline Conclusions The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI. PMID:29635297

Analysis of Aspergillus nidulans metabolism at the genome-scale

PubMed Central

David, Helga; Özçelik, İlknur Ş; Hofmann, Gerald; Nielsen, Jens

2008-01-01

Background Aspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned a function. Results In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated, and to look for candidate ORFs in the genome of A. nidulans by comparing its sequence to sequences of well-characterized genes in other species encoding the function of interest. A classification system, based on defined criteria, was developed for evaluating and selecting the ORFs among the candidates, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans. Conclusion We demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism. Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data. PMID:18405346

Cross-organism learning method to discover new gene functionalities.

PubMed

Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

2016-04-01

Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones, without influence of the evolutionary distance between the considered organisms. The generated ranked lists of reliably predicted annotations, which describe novel gene functionalities and have an associated likelihood value, are very valuable both to complement available annotations, for better coverage in biomedical knowledge discovery analyses, and to quicken the annotation curation process, by focusing it on the prioritized novel annotations predicted. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An integrative data mining approach to identifying adverse outcome pathway signatures.

PubMed

Oki, Noffisat O; Edwards, Stephen W

2016-03-28

The Adverse Outcome Pathway (AOP) framework is a tool for making biological connections and summarizing key information across different levels of biological organization to connect biological perturbations at the molecular level to adverse outcomes for an individual or population. Computational approaches to explore and determine these connections can accelerate the assembly of AOPs. By leveraging the wealth of publicly available data covering chemical effects on biological systems, computationally-predicted AOPs (cpAOPs) were assembled via data mining of high-throughput screening (HTS) in vitro data, in vivo data and other disease phenotype information. Frequent Itemset Mining (FIM) was used to find associations between the gene targets of ToxCast HTS assays and disease data from Comparative Toxicogenomics Database (CTD) by using the chemicals as the common aggregators between datasets. The method was also used to map gene expression data to disease data from CTD. A cpAOP network was defined by considering genes and diseases as nodes and FIM associations as edges. This network contained 18,283 gene to disease associations for the ToxCast data and 110,253 for CTD gene expression. Two case studies show the value of the cpAOP network by extracting subnetworks focused either on fatty liver disease or the Aryl Hydrocarbon Receptor (AHR). The subnetwork surrounding fatty liver disease included many genes known to play a role in this disease. When querying the cpAOP network with the AHR gene, an interesting subnetwork including glaucoma was identified. While substantial literature exists to support the potential for AHR ligands to elicit glaucoma, it was not explicitly captured in the public annotation information in CTD. The subnetwork from this analysis suggests a cpAOP that includes changes in CYP1B1 expression, which has been previously established in the literature as a primary cause of glaucoma. These case studies highlight the value in integrating multiple data sources when defining cpAOPs for HTS data. Copyright © 2016. Published by Elsevier Ireland Ltd.

Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses.

PubMed

de Oliveira Dal'Molin, Cristiana G; Orellana, Camila; Gebbie, Leigh; Steen, Jennifer; Hodson, Mark P; Chrysanthopoulos, Panagiotis; Plan, Manuel R; McQualter, Richard; Palfreyman, Robin W; Nielsen, Lars K

2016-01-01

The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses.

Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses

PubMed Central

de Oliveira Dal'Molin, Cristiana G.; Orellana, Camila; Gebbie, Leigh; Steen, Jennifer; Hodson, Mark P.; Chrysanthopoulos, Panagiotis; Plan, Manuel R.; McQualter, Richard; Palfreyman, Robin W.; Nielsen, Lars K.

2016-01-01

The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses. PMID:27559337

Integrating sequence and structural biology with DAS

PubMed Central

Prlić, Andreas; Down, Thomas A; Kulesha, Eugene; Finn, Robert D; Kähäri, Andreas; Hubbard, Tim JP

2007-01-01

Background The Distributed Annotation System (DAS) is a network protocol for exchanging biological data. It is frequently used to share annotations of genomes and protein sequence. Results Here we present several extensions to the current DAS 1.5 protocol. These provide new commands to share alignments, three dimensional molecular structure data, add the possibility for registration and discovery of DAS servers, and provide a convention how to provide different types of data plots. We present examples of web sites and applications that use the new extensions. We operate a public registry of DAS sources, which now includes entries for more than 250 distinct sources. Conclusion Our DAS extensions are essential for the management of the growing number of services and exchange of diverse biological data sets. In addition the extensions allow new types of applications to be developed and scientific questions to be addressed. The registry of DAS sources is available at PMID:17850653

Literature classification for semi-automated updating of biological knowledgebases

PubMed Central

2013-01-01

Background As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. PMID:24564403

Composite Structural Motifs of Binding Sites for Delineating Biological Functions of Proteins

PubMed Central

Kinjo, Akira R.; Nakamura, Haruki

2012-01-01

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures. PMID:22347478

BioSAVE: display of scored annotation within a sequence context.

PubMed

Pollock, Richard F; Adryan, Boris

2008-03-20

Visualization of sequence annotation is a common feature in many bioinformatics tools. For many applications it is desirable to restrict the display of such annotation according to a score cutoff, as biological interpretation can be difficult in the presence of the entire data. Unfortunately, many visualisation solutions are somewhat static in the way they handle such score cutoffs. We present BioSAVE, a sequence annotation viewer with on-the-fly selection of visualisation thresholds for each feature. BioSAVE is a versatile OS X program for visual display of scored features (annotation) within a sequence context. The program reads sequence and additional supplementary annotation data (e.g., position weight matrix matches, conservation scores, structural domains) from a variety of commonly used file formats and displays them graphically. Onscreen controls then allow for live customisation of these graphics, including on-the-fly selection of visualisation thresholds for each feature. Possible applications of the program include display of transcription factor binding sites in a genomic context or the visualisation of structural domain assignments in protein sequences and many more. The dynamic visualisation of these annotations is useful, e.g., for the determination of cutoff values of predicted features to match experimental data. Program, source code and exemplary files are freely available at the BioSAVE homepage.

BioSAVE: Display of scored annotation within a sequence context

PubMed Central

Pollock, Richard F; Adryan, Boris

2008-01-01

Background Visualization of sequence annotation is a common feature in many bioinformatics tools. For many applications it is desirable to restrict the display of such annotation according to a score cutoff, as biological interpretation can be difficult in the presence of the entire data. Unfortunately, many visualisation solutions are somewhat static in the way they handle such score cutoffs. Results We present BioSAVE, a sequence annotation viewer with on-the-fly selection of visualisation thresholds for each feature. BioSAVE is a versatile OS X program for visual display of scored features (annotation) within a sequence context. The program reads sequence and additional supplementary annotation data (e.g., position weight matrix matches, conservation scores, structural domains) from a variety of commonly used file formats and displays them graphically. Onscreen controls then allow for live customisation of these graphics, including on-the-fly selection of visualisation thresholds for each feature. Conclusion Possible applications of the program include display of transcription factor binding sites in a genomic context or the visualisation of structural domain assignments in protein sequences and many more. The dynamic visualisation of these annotations is useful, e.g., for the determination of cutoff values of predicted features to match experimental data. Program, source code and exemplary files are freely available at the BioSAVE homepage. PMID:18366701