An integrated database-pipeline system for studying single nucleotide polymorphisms and diseases.

PubMed

Yang, Jin Ok; Hwang, Sohyun; Oh, Jeongsu; Bhak, Jong; Sohn, Tae-Kwon

2008-12-12

Studies on the relationship between disease and genetic variations such as single nucleotide polymorphisms (SNPs) are important. Genetic variations can cause disease by influencing important biological regulation processes. Despite the needs for analyzing SNP and disease correlation, most existing databases provide information only on functional variants at specific locations on the genome, or deal with only a few genes associated with disease. There is no combined resource to widely support gene-, SNP-, and disease-related information, and to capture relationships among such data. Therefore, we developed an integrated database-pipeline system for studying SNPs and diseases. To implement the pipeline system for the integrated database, we first unified complicated and redundant disease terms and gene names using the Unified Medical Language System (UMLS) for classification and noun modification, and the HUGO Gene Nomenclature Committee (HGNC) and NCBI gene databases. Next, we collected and integrated representative databases for three categories of information. For genes and proteins, we examined the NCBI mRNA, UniProt, UCSC Table Track and MitoDat databases. For genetic variants we used the dbSNP, JSNP, ALFRED, and HGVbase databases. For disease, we employed OMIM, GAD, and HGMD databases. The database-pipeline system provides a disease thesaurus, including genes and SNPs associated with disease. The search results for these categories are available on the web page http://diseasome.kobic.re.kr/, and a genome browser is also available to highlight findings, as well as to permit the convenient review of potentially deleterious SNPs among genes strongly associated with specific diseases and clinical phenotypes. Our system is designed to capture the relationships between SNPs associated with disease and disease-causing genes. The integrated database-pipeline provides a list of candidate genes and SNP markers for evaluation in both epidemiological and molecular biological approaches to diseases-gene association studies. Furthermore, researchers then can decide semi-automatically the data set for association studies while considering the relationships between genetic variation and diseases. The database can also be economical for disease-association studies, as well as to facilitate an understanding of the processes which cause disease. Currently, the database contains 14,674 SNP records and 109,715 gene records associated with human diseases and it is updated at regular intervals.

Liverome: a curated database of liver cancer-related gene signatures with self-contained context information.

PubMed

Lee, Langho; Wang, Kai; Li, Gang; Xie, Zhi; Wang, Yuli; Xu, Jiangchun; Sun, Shaoxian; Pocalyko, David; Bhak, Jong; Kim, Chulhong; Lee, Kee-Ho; Jang, Ye Jin; Yeom, Young Il; Yoo, Hyang-Sook; Hwang, Seungwoo

2011-11-30

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene's expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc, or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. Liverome's data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a straightforward retrieval of known differential expression information on a query gene and a comparison of signatures to prioritize common genes. The utility of Liverome-collected data is shown by case studies in which useful biological insights on HCC are produced. Liverome database provides a comprehensive collection of well-curated HCC gene signatures and straightforward interfaces for gene search and signature comparison as well. Liverome is available at http://liverome.kobic.re.kr.

Identification of Novel Tissue-Specific Genes by Analysis of Microarray Databases: A Human and Mouse Model

PubMed Central

Suh, Yeunsu; Davis, Michael E.; Lee, Kichoon

2013-01-01

Understanding the tissue-specific pattern of gene expression is critical in elucidating the molecular mechanisms of tissue development, gene function, and transcriptional regulations of biological processes. Although tissue-specific gene expression information is available in several databases, follow-up strategies to integrate and use these data are limited. The objective of the current study was to identify and evaluate novel tissue-specific genes in human and mouse tissues by performing comparative microarray database analysis and semi-quantitative PCR analysis. We developed a powerful approach to predict tissue-specific genes by analyzing existing microarray data from the NCBI′s Gene Expression Omnibus (GEO) public repository. We investigated and confirmed tissue-specific gene expression in the human and mouse kidney, liver, lung, heart, muscle, and adipose tissue. Applying our novel comparative microarray approach, we confirmed 10 kidney, 11 liver, 11 lung, 11 heart, 8 muscle, and 8 adipose specific genes. The accuracy of this approach was further verified by employing semi-quantitative PCR reaction and by searching for gene function information in existing publications. Three novel tissue-specific genes were discovered by this approach including AMDHD1 (amidohydrolase domain containing 1) in the liver, PRUNE2 (prune homolog 2) in the heart, and ACVR1C (activin A receptor, type IC) in adipose tissue. We further confirmed the tissue-specific expression of these 3 novel genes by real-time PCR. Among them, ACVR1C is adipose tissue-specific and adipocyte-specific in adipose tissue, and can be used as an adipocyte developmental marker. From GEO profiles, we predicted the processes in which AMDHD1 and PRUNE2 may participate. Our approach provides a novel way to identify new sets of tissue-specific genes and to predict functions in which they may be involved. PMID:23741331

The NCBI BioSystems database.

PubMed

Geer, Lewis Y; Marchler-Bauer, Aron; Geer, Renata C; Han, Lianyi; He, Jane; He, Siqian; Liu, Chunlei; Shi, Wenyao; Bryant, Stephen H

2010-01-01

The NCBI BioSystems database, found at http://www.ncbi.nlm.nih.gov/biosystems/, centralizes and cross-links existing biological systems databases, increasing their utility and target audience by integrating their pathways and systems into NCBI resources. This integration allows users of NCBI's Entrez databases to quickly categorize proteins, genes and small molecules by metabolic pathway, disease state or other BioSystem type, without requiring time-consuming inference of biological relationships from the literature or multiple experimental datasets.

The NCBI BioSystems database

PubMed Central

Geer, Lewis Y.; Marchler-Bauer, Aron; Geer, Renata C.; Han, Lianyi; He, Jane; He, Siqian; Liu, Chunlei; Shi, Wenyao; Bryant, Stephen H.

2010-01-01

The NCBI BioSystems database, found at http://www.ncbi.nlm.nih.gov/biosystems/, centralizes and cross-links existing biological systems databases, increasing their utility and target audience by integrating their pathways and systems into NCBI resources. This integration allows users of NCBI’s Entrez databases to quickly categorize proteins, genes and small molecules by metabolic pathway, disease state or other BioSystem type, without requiring time-consuming inference of biological relationships from the literature or multiple experimental datasets. PMID:19854944

A graph-based semantic similarity measure for the gene ontology.

PubMed

Alvarez, Marco A; Yan, Changhui

2011-12-01

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

AgeFactDB—the JenAge Ageing Factor Database—towards data integration in ageing research

PubMed Central

Hühne, Rolf; Thalheim, Torsten; Sühnel, Jürgen

2014-01-01

AgeFactDB (http://agefactdb.jenage.de) is a database aimed at the collection and integration of ageing phenotype data including lifespan information. Ageing factors are considered to be genes, chemical compounds or other factors such as dietary restriction, whose action results in a changed lifespan or another ageing phenotype. Any information related to the effects of ageing factors is called an observation and is presented on observation pages. To provide concise access to the complete information for a particular ageing factor, corresponding observations are also summarized on ageing factor pages. In a first step, ageing-related data were primarily taken from existing databases such as the Ageing Gene Database—GenAge, the Lifespan Observations Database and the Dietary Restriction Gene Database—GenDR. In addition, we have started to include new ageing-related information. Based on homology data taken from the HomoloGene Database, AgeFactDB also provides observation and ageing factor pages of genes that are homologous to known ageing-related genes. These homologues are considered as candidate or putative ageing-related genes. AgeFactDB offers a variety of search and browse options, and also allows the download of ageing factor or observation lists in TSV, CSV and XML formats. PMID:24217911

BioWarehouse: a bioinformatics database warehouse toolkit

PubMed Central

Lee, Thomas J; Pouliot, Yannick; Wagner, Valerie; Gupta, Priyanka; Stringer-Calvert, David WJ; Tenenbaum, Jessica D; Karp, Peter D

2006-01-01

Background This article addresses the problem of interoperation of heterogeneous bioinformatics databases. Results We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. Conclusion BioWarehouse embodies significant progress on the database integration problem for bioinformatics. PMID:16556315

BioWarehouse: a bioinformatics database warehouse toolkit.

PubMed

Lee, Thomas J; Pouliot, Yannick; Wagner, Valerie; Gupta, Priyanka; Stringer-Calvert, David W J; Tenenbaum, Jessica D; Karp, Peter D

2006-03-23

This article addresses the problem of interoperation of heterogeneous bioinformatics databases. We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. BioWarehouse embodies significant progress on the database integration problem for bioinformatics.

CoReCG: a comprehensive database of genes associated with colon-rectal cancer

PubMed Central

Agarwal, Rahul; Kumar, Binayak; Jayadev, Msk; Raghav, Dhwani; Singh, Ashutosh

2016-01-01

Cancer of large intestine is commonly referred as colorectal cancer, which is also the third most frequently prevailing neoplasm across the globe. Though, much of work is being carried out to understand the mechanism of carcinogenesis and advancement of this disease but, fewer studies has been performed to collate the scattered information of alterations in tumorigenic cells like genes, mutations, expression changes, epigenetic alteration or post translation modification, genetic heterogeneity. Earlier findings were mostly focused on understanding etiology of colorectal carcinogenesis but less emphasis were given for the comprehensive review of the existing findings of individual studies which can provide better diagnostics based on the suggested markers in discrete studies. Colon Rectal Cancer Gene Database (CoReCG), contains 2056 colon-rectal cancer genes information involved in distinct colorectal cancer stages sourced from published literature with an effective knowledge based information retrieval system. Additionally, interactive web interface enriched with various browsing sections, augmented with advance search facility for querying the database is provided for user friendly browsing, online tools for sequence similarity searches and knowledge based schema ensures a researcher friendly information retrieval mechanism. Colorectal cancer gene database (CoReCG) is expected to be a single point source for identification of colorectal cancer-related genes, thereby helping with the improvement of classification, diagnosis and treatment of human cancers. Database URL: lms.snu.edu.in/corecg PMID:27114494

Analysis of Cytoskeletal and Motility Proteins in the Sea Urchin Genome Assembly

PubMed Central

RL, Morris; MP, Hoffman; RA, Obar; SS, McCafferty; IR, Gibbons; AD, Leone; J, Cool; EL, Allgood; AM, Musante; KM, Judkins; BJ, Rossetti; AP, Rawson; DR, Burgess

2007-01-01

The sea urchin embryo is a classical model system for studying the role of the cytoskeleton in such events as fertilization, mitosis, cleavage, cell migration and gastrulation. We have conducted an analysis of gene models derived from the Strongylocentrotus purpuratus genome assembly and have gathered strong evidence for the existence of multiple gene families encoding cytoskeletal proteins and their regulators in sea urchin. While many cytoskeletal genes have been cloned from sea urchin with sequences already existing in public databases, genome analysis reveals a significantly higher degree of diversity within certain gene families. Furthermore, genes are described corresponding to homologs of cytoskeletal proteins not previously documented in sea urchins. To illustrate the varying degree of sequence diversity that exists within cytoskeletal gene families, we conducted an analysis of genes encoding actins, specific actin-binding proteins, myosins, tubulins, kinesins, dyneins, specific microtubule-associated proteins, and intermediate filaments. We conducted ontological analysis of select genes to better understand the relatedness of urchin cytoskeletal genes to those of other deuterostomes. We analyzed developmental expression (EST) data to confirm the existence of select gene models and to understand their differential expression during various stages of early development. PMID:17027957

Integrating In Silico Resources to Map a Signaling Network

PubMed Central

Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

2013-01-01

The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol to building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

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.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

PubMed

Monteiro, Pedro Tiago; Pais, Pedro; Costa, Catarina; Manna, Sauvagya; Sá-Correia, Isabel; Teixeira, Miguel Cacho

2017-01-04

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

miRWalk--database: prediction of possible miRNA binding sites by "walking" the genes of three genomes.

PubMed

Dweep, Harsh; Sticht, Carsten; Pandey, Priyanka; Gretz, Norbert

2011-10-01

MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3'-UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3'-UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat. All mRNAs, mitochondrial genes and 10 kb upstream flanking regions of all known genes of human, mouse and rat were analyzed by using a newly developed algorithm named 'miRWalk' as well as with eight already established programs for putative miRNA binding sites. An automated and extensive text-mining search was performed on PubMed database to extract validated information on miRNAs. Combined information was put into a MySQL database. miRWalk presents predicted and validated information on miRNA-target interaction. Such a resource enables researchers to validate new targets of miRNA not only on 3'-UTR, but also on the other regions of all known genes. The 'Validated Target module' is updated every month and the 'Predicted Target module' is updated every 6 months. miRWalk is freely available at http://mirwalk.uni-hd.de/. Copyright © 2011 Elsevier Inc. All rights reserved.

Detection of alternative splice variants at the proteome level in Aspergillus flavus.

PubMed

Chang, Kung-Yen; Georgianna, D Ryan; Heber, Steffen; Payne, Gary A; Muddiman, David C

2010-03-05

Identification of proteins from proteolytic peptides or intact proteins plays an essential role in proteomics. Researchers use search engines to match the acquired peptide sequences to the target proteins. However, search engines depend on protein databases to provide candidates for consideration. Alternative splicing (AS), the mechanism where the exon of pre-mRNAs can be spliced and rearranged to generate distinct mRNA and therefore protein variants, enable higher eukaryotic organisms, with only a limited number of genes, to have the requisite complexity and diversity at the proteome level. Multiple alternative isoforms from one gene often share common segments of sequences. However, many protein databases only include a limited number of isoforms to keep minimal redundancy. As a result, the database search might not identify a target protein even with high quality tandem MS data and accurate intact precursor ion mass. We computationally predicted an exhaustive list of putative isoforms of Aspergillus flavus proteins from 20 371 expressed sequence tags to investigate whether an alternative splicing protein database can assign a greater proportion of mass spectrometry data. The newly constructed AS database provided 9807 new alternatively spliced variants in addition to 12 832 previously annotated proteins. The searches of the existing tandem MS spectra data set using the AS database identified 29 new proteins encoded by 26 genes. Nine fungal genes appeared to have multiple protein isoforms. In addition to the discovery of splice variants, AS database also showed potential to improve genome annotation. In summary, the introduction of an alternative splicing database helps identify more proteins and unveils more information about a proteome.

Transcriptomic Analysis and the Expression of Disease-Resistant Genes in Oryza meyeriana under Native Condition

PubMed Central

He, Bin; Tao, Xiang; Gu, Yinghong; Wei, Changhe; Cheng, Xiaojie; Xiao, Suqin; Cheng, Zaiquan; Zhang, Yizheng

2015-01-01

Oryza meyeriana (O. meyeriana), with a GG genome type (2n = 24), accumulated plentiful excellent characteristics with respect to resistance to many diseases such as rice shade and blast, even immunity to bacterial blight. It is very important to know if the diseases-resistant genes exist and express in this wild rice under native conditions. However, limited genomic or transcriptomic data of O. meyeriana are currently available. In this study, we present the first comprehensive characterization of the O. meyeriana transcriptome using RNA-seq and obtained 185,323 contigs with an average length of 1,692 bp and an N50 of 2,391 bp. Through differential expression analysis, it was found that there were most tissue-specifically expressed genes in roots, and next to stems and leaves. By similarity search against protein databases, 146,450 had at least a significant alignment to existed gene models. Comparison with the Oryza sativa (japonica-type Nipponbare and indica-type 93–11) genomes revealed that 13% of the O. meyeriana contigs had not been detected in O. sativa. Many diseases-resistant genes, such as bacterial blight resistant, blast resistant, rust resistant, fusarium resistant, cyst nematode resistant and downy mildew gene, were mined from the transcriptomic database. There are two kinds of rice bacterial blight-resistant genes (Xa1 and Xa26) differentially or specifically expressed in O. meyeriana. The 4 Xa1 contigs were all only expressed in root, while three of Xa26 contigs have the highest expression level in leaves, two of Xa26 contigs have the highest expression profile in stems and one of Xa26 contigs was expressed dominantly in roots. The transcriptomic database of O. meyeriana has been constructed and many diseases-resistant genes were found to express under native condition, which provides a foundation for future discovery of a number of novel genes and provides a basis for studying the molecular mechanisms associated with disease resistance in O. meyeriana. PMID:26640944

Mining biological databases for candidate disease genes

NASA Astrophysics Data System (ADS)

Braun, Terry A.; Scheetz, Todd; Webster, Gregg L.; Casavant, Thomas L.

2001-07-01

The publicly-funded effort to sequence the complete nucleotide sequence of the human genome, the Human Genome Project (HGP), has currently produced more than 93% of the 3 billion nucleotides of the human genome into a preliminary `draft' format. In addition, several valuable sources of information have been developed as direct and indirect results of the HGP. These include the sequencing of model organisms (rat, mouse, fly, and others), gene discovery projects (ESTs and full-length), and new technologies such as expression analysis and resources (micro-arrays or gene chips). These resources are invaluable for the researchers identifying the functional genes of the genome that transcribe and translate into the transcriptome and proteome, both of which potentially contain orders of magnitude more complexity than the genome itself. Preliminary analyses of this data identified approximately 30,000 - 40,000 human `genes.' However, the bulk of the effort still remains -- to identify the functional and structural elements contained within the transcriptome and proteome, and to associate function in the transcriptome and proteome to genes. A fortuitous consequence of the HGP is the existence of hundreds of databases containing biological information that may contain relevant data pertaining to the identification of disease-causing genes. The task of mining these databases for information on candidate genes is a commercial application of enormous potential. We are developing a system to acquire and mine data from specific databases to aid our efforts to identify disease genes. A high speed cluster of Linux of workstations is used to analyze sequence and perform distributed sequence alignments as part of our data mining and processing. This system has been used to mine GeneMap99 sequences within specific genomic intervals to identify potential candidate disease genes associated with Bardet-Biedle Syndrome (BBS).

SGP-1: Prediction and Validation of Homologous Genes Based on Sequence Alignments

PubMed Central

Wiehe, Thomas; Gebauer-Jung, Steffi; Mitchell-Olds, Thomas; Guigó, Roderic

2001-01-01

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors. PMID:11544202

Novel primers for complete mitochondrial cytochrome b genesequencing in mammals

USGS Publications Warehouse

Naidu, Ashwin; Fitak, Robert R.; Munguia-Vega, Adrian; Culver, Melanie

2011-01-01

Sequence-based species identification relies on the extent and integrity of sequence data available in online databases such as GenBank. When identifying species from a sample of unknown origin, partial DNA sequences obtained from the sample are aligned against existing sequences in databases. When the sequence from the matching species is not present in the database, high-scoring alignments with closely related sequences might produce unreliable results on species identity. For species identification in mammals, the cytochrome b (cyt b) gene has been identified to be highly informative; thus, large amounts of reference sequence data from the cyt b gene are much needed. To enhance availability of cyt b gene sequence data on a large number of mammalian species in GenBank and other such publicly accessible online databases, we identified a primer pair for complete cyt b gene sequencing in mammals. Using this primer pair, we successfully PCR amplified and sequenced the complete cyt b gene from 40 of 44 mammalian species representing 10 orders of mammals. We submitted 40 complete, correctly annotated, cyt b protein coding sequences to GenBank. To our knowledge, this is the first single primer pair to amplify the complete cyt b gene in a broad range of mammalian species. This primer pair can be used for the addition of new cyt b gene sequences and to enhance data available on species represented in GenBank. The availability of novel and complete gene sequences as high-quality reference data can improve the reliability of sequence-based species identification.

SAMMD: Staphylococcus aureus microarray meta-database.

PubMed

Nagarajan, Vijayaraj; Elasri, Mohamed O

2007-10-02

Staphylococcus aureus is an important human pathogen, causing a wide variety of diseases ranging from superficial skin infections to severe life threatening infections. S. aureus is one of the leading causes of nosocomial infections. Its ability to resist multiple antibiotics poses a growing public health problem. In order to understand the mechanism of pathogenesis of S. aureus, several global expression profiles have been developed. These transcriptional profiles included regulatory mutants of S. aureus and growth of wild type under different growth conditions. The abundance of these profiles has generated a large amount of data without a uniform annotation system to comprehensively examine them. We report the development of the Staphylococcus aureus Microarray meta-database (SAMMD) which includes data from all the published transcriptional profiles. SAMMD is a web-accessible database that helps users to perform a variety of analysis against and within the existing transcriptional profiles. SAMMD is a relational database that uses MySQL as the back end and PHP/JavaScript/DHTML as the front end. The database is normalized and consists of five tables, which holds information about gene annotations, regulated gene lists, experimental details, references, and other details. SAMMD data is collected from the peer-reviewed published articles. Data extraction and conversion was done using perl scripts while data entry was done through phpMyAdmin tool. The database is accessible via a web interface that contains several features such as a simple search by ORF ID, gene name, gene product name, advanced search using gene lists, comparing among datasets, browsing, downloading, statistics, and help. The database is licensed under General Public License (GPL). SAMMD is hosted and available at http://www.bioinformatics.org/sammd/. Currently there are over 9500 entries for regulated genes, from 67 microarray experiments. SAMMD will help staphylococcal scientists to analyze their expression data and understand it at global level. It will also allow scientists to compare and contrast their transcriptome to that of the other published transcriptomes.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

PubMed Central

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K.

2018-01-01

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly. PMID:29470400

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

PubMed

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K; Mathé, Ewy A

2018-02-22

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

SAMMD: Staphylococcus aureus Microarray Meta-Database

PubMed Central

Nagarajan, Vijayaraj; Elasri, Mohamed O

2007-01-01

Background Staphylococcus aureus is an important human pathogen, causing a wide variety of diseases ranging from superficial skin infections to severe life threatening infections. S. aureus is one of the leading causes of nosocomial infections. Its ability to resist multiple antibiotics poses a growing public health problem. In order to understand the mechanism of pathogenesis of S. aureus, several global expression profiles have been developed. These transcriptional profiles included regulatory mutants of S. aureus and growth of wild type under different growth conditions. The abundance of these profiles has generated a large amount of data without a uniform annotation system to comprehensively examine them. We report the development of the Staphylococcus aureus Microarray meta-database (SAMMD) which includes data from all the published transcriptional profiles. SAMMD is a web-accessible database that helps users to perform a variety of analysis against and within the existing transcriptional profiles. Description SAMMD is a relational database that uses MySQL as the back end and PHP/JavaScript/DHTML as the front end. The database is normalized and consists of five tables, which holds information about gene annotations, regulated gene lists, experimental details, references, and other details. SAMMD data is collected from the peer-reviewed published articles. Data extraction and conversion was done using perl scripts while data entry was done through phpMyAdmin tool. The database is accessible via a web interface that contains several features such as a simple search by ORF ID, gene name, gene product name, advanced search using gene lists, comparing among datasets, browsing, downloading, statistics, and help. The database is licensed under General Public License (GPL). Conclusion SAMMD is hosted and available at . Currently there are over 9500 entries for regulated genes, from 67 microarray experiments. SAMMD will help staphylococcal scientists to analyze their expression data and understand it at global level. It will also allow scientists to compare and contrast their transcriptome to that of the other published transcriptomes. PMID:17910768

Determining the semantic similarities among Gene Ontology terms.

PubMed

Taha, Kamal

2013-05-01

We present in this paper novel techniques that determine the semantic relationships among GeneOntology (GO) terms. We implemented these techniques in a prototype system called GoSE, which resides between user application and GO database. Given a set S of GO terms, GoSE would return another set S' of GO terms, where each term in S' is semantically related to each term in S. Most current research is focused on determining the semantic similarities among GO ontology terms based solely on their IDs and proximity to one another in the GO graph structure, while overlooking the contexts of the terms, which may lead to erroneous results. The context of a GO term T is the set of other terms, whose existence in the GO graph structure is dependent on T. We propose novel techniques that determine the contexts of terms based on the concept of existence dependency. We present a stack-based sort-merge algorithm employing these techniques for determining the semantic similarities among GO terms.We evaluated GoSE experimentally and compared it with three existing methods. The results of measuring the semantic similarities among genes in KEGG and Pfam pathways retrieved from the DBGET and Sanger Pfam databases, respectively, have shown that our method outperforms the other three methods in recall and precision.

FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer.

PubMed

Panigrahi, Priyabrata; Jere, Abhay; Anamika, Krishanpal

2018-01-01

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

FunSimMat: a comprehensive functional similarity database

PubMed Central

Schlicker, Andreas; Albrecht, Mario

2008-01-01

Functional similarity based on Gene Ontology (GO) annotation is used in diverse applications like gene clustering, gene expression data analysis, protein interaction prediction and evaluation. However, there exists no comprehensive resource of functional similarity values although such a database would facilitate the use of functional similarity measures in different applications. Here, we describe FunSimMat (Functional Similarity Matrix, http://funsimmat.bioinf.mpi-inf.mpg.de/), a large new database that provides several different semantic similarity measures for GO terms. It offers various precomputed functional similarity values for proteins contained in UniProtKB and for protein families in Pfam and SMART. The web interface allows users to efficiently perform both semantic similarity searches with GO terms and functional similarity searches with proteins or protein families. All results can be downloaded in tab-delimited files for use with other tools. An additional XML–RPC interface gives automatic online access to FunSimMat for programs and remote services. PMID:17932054

CnidBase: The Cnidarian Evolutionary Genomics Database

PubMed Central

Ryan, Joseph F.; Finnerty, John R.

2003-01-01

CnidBase, the Cnidarian Evolutionary Genomics Database, is a tool for investigating the evolutionary, developmental and ecological factors that affect gene expression and gene function in cnidarians. In turn, CnidBase will help to illuminate the role of specific genes in shaping cnidarian biodiversity in the present day and in the distant past. CnidBase highlights evolutionary changes between species within the phylum Cnidaria and structures genomic and expression data to facilitate comparisons to non-cnidarian metazoans. CnidBase aims to further the progress that has already been made in the realm of cnidarian evolutionary genomics by creating a central community resource which will help drive future research and facilitate more accurate classification and comparison of new experimental data with existing data. CnidBase is available at http://cnidbase.bu.edu/. PMID:12519972

CGO: utilizing and integrating gene expression microarray data in clinical research and data management.

PubMed

Bumm, Klaus; Zheng, Mingzhong; Bailey, Clyde; Zhan, Fenghuang; Chiriva-Internati, M; Eddlemon, Paul; Terry, Julian; Barlogie, Bart; Shaughnessy, John D

2002-02-01

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.

VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine).

PubMed

Wong, Darren C J; Sweetman, Crystal; Drew, Damian P; Ford, Christopher M

2013-12-16

Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis) whereby the recovered sub-networks reconfirm established plant gene functions and also identify novel associations. Together, we present valuable insights into grapevine transcriptional regulation by developing network models applicable to researchers in their prioritisation of gene candidates, for on-going study of biological processes related to grapevine development, metabolism and stress responses.

Database of cattle candidate genes and genetic markers for milk production and mastitis

PubMed Central

Ogorevc, J; Kunej, T; Razpet, A; Dovc, P

2009-01-01

A cattle database of candidate genes and genetic markers for milk production and mastitis has been developed to provide an integrated research tool incorporating different types of information supporting a genomic approach to study lactation, udder development and health. The database contains 943 genes and genetic markers involved in mammary gland development and function, representing candidates for further functional studies. The candidate loci were drawn on a genetic map to reveal positional overlaps. For identification of candidate loci, data from seven different research approaches were exploited: (i) gene knockouts or transgenes in mice that result in specific phenotypes associated with mammary gland (143 loci); (ii) cattle QTL for milk production (344) and mastitis related traits (71); (iii) loci with sequence variations that show specific allele-phenotype interactions associated with milk production (24) or mastitis (10) in cattle; (iv) genes with expression profiles associated with milk production (207) or mastitis (107) in cattle or mouse; (v) cattle milk protein genes that exist in different genetic variants (9); (vi) miRNAs expressed in bovine mammary gland (32) and (vii) epigenetically regulated cattle genes associated with mammary gland function (1). Fourty-four genes found by multiple independent analyses were suggested as the most promising candidates and were further in silico analysed for expression levels in lactating mammary gland, genetic variability and top biological functions in functional networks. A miRNA target search for mammary gland expressed miRNAs identified 359 putative binding sites in 3′UTRs of candidate genes. PMID:19508288

RCDB: Renal Cancer Gene Database.

PubMed

Ramana, Jayashree

2012-05-18

Renal cell carcinoma or RCC is one of the common and most lethal urological cancers, with 40% of the patients succumbing to death because of metastatic progression of the disease. Treatment of metastatic RCC remains highly challenging because of its resistance to chemotherapy as well as radiotherapy, besides surgical resection. Whereas RCC comprises tumors with differing histological types, clear cell RCC remains the most common. A major problem in the clinical management of patients presenting with localized ccRCC is the inability to determine tumor aggressiveness and accurately predict the risk of metastasis following surgery. As a measure to improve the diagnosis and prognosis of RCC, researchers have identified several molecular markers through a number of techniques. However the wealth of information available is scattered in literature and not easily amenable to data-mining. To reduce this gap, this work describes a comprehensive repository called Renal Cancer Gene Database, as an integrated gateway to study renal cancer related data. Renal Cancer Gene Database is a manually curated compendium of 240 protein-coding and 269 miRNA genes contributing to the etiology and pathogenesis of various forms of renal cell carcinomas. The protein coding genes have been classified according to the kind of gene alteration observed in RCC. RCDB also includes the miRNAsdysregulated in RCC, along with the corresponding information regarding the type of RCC and/or metastatic or prognostic significance. While some of the miRNA genes showed an association with other types of cancers few were unique to RCC. Users can query the database using keywords, category and chromosomal location of the genes. The knowledgebase can be freely accessed via a user-friendly web interface at http://www.juit.ac.in/attachments/jsr/rcdb/homenew.html. It is hoped that this database would serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in RCC genetics.

DEXTER: Disease-Expression Relation Extraction from Text.

PubMed

Gupta, Samir; Dingerdissen, Hayley; Ross, Karen E; Hu, Yu; Wu, Cathy H; Mazumder, Raja; Vijay-Shanker, K

2018-01-01

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets

PubMed Central

Brozovic, Matija; Dantec, Christelle; Dardaillon, Justine; Dauga, Delphine; Faure, Emmanuel; Gineste, Mathieu; Louis, Alexandra; Naville, Magali; Nitta, Kazuhiro R; Piette, Jacques; Reeves, Wendy; Scornavacca, Céline; Simion, Paul; Vincentelli, Renaud; Bellec, Maelle; Aicha, Sameh Ben; Fagotto, Marie; Guéroult-Bellone, Marion; Haeussler, Maximilian; Jacox, Edwin; Lowe, Elijah K; Mendez, Mickael; Roberge, Alexis; Stolfi, Alberto; Yokomori, Rui; Cambillau, Christian; Christiaen, Lionel; Delsuc, Frédéric; Douzery, Emmanuel; Dumollard, Rémi; Kusakabe, Takehiro; Nakai, Kenta; Nishida, Hiroki; Satou, Yutaka; Swalla, Billie; Veeman, Michael; Volff, Jean-Nicolas

2018-01-01

Abstract ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates. PMID:29149270

Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization.

PubMed

Zhao, Zhi-Qin; Han, Guo-Sheng; Yu, Zu-Guo; Li, Jinyan

2015-08-01

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request. Copyright © 2015 Elsevier Ltd. All rights reserved.

Saccharomyces genome database informs human biology

PubMed Central

Skrzypek, Marek S; Nash, Robert S; Wong, Edith D; MacPherson, Kevin A; Karra, Kalpana; Binkley, Gail; Simison, Matt; Miyasato, Stuart R

2018-01-01

Abstract The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD. PMID:29140510

The Plant Structure Ontology, a Unified Vocabulary of Anatomy and Morphology of a Flowering Plant1[W][OA

PubMed Central

Ilic, Katica; Kellogg, Elizabeth A.; Jaiswal, Pankaj; Zapata, Felipe; Stevens, Peter F.; Vincent, Leszek P.; Avraham, Shulamit; Reiser, Leonore; Pujar, Anuradha; Sachs, Martin M.; Whitman, Noah T.; McCouch, Susan R.; Schaeffer, Mary L.; Ware, Doreen H.; Stein, Lincoln D.; Rhee, Seung Y.

2007-01-01

Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological representation of anatomy and morphology of a flowering plant. The PSO is intended for a broad plant research community, including bench scientists, curators in genomic databases, and bioinformaticians. The initial releases of the PSO integrated existing ontologies for Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa); more recent versions of the ontology encompass terms relevant to Fabaceae, Solanaceae, additional cereal crops, and poplar (Populus spp.). Databases such as The Arabidopsis Information Resource, Nottingham Arabidopsis Stock Centre, Gramene, MaizeGDB, and SOL Genomics Network are using the PSO to describe expression patterns of genes and phenotypes of mutants and natural variants and are regularly contributing new annotations to the Plant Ontology database. The PSO is also used in specialized public databases, such as BRENDA, GENEVESTIGATOR, NASCArrays, and others. Over 10,000 gene annotations and phenotype descriptions from participating databases can be queried and retrieved using the Plant Ontology browser. The PSO, as well as contributed gene associations, can be obtained at www.plantontology.org. PMID:17142475

SSER: Species specific essential reactions database.

PubMed

Labena, Abraham A; Ye, Yuan-Nong; Dong, Chuan; Zhang, Fa-Z; Guo, Feng-Biao

2017-04-19

Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value. Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database. SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser .

Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles.

PubMed

Gadala-Maria, Daniel; Yaari, Gur; Uduman, Mohamed; Kleinstein, Steven H

2015-02-24

Individual variation in germline and expressed B-cell immunoglobulin (Ig) repertoires has been associated with aging, disease susceptibility, and differential response to infection and vaccination. Repertoire properties can now be studied at large-scale through next-generation sequencing of rearranged Ig genes. Accurate analysis of these repertoire-sequencing (Rep-Seq) data requires identifying the germline variable (V), diversity (D), and joining (J) gene segments used by each Ig sequence. Current V(D)J assignment methods work by aligning sequences to a database of known germline V(D)J segment alleles. However, existing databases are likely to be incomplete and novel polymorphisms are hard to differentiate from the frequent occurrence of somatic hypermutations in Ig sequences. Here we develop a Tool for Ig Genotype Elucidation via Rep-Seq (TIgGER). TIgGER analyzes mutation patterns in Rep-Seq data to identify novel V segment alleles, and also constructs a personalized germline database containing the specific set of alleles carried by a subject. This information is then used to improve the initial V segment assignments from existing tools, like IMGT/HighV-QUEST. The application of TIgGER to Rep-Seq data from seven subjects identified 11 novel V segment alleles, including at least one in every subject examined. These novel alleles constituted 13% of the total number of unique alleles in these subjects, and impacted 3% of V(D)J segment assignments. These results reinforce the highly polymorphic nature of human Ig V genes, and suggest that many novel alleles remain to be discovered. The integration of TIgGER into Rep-Seq processing pipelines will increase the accuracy of V segment assignments, thus improving B-cell repertoire analyses.

IMGT/GeneInfo: T cell receptor gamma TRG and delta TRD genes in database give access to all TR potential V(D)J recombinations

PubMed Central

Baum, Thierry-Pascal; Hierle, Vivien; Pasqual, Nicolas; Bellahcene, Fatena; Chaume, Denys; Lefranc, Marie-Paule; Jouvin-Marche, Evelyne; Marche, Patrice Noël; Demongeot, Jacques

2006-01-01

Background Adaptative immune repertoire diversity in vertebrate species is generated by recombination of variable (V), diversity (D) and joining (J) genes in the immunoglobulin (IG) loci of B lymphocytes and in the T cell receptor (TR) loci of T lymphocytes. These V-J and V-D-J gene rearrangements at the DNA level involve recombination signal sequences (RSS). Whereas many data exist, they are scattered in non specialized resources with different nomenclatures (eg. flat files) and are difficult to extract. Description IMGT/GeneInfo is an online information system that provides, through a user-friendly interface, exhaustive information resulting from the complex mechanisms of T cell receptor V-J and V-D-J recombinations. T cells comprise two populations which express the αβ and γδ TR, respectively. The first version of the system dealt with the Homo sapiens and Mus musculus TRA and TRB loci whose gene rearrangements allow the synthesis of the αβ TR chains. In this paper, we present the second version of IMGT/GeneInfo where we complete the database for the Homo sapiens and Mus musculus TRG and TRD loci along with the introduction of a quality control procedure for existing and new data. We also include new functionalities to the four loci analysis, giving, to date, a very informative tool which allows to work on V(D)J genes of all TR loci in both human and mouse species. IMGT/GeneInfo provides more than 59,000 rearrangement combinations with a full gene description which is freely available at . Conclusion IMGT/GeneInfo allows all TR information sequences to be in the same spot, and are now available within two computer-mouse clicks. This is useful for biologists and bioinformaticians for the study of T lymphocyte V(D)J gene rearrangements and their applications in immune response analysis. PMID:16640788

Automated Identification of Medically Important Bacteria by 16S rRNA Gene Sequencing Using a Novel Comprehensive Database, 16SpathDB▿

PubMed Central

Woo, Patrick C. Y.; Teng, Jade L. L.; Yeung, Juilian M. Y.; Tse, Herman; Lau, Susanna K. P.; Yuen, Kwok-Yung

2011-01-01

Despite the increasing use of 16S rRNA gene sequencing, interpretation of 16S rRNA gene sequence results is one of the most difficult problems faced by clinical microbiologists and technicians. To overcome the problems we encountered in the existing databases during 16S rRNA gene sequence interpretation, we built a comprehensive database, 16SpathDB (http://147.8.74.24/16SpathDB) based on the 16S rRNA gene sequences of all medically important bacteria listed in the Manual of Clinical Microbiology and evaluated its use for automated identification of these bacteria. Among 91 nonduplicated bacterial isolates collected in our clinical microbiology laboratory, 71 (78%) were reported by 16SpathDB as a single bacterial species having >98.0% nucleotide identity with the query sequence, 19 (20.9%) were reported as more than one bacterial species having >98.0% nucleotide identity with the query sequence, and 1 (1.1%) was reported as no match. For the 71 bacterial isolates reported as a single bacterial species, all results were identical to their true identities as determined by a polyphasic approach. For the 19 bacterial isolates reported as more than one bacterial species, all results contained their true identities as determined by a polyphasic approach and all of them had their true identities as the “best match in 16SpathDB.” For the isolate (Gordonibacter pamelaeae) reported as no match, the bacterium has never been reported to be associated with human disease and was not included in the Manual of Clinical Microbiology. 16SpathDB is an automated, user-friendly, efficient, accurate, and regularly updated database for 16S rRNA gene sequence interpretation in clinical microbiology laboratories. PMID:21389154

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/ .

ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets.

PubMed

Brozovic, Matija; Dantec, Christelle; Dardaillon, Justine; Dauga, Delphine; Faure, Emmanuel; Gineste, Mathieu; Louis, Alexandra; Naville, Magali; Nitta, Kazuhiro R; Piette, Jacques; Reeves, Wendy; Scornavacca, Céline; Simion, Paul; Vincentelli, Renaud; Bellec, Maelle; Aicha, Sameh Ben; Fagotto, Marie; Guéroult-Bellone, Marion; Haeussler, Maximilian; Jacox, Edwin; Lowe, Elijah K; Mendez, Mickael; Roberge, Alexis; Stolfi, Alberto; Yokomori, Rui; Brown, C Titus; Cambillau, Christian; Christiaen, Lionel; Delsuc, Frédéric; Douzery, Emmanuel; Dumollard, Rémi; Kusakabe, Takehiro; Nakai, Kenta; Nishida, Hiroki; Satou, Yutaka; Swalla, Billie; Veeman, Michael; Volff, Jean-Nicolas; Lemaire, Patrick

2018-01-04

ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II.

PubMed

Lu, Zhiyong; Hirschman, Lynette

2012-01-01

Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators. DATABASE URL: http://www.biocreative.org/tasks/bc-workshop-2012/workflow/.

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

PubMed Central

Green, Jason M.; Harnsomburana, Jaturon; Schaeffer, Mary L.; Lawrence, Carolyn J.; Shyu, Chi-Ren

2011-01-01

Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases. Database URL: http:www.PhenomicsWorld.org/QBTA.php PMID:21558151

Genetic analysis of the cytoplasmic dynein subunit families.

PubMed

Pfister, K Kevin; Shah, Paresh R; Hummerich, Holger; Russ, Andreas; Cotton, James; Annuar, Azlina Ahmad; King, Stephen M; Fisher, Elizabeth M C

2006-01-01

Cytoplasmic dyneins, the principal microtubule minus-end-directed motor proteins of the cell, are involved in many essential cellular processes. The major form of this enzyme is a complex of at least six protein subunits, and in mammals all but one of the subunits are encoded by at least two genes. Here we review current knowledge concerning the subunits, their interactions, and their functional roles as derived from biochemical and genetic analyses. We also carried out extensive database searches to look for new genes and to clarify anomalies in the databases. Our analysis documents evolutionary relationships among the dynein subunits of mammals and other model organisms, and sheds new light on the role of this diverse group of proteins, highlighting the existence of two cytoplasmic dynein complexes with distinct cellular roles.

Genetic Analysis of the Cytoplasmic Dynein Subunit Families

PubMed Central

Pfister, K. Kevin; Shah, Paresh R; Hummerich, Holger; Russ, Andreas; Cotton, James; Annuar, Azlina Ahmad; King, Stephen M; Fisher, Elizabeth M. C

2006-01-01

Cytoplasmic dyneins, the principal microtubule minus-end-directed motor proteins of the cell, are involved in many essential cellular processes. The major form of this enzyme is a complex of at least six protein subunits, and in mammals all but one of the subunits are encoded by at least two genes. Here we review current knowledge concerning the subunits, their interactions, and their functional roles as derived from biochemical and genetic analyses. We also carried out extensive database searches to look for new genes and to clarify anomalies in the databases. Our analysis documents evolutionary relationships among the dynein subunits of mammals and other model organisms, and sheds new light on the role of this diverse group of proteins, highlighting the existence of two cytoplasmic dynein complexes with distinct cellular roles. PMID:16440056

Saccharomyces genome database informs human biology.

PubMed

Skrzypek, Marek S; Nash, Robert S; Wong, Edith D; MacPherson, Kevin A; Hellerstedt, Sage T; Engel, Stacia R; Karra, Kalpana; Weng, Shuai; Sheppard, Travis K; Binkley, Gail; Simison, Matt; Miyasato, Stuart R; Cherry, J Michael

2018-01-04

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

Computational Identification of Novel Genes: Current and Future Perspectives.

PubMed

Klasberg, Steffen; Bitard-Feildel, Tristan; Mallet, Ludovic

2016-01-01

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

Pyviko: an automated Python tool to design gene knockouts in complex viruses with overlapping genes.

PubMed

Taylor, Louis J; Strebel, Klaus

2017-01-07

Gene knockouts are a common tool used to study gene function in various organisms. However, designing gene knockouts is complicated in viruses, which frequently contain sequences that code for multiple overlapping genes. Designing mutants that can be traced by the creation of new or elimination of existing restriction sites further compounds the difficulty in experimental design of knockouts of overlapping genes. While software is available to rapidly identify restriction sites in a given nucleotide sequence, no existing software addresses experimental design of mutations involving multiple overlapping amino acid sequences in generating gene knockouts. Pyviko performed well on a test set of over 240,000 gene pairs collected from viral genomes deposited in the National Center for Biotechnology Information Nucleotide database, identifying a point mutation which added a premature stop codon within the first 20 codons of the target gene in 93.2% of all tested gene-overprinted gene pairs. This shows that Pyviko can be used successfully in a wide variety of contexts to facilitate the molecular cloning and study of viral overprinted genes. Pyviko is an extensible and intuitive Python tool for designing knockouts of overlapping genes. Freely available as both a Python package and a web-based interface ( http://louiejtaylor.github.io/pyViKO/ ), Pyviko simplifies the experimental design of gene knockouts in complex viruses with overlapping genes.

Epigenetic hereditary transcription profiles III, evidence for an epigenetic network resulting in gender, tissue and age-specific variation in overall transcription

PubMed Central

Simons, Johannes WIM

2009-01-01

Background We have previously shown that deviations from the average transcription profile of a group of functionally related genes are not only heritable, but also demonstrate specific patterns associated with age, gender and differentiation, thereby implicating genome-wide nuclear programming as the cause. To determine whether these results could be reproduced, a different micro-array database (obtained from two types of muscle tissue, derived from 81 human donors aged between 16 to 89 years) was studied. Results This new database also revealed the existence of age, gender and tissue-specific features in a small group of functionally related genes. In order to further analyze this phenomenon, a method was developed for quantifying the contribution of different factors to the variability in gene expression, and for generating a database limited to residual values reflecting constitutional differences between individuals. These constitutional differences, presumably epigenetic in origin, contribute to about 50% of the observed residual variance which is connected with a network of interrelated changes in gene expression with some genes displaying a decrease or increase in residual variation with age. Conclusion Epigenetic variation in gene expression without a clear concomitant relation to gene function appears to be a widespread phenomenon. This variation is connected with interactions between genes, is gender and tissue specific and is related to cellular aging. This finding, together with the method developed for analysis, might contribute to the elucidation of the role of nuclear programming in differentiation, aging and carcinogenesis Reviewers This article was reviewed by Thiago M. Venancio (nominated by Aravind Iyer), Hua Li (nominated by Arcady Mushegian) and Arcady Mushegian and J.P.de Magelhaes (nominated by G. Church). PMID:19796384

In silico analysis of 16S ribosomal RNA gene sequencing‐based methods for identification of medically important anaerobic bacteria

PubMed Central

Woo, Patrick C Y; Chung, Liliane M W; Teng, Jade L L; Tse, Herman; Pang, Sherby S Y; Lau, Veronica Y T; Wong, Vanessa W K; Kam, Kwok‐ling; Lau, Susanna K P; Yuen, Kwok‐Yung

2007-01-01

This study is the first study that provides useful guidelines to clinical microbiologists and technicians on the usefulness of full 16S rRNA sequencing, 5′‐end 527‐bp 16S rRNA sequencing and the existing MicroSeq full and 500 16S rDNA bacterial identification system (MicroSeq, Perkin‐Elmer Applied Biosystems Division, Foster City, California, USA) databases for the identification of all existing medically important anaerobic bacteria. Full and 527‐bp 16S rRNA sequencing are able to identify 52–63% of 130 Gram‐positive anaerobic rods, 72–73% of 86 Gram‐negative anaerobic rods and 78% of 23 anaerobic cocci. The existing MicroSeq databases are able to identify only 19–25% of 130 Gram‐positive anaerobic rods, 38% of 86 Gram‐negative anaerobic rods and 39% of 23 anaerobic cocci. These represent only 45–46% of those that should be confidently identified by full and 527‐bp 16S rRNA sequencing. To improve the usefulness of MicroSeq, bacterial species that should be confidently identified by full and/or 527‐bp 16S rRNA sequencing but not included in the existing MicroSeq databases should be included. PMID:17046845

De novo sequencing and analysis of the transcriptome during the browning of fresh-cut Luffa cylindrica 'Fusi-3' fruits.

PubMed

Zhu, Haisheng; Liu, Jianting; Wen, Qingfang; Chen, Mindong; Wang, Bin; Zhang, Qianrong; Xue, Zhuzheng

2017-01-01

Fresh-cut luffa (Luffa cylindrica) fruits commonly undergo browning. However, little is known about the molecular mechanisms regulating this process. We used the RNA-seq technique to analyze the transcriptomic changes occurring during the browning of fresh-cut fruits from luffa cultivar 'Fusi-3'. Over 90 million high-quality reads were assembled into 58,073 Unigenes, and 60.86% of these were annotated based on sequences in four public databases. We detected 35,282 Unigenes with significant hits to sequences in the NCBInr database, and 24,427 Unigenes encoded proteins with sequences that were similar to those of known proteins in the Swiss-Prot database. Additionally, 20,546 and 13,021 Unigenes were similar to existing sequences in the Eukaryotic Orthologous Groups of proteins and Kyoto Encyclopedia of Genes and Genomes databases, respectively. Furthermore, 27,301 Unigenes were differentially expressed during the browning of fresh-cut luffa fruits (i.e., after 1-6 h). Moreover, 11 genes from five gene families (i.e., PPO, PAL, POD, CAT, and SOD) identified as potentially associated with enzymatic browning as well as four WRKY transcription factors were observed to be differentially regulated in fresh-cut luffa fruits. With the assistance of rapid amplification of cDNA ends technology, we obtained the full-length sequences of the 15 Unigenes. We also confirmed these Unigenes were expressed by quantitative real-time polymerase chain reaction analysis. This study provides a comprehensive transcriptome sequence resource, and may facilitate further studies aimed at identifying genes affecting luffa fruit browning for the exploitation of the underlying mechanism.

PCV: An Alignment Free Method for Finding Homologous Nucleotide Sequences and its Application in Phylogenetic Study.

PubMed

Kumar, Rajnish; Mishra, Bharat Kumar; Lahiri, Tapobrata; Kumar, Gautam; Kumar, Nilesh; Gupta, Rahul; Pal, Manoj Kumar

2017-06-01

Online retrieval of the homologous nucleotide sequences through existing alignment techniques is a common practice against the given database of sequences. The salient point of these techniques is their dependence on local alignment techniques and scoring matrices the reliability of which is limited by computational complexity and accuracy. Toward this direction, this work offers a novel way for numerical representation of genes which can further help in dividing the data space into smaller partitions helping formation of a search tree. In this context, this paper introduces a 36-dimensional Periodicity Count Value (PCV) which is representative of a particular nucleotide sequence and created through adaptation from the concept of stochastic model of Kolekar et al. (American Institute of Physics 1298:307-312, 2010. doi: 10.1063/1.3516320 ). The PCV construct uses information on physicochemical properties of nucleotides and their positional distribution pattern within a gene. It is observed that PCV representation of gene reduces computational cost in the calculation of distances between a pair of genes while being consistent with the existing methods. The validity of PCV-based method was further tested through their use in molecular phylogeny constructs in comparison with that using existing sequence alignment methods.

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

PubMed Central

Gioutlakis, Aris; Klapa, Maria I.

2017-01-01

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes. PMID:29023571

BIG: a large-scale data integration tool for renal physiology.

PubMed

Zhao, Yue; Yang, Chin-Rang; Raghuram, Viswanathan; Parulekar, Jaya; Knepper, Mark A

2016-10-01

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

ISAAC - InterSpecies Analysing Application using Containers.

PubMed

Baier, Herbert; Schultz, Jörg

2014-01-15

Information about genes, transcripts and proteins is spread over a wide variety of databases. Different tools have been developed using these databases to identify biological signals in gene lists from large scale analysis. Mostly, they search for enrichments of specific features. But, these tools do not allow an explorative walk through different views and to change the gene lists according to newly upcoming stories. To fill this niche, we have developed ISAAC, the InterSpecies Analysing Application using Containers. The central idea of this web based tool is to enable the analysis of sets of genes, transcripts and proteins under different biological viewpoints and to interactively modify these sets at any point of the analysis. Detailed history and snapshot information allows tracing each action. Furthermore, one can easily switch back to previous states and perform new analyses. Currently, sets can be viewed in the context of genomes, protein functions, protein interactions, pathways, regulation, diseases and drugs. Additionally, users can switch between species with an automatic, orthology based translation of existing gene sets. As todays research usually is performed in larger teams and consortia, ISAAC provides group based functionalities. Here, sets as well as results of analyses can be exchanged between members of groups. ISAAC fills the gap between primary databases and tools for the analysis of large gene lists. With its highly modular, JavaEE based design, the implementation of new modules is straight forward. Furthermore, ISAAC comes with an extensive web-based administration interface including tools for the integration of third party data. Thus, a local installation is easily feasible. In summary, ISAAC is tailor made for highly explorative interactive analyses of gene, transcript and protein sets in a collaborative environment.

Mutation databases for inherited renal disease: are they complete, accurate, clinically relevant, and freely available?

PubMed

Savige, Judy; Dagher, Hayat; Povey, Sue

2014-07-01

This study examined whether gene-specific DNA variant databases for inherited diseases of the kidney fulfilled the Human Variome Project recommendations of being complete, accurate, clinically relevant and freely available. A recent review identified 60 inherited renal diseases caused by mutations in 132 genes. The disease name, MIM number, gene name, together with "mutation" or "database," were used to identify web-based databases. Fifty-nine diseases (98%) due to mutations in 128 genes had a variant database. Altogether there were 349 databases (a median of 3 per gene, range 0-6), but no gene had two databases with the same number of variants, and 165 (50%) databases included fewer than 10 variants. About half the databases (180, 54%) had been updated in the previous year. Few (77, 23%) were curated by "experts" but these included nine of the 11 with the most variants. Even fewer databases (41, 12%) included clinical features apart from the name of the associated disease. Most (223, 67%) could be accessed without charge, including those for 50 genes (40%) with the maximum number of variants. Future efforts should focus on encouraging experts to collaborate on a single database for each gene affected in inherited renal disease, including both unpublished variants, and clinical phenotypes. © 2014 WILEY PERIODICALS, INC.

Identification of learning and memory genes in canine; promoter investigation and determining the selective pressure.

PubMed

Seifi Moroudi, Reihane; Masoudi, Ali Akbar; Vaez Torshizi, Rasoul; Zandi, Mohammad

2014-12-01

One of the important behaviors of dogs is trainability which is affected by learning and memory genes. These kinds of the genes have not yet been identified in dogs. In the current research, these genes were found in animal models by mining the biological data and scientific literatures. The proteins of these genes were obtained from the UniProt database in dogs and humans. Not all homologous proteins perform similar functions, thus comparison of these proteins was studied in terms of protein families, domains, biological processes, molecular functions, and cellular location of metabolic pathways in Interpro, KEGG, Quick Go and Psort databases. The results showed that some of these proteins have the same performance in the rat or mouse, dog, and human. It is anticipated that the protein of these genes may be effective in learning and memory in dogs. Then, the expression pattern of the recognized genes was investigated in the dog hippocampus using the existing information in the GEO profile. The results showed that BDNF, TAC1 and CCK genes are expressed in the dog hippocampus, therefore, these genes could be strong candidates associated with learning and memory in dogs. Subsequently, due to the importance of the promoter regions in gene function, this region was investigated in the above genes. Analysis of the promoter indicated that the HNF-4 site of BDNF gene and the transcription start site of CCK gene is exposed to methylation. Phylogenetic analysis of protein sequences of these genes showed high similarity in each of these three genes among the studied species. The dN/dS ratio for BDNF, TAC1 and CCK genes indicates a purifying selection during the evolution of the genes.

Using diverse U.S. beef cattle genomes to identify missense mutations in EPAS1, a gene associated with pulmonary hypertension

USDA-ARS?s Scientific Manuscript database

The availability of whole genome sequence (WGS) data has made it possible to discover protein variants in silico. However, existing bovine WGS databases do not show data in a form conducive to protein variant analysis, and tend to under represent the breadth of genetic diversity in U.S. beef cattle...

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

Zhu, Yitan; Xu, Yanxun; Helseth, Donald L.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood modelmore » derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.« less

Discovery of novel cold-induced CISP genes encoding small RNA-binding proteins related to cold adaptation in barley.

PubMed

Ying, Mengchao; Kidou, Shin-Ichiro

2017-07-01

To adapt to cold conditions, barley plants rely on specific mechanisms, which have not been fully understood. In this study, we characterized a novel barley cold-induced gene identified using a PCR-based high coverage gene expression profiling method. The identified gene encodes a small protein that we named CISP1 (Cold-induced Small Protein 1). Homology searches of sequence databases revealed that CISP1 homologs (CISP2 and CISP3) exist in barley genome. Further database analyses showed that the CISP1 homologs were widely distributed in cold-tolerant plants such as wheat and rye. Quantitative reverse transcription PCR analyses indicated that the expression of barley CISP genes was markedly increased in roots exposed to cold conditions. In situ hybridization analyses showed that the CISP1 transcripts were localized in the root tip and lateral root primordium. We also demonstrated that the CISP1 protein bound to RNA. Taken together, these findings indicate that CISP1 and its homologs encoding small RNA-binding proteins may serve as RNA chaperones playing a vital role in the cold adaptation of barley root. This is the first report describing the likely close relationship between root-specific genes and the cold adaptation process, as well as the potential function of the identified genes. Copyright © 2017 Elsevier B.V. All rights reserved.

LCGbase: A Comprehensive Database for Lineage-Based Co-regulated Genes.

PubMed

Wang, Dapeng; Zhang, Yubin; Fan, Zhonghua; Liu, Guiming; Yu, Jun

2012-01-01

Animal genes of different lineages, such as vertebrates and arthropods, are well-organized and blended into dynamic chromosomal structures that represent a primary regulatory mechanism for body development and cellular differentiation. The majority of genes in a genome are actually clustered, which are evolutionarily stable to different extents and biologically meaningful when evaluated among genomes within and across lineages. Until now, many questions concerning gene organization, such as what is the minimal number of genes in a cluster and what is the driving force leading to gene co-regulation, remain to be addressed. Here, we provide a user-friendly database-LCGbase (a comprehensive database for lineage-based co-regulated genes)-hosting information on evolutionary dynamics of gene clustering and ordering within animal kingdoms in two different lineages: vertebrates and arthropods. The database is constructed on a web-based Linux-Apache-MySQL-PHP framework and effective interactive user-inquiry service. Compared to other gene annotation databases with similar purposes, our database has three comprehensible advantages. First, our database is inclusive, including all high-quality genome assemblies of vertebrates and representative arthropod species. Second, it is human-centric since we map all gene clusters from other genomes in an order of lineage-ranks (such as primates, mammals, warm-blooded, and reptiles) onto human genome and start the database from well-defined gene pairs (a minimal cluster where the two adjacent genes are oriented as co-directional, convergent, and divergent pairs) to large gene clusters. Furthermore, users can search for any adjacent genes and their detailed annotations. Third, the database provides flexible parameter definitions, such as the distance of transcription start sites between two adjacent genes, which is extendable to genes that flanking the cluster across species. We also provide useful tools for sequence alignment, gene ontology (GO) annotation, promoter identification, gene expression (co-expression), and evolutionary analysis. This database not only provides a way to define lineage-specific and species-specific gene clusters but also facilitates future studies on gene co-regulation, epigenetic control of gene expression (DNA methylation and histone marks), and chromosomal structures in a context of gene clusters and species evolution. LCGbase is freely available at http://lcgbase.big.ac.cn/LCGbase.

Compositional Gene Landscapes in Vertebrates

PubMed Central

Cruveiller, Stéphane; Jabbari, Kamel; Clay, Oliver; Bernardi, Giorgio

2004-01-01

The existence of a well conserved linear relationship between GC levels of genes' second and third codon positions (GC2, GC3) prompted us to focus on the landscape, or joint distribution, spanned by these two variables. In human, well curated coding sequences now cover at least 15%–30% of the estimated total gene set. Our analysis of the landscape defined by this gene set revealed not only the well documented linear crest, but also the presence of several peaks and valleys along that crest, a property that was also indicated in two other warm-blooded vertebrates represented by large gene databases, that is, mouse and chicken. GC2 is the sum of eight amino acid frequencies, whereas GC3 is linearly related to the GC level of the chromosomal region containing the gene. The landscapes therefore portray relations between proteins and the DNA environments of the genes that encode them. PMID:15123586

Compositional gene landscapes in vertebrates.

PubMed

Cruveiller, Stéphane; Jabbari, Kamel; Clay, Oliver; Bernardi, Giorgio

2004-05-01

The existence of a well conserved linear relationship between GC levels of genes' second and third codon positions (GC2, GC3) prompted us to focus on the landscape, or joint distribution, spanned by these two variables. In human, well curated coding sequences now cover at least 15%-30% of the estimated total gene set. Our analysis of the landscape defined by this gene set revealed not only the well documented linear crest, but also the presence of several peaks and valleys along that crest, a property that was also indicated in two other warm-blooded vertebrates represented by large gene databases, that is, mouse and chicken. GC2 is the sum of eight amino acid frequencies, whereas GC3 is linearly related to the GC level of the chromosomal region containing the gene. The landscapes therefore portray relations between proteins and the DNA environments of the genes that encode them.

Gene Unprediction with Spurio: A tool to identify spurious protein sequences.

PubMed

Höps, Wolfram; Jeffryes, Matt; Bateman, Alex

2018-01-01

We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation.  Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases.  We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes.  Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.

SSTAR, a Stand-Alone Easy-To-Use Antimicrobial Resistance Gene Predictor.

PubMed

de Man, Tom J B; Limbago, Brandi M

2016-01-01

We present the easy-to-use Sequence Search Tool for Antimicrobial Resistance, SSTAR. It combines a locally executed BLASTN search against a customizable database with an intuitive graphical user interface for identifying antimicrobial resistance (AR) genes from genomic data. Although the database is initially populated from a public repository of acquired resistance determinants (i.e., ARG-ANNOT), it can be customized for particular pathogen groups and resistance mechanisms. For instance, outer membrane porin sequences associated with carbapenem resistance phenotypes can be added, and known intrinsic mechanisms can be included. Unique about this tool is the ability to easily detect putative new alleles and truncated versions of existing AR genes. Variants and potential new alleles are brought to the attention of the user for further investigation. For instance, SSTAR is able to identify modified or truncated versions of porins, which may be of great importance in carbapenemase-negative carbapenem-resistant Enterobacteriaceae. SSTAR is written in Java and is therefore platform independent and compatible with both Windows and Unix operating systems. SSTAR and its manual, which includes a simple installation guide, are freely available from https://github.com/tomdeman-bio/Sequence-Search-Tool-for-Antimicrobial-Resistance-SSTAR-. IMPORTANCE Whole-genome sequencing (WGS) is quickly becoming a routine method for identifying genes associated with antimicrobial resistance (AR). However, for many microbiologists, the use and analysis of WGS data present a substantial challenge. We developed SSTAR, software with a graphical user interface that enables the identification of known AR genes from WGS and has the unique capacity to easily detect new variants of known AR genes, including truncated protein variants. Current software solutions do not notify the user when genes are truncated and, therefore, likely nonfunctional, which makes phenotype predictions less accurate. SSTAR users can apply any AR database of interest as a reference comparator and can manually add genes that impact resistance, even if such genes are not resistance determinants per se (e.g., porins and efflux pumps).

[Establishment of a comprehensive database for laryngeal cancer related genes and the miRNAs].

PubMed

Li, Mengjiao; E, Qimin; Liu, Jialin; Huang, Tingting; Liang, Chuanyu

2015-09-01

By collecting and analyzing the laryngeal cancer related genes and the miRNAs, to build a comprehensive laryngeal cancer-related gene database, which differs from the current biological information database with complex and clumsy structure and focuses on the theme of gene and miRNA, and it could make the research and teaching more convenient and efficient. Based on the B/S architecture, using Apache as a Web server, MySQL as coding language of database design and PHP as coding language of web design, a comprehensive database for laryngeal cancer-related genes was established, providing with the gene tables, protein tables, miRNA tables and clinical information tables of the patients with laryngeal cancer. The established database containsed 207 laryngeal cancer related genes, 243 proteins, 26 miRNAs, and their particular information such as mutations, methylations, diversified expressions, and the empirical references of laryngeal cancer relevant molecules. The database could be accessed and operated via the Internet, by which browsing and retrieval of the information were performed. The database were maintained and updated regularly. The database for laryngeal cancer related genes is resource-integrated and user-friendly, providing a genetic information query tool for the study of laryngeal cancer.

A New Omics Data Resource of Pleurocybella porrigens for Gene Discovery

PubMed Central

Dohra, Hideo; Someya, Takumi; Takano, Tomoyuki; Harada, Kiyonori; Omae, Saori; Hirai, Hirofumi; Yano, Kentaro; Kawagishi, Hirokazu

2013-01-01

Background Pleurocybella porrigens is a mushroom-forming fungus, which has been consumed as a traditional food in Japan. In 2004, 55 people were poisoned by eating the mushroom and 17 people among them died of acute encephalopathy. Since then, the Japanese government has been alerting Japanese people to take precautions against eating the P . porrigens mushroom. Unfortunately, despite efforts, the molecular mechanism of the encephalopathy remains elusive. The genome and transcriptome sequence data of P . porrigens and the related species, however, are not stored in the public database. To gain the omics data in P . porrigens , we sequenced genome and transcriptome of its fruiting bodies and mycelia by next generation sequencing. Methodology/Principal Findings Short read sequences of genomic DNAs and mRNAs in P . porrigens were generated by Illumina Genome Analyzer. Genome short reads were de novo assembled into scaffolds using Velvet. Comparisons of genome signatures among Agaricales showed that P . porrigens has a unique genome signature. Transcriptome sequences were assembled into contigs (unigenes). Biological functions of unigenes were predicted by Gene Ontology and KEGG pathway analyses. The majority of unigenes would be novel genes without significant counterparts in the public omics databases. Conclusions Functional analyses of unigenes present the existence of numerous novel genes in the basidiomycetes division. The results mean that the omics information such as genome, transcriptome and metabolome in basidiomycetes is short in the current databases. The large-scale omics information on P . porrigens , provided from this research, will give a new data resource for gene discovery in basidiomycetes. PMID:23936076

Cloning, analysis and functional annotation of expressed sequence tags from the Earthworm Eisenia fetida

PubMed Central

Pirooznia, Mehdi; Gong, Ping; Guan, Xin; Inouye, Laura S; Yang, Kuan; Perkins, Edward J; Deng, Youping

2007-01-01

Background Eisenia fetida, commonly known as red wiggler or compost worm, belongs to the Lumbricidae family of the Annelida phylum. Little is known about its genome sequence although it has been extensively used as a test organism in terrestrial ecotoxicology. In order to understand its gene expression response to environmental contaminants, we cloned 4032 cDNAs or expressed sequence tags (ESTs) from two E. fetida libraries enriched with genes responsive to ten ordnance related compounds using suppressive subtractive hybridization-PCR. Results A total of 3144 good quality ESTs (GenBank dbEST accession number EH669363–EH672369 and EL515444–EL515580) were obtained from the raw clone sequences after cleaning. Clustering analysis yielded 2231 unique sequences including 448 contigs (from 1361 ESTs) and 1783 singletons. Comparative genomic analysis showed that 743 or 33% of the unique sequences shared high similarity with existing genes in the GenBank nr database. Provisional function annotation assigned 830 Gene Ontology terms to 517 unique sequences based on their homology with the annotated genomes of four model organisms Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, and Caenorhabditis elegans. Seven percent of the unique sequences were further mapped to 99 Kyoto Encyclopedia of Genes and Genomes pathways based on their matching Enzyme Commission numbers. All the information is stored and retrievable at a highly performed, web-based and user-friendly relational database called EST model database or ESTMD version 2. Conclusion The ESTMD containing the sequence and annotation information of 4032 E. fetida ESTs is publicly accessible at . PMID:18047730

Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys

PubMed Central

Werner, Jeffrey J; Koren, Omry; Hugenholtz, Philip; DeSantis, Todd Z; Walters, William A; Caporaso, J Gregory; Angenent, Largus T; Knight, Rob; Ley, Ruth E

2012-01-01

Taxonomic classification of the thousands–millions of 16S rRNA gene sequences generated in microbiome studies is often achieved using a naïve Bayesian classifier (for example, the Ribosomal Database Project II (RDP) classifier), due to favorable trade-offs among automation, speed and accuracy. The resulting classification depends on the reference sequences and taxonomic hierarchy used to train the model; although the influence of primer sets and classification algorithms have been explored in detail, the influence of training set has not been characterized. We compared classification results obtained using three different publicly available databases as training sets, applied to five different bacterial 16S rRNA gene pyrosequencing data sets generated (from human body, mouse gut, python gut, soil and anaerobic digester samples). We observed numerous advantages to using the largest, most diverse training set available, that we constructed from the Greengenes (GG) bacterial/archaeal 16S rRNA gene sequence database and the latest GG taxonomy. Phylogenetic clusters of previously unclassified experimental sequences were identified with notable improvements (for example, 50% reduction in reads unclassified at the phylum level in mouse gut, soil and anaerobic digester samples), especially for phylotypes belonging to specific phyla (Tenericutes, Chloroflexi, Synergistetes and Candidate phyla TM6, TM7). Trimming the reference sequences to the primer region resulted in systematic improvements in classification depth, and greatest gains at higher confidence thresholds. Phylotypes unclassified at the genus level represented a greater proportion of the total community variation than classified operational taxonomic units in mouse gut and anaerobic digester samples, underscoring the need for greater diversity in existing reference databases. PMID:21716311

De novo sequencing and analysis of the transcriptome during the browning of fresh-cut Luffa cylindrica 'Fusi-3' fruits

PubMed Central

Chen, Mindong; Wang, Bin; Zhang, Qianrong; Xue, Zhuzheng

2017-01-01

Fresh-cut luffa (Luffa cylindrica) fruits commonly undergo browning. However, little is known about the molecular mechanisms regulating this process. We used the RNA-seq technique to analyze the transcriptomic changes occurring during the browning of fresh-cut fruits from luffa cultivar ‘Fusi-3’. Over 90 million high-quality reads were assembled into 58,073 Unigenes, and 60.86% of these were annotated based on sequences in four public databases. We detected 35,282 Unigenes with significant hits to sequences in the NCBInr database, and 24,427 Unigenes encoded proteins with sequences that were similar to those of known proteins in the Swiss-Prot database. Additionally, 20,546 and 13,021 Unigenes were similar to existing sequences in the Eukaryotic Orthologous Groups of proteins and Kyoto Encyclopedia of Genes and Genomes databases, respectively. Furthermore, 27,301 Unigenes were differentially expressed during the browning of fresh-cut luffa fruits (i.e., after 1–6 h). Moreover, 11 genes from five gene families (i.e., PPO, PAL, POD, CAT, and SOD) identified as potentially associated with enzymatic browning as well as four WRKY transcription factors were observed to be differentially regulated in fresh-cut luffa fruits. With the assistance of rapid amplification of cDNA ends technology, we obtained the full-length sequences of the 15 Unigenes. We also confirmed these Unigenes were expressed by quantitative real-time polymerase chain reaction analysis. This study provides a comprehensive transcriptome sequence resource, and may facilitate further studies aimed at identifying genes affecting luffa fruit browning for the exploitation of the underlying mechanism. PMID:29145430

BIG: a large-scale data integration tool for renal physiology

PubMed Central

Zhao, Yue; Yang, Chin-Rang; Raghuram, Viswanathan; Parulekar, Jaya

2016-01-01

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: “How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?” This is the type of problem that has motivated the “Big-Data” revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/. PMID:27279488

The ABCs of membrane transporters in health and disease (SLC series): Introduction☆☆☆

PubMed Central

Hediger, Matthias A.; Clémençon, Benjamin; Burrier, Robert E.; Bruford, Elspeth A.

2013-01-01

The field of transport biology has steadily grown over the past decade and is now recognized as playing an important role in manifestation and treatment of disease. The SLC (solute carrier) gene series has grown to now include 52 families and 395 transporter genes in the human genome. A list of these genes can be found at the HUGO Gene Nomenclature Committee (HGNC) website (see www.genenames.org/genefamilies/SLC). This special issue features mini-reviews for each of these SLC families written by the experts in each field. The existing online resource for solute carriers, the Bioparadigms SLC Tables (www.bioparadigms.org), has been updated and significantly extended with additional information and cross-links to other relevant databases, and the nomenclature used in this database has been validated and approved by the HGNC. In addition, the Bioparadigms SLC Tables functionality has been improved to allow easier access by the scientific community. This introduction includes: an overview of all known SLC and “non-SLC” transporter genes; a list of transporters of water soluble vitamins; a summary of recent progress in the structure determination of transporters (including GLUT1/SLC2A1); roles of transporters in human diseases and roles in drug approval and pharmaceutical perspectives. PMID:23506860

Metagenomic analysis revealed highly diverse microbial arsenic metabolism genes in paddy soils with low-arsenic contents.

PubMed

Xiao, Ke-Qing; Li, Li-Guan; Ma, Li-Ping; Zhang, Si-Yu; Bao, Peng; Zhang, Tong; Zhu, Yong-Guan

2016-04-01

Microbe-mediated arsenic (As) metabolism plays a critical role in global As cycle, and As metabolism involves different types of genes encoding proteins facilitating its biotransformation and transportation processes. Here, we used metagenomic analysis based on high-throughput sequencing and constructed As metabolism protein databases to analyze As metabolism genes in five paddy soils with low-As contents. The results showed that highly diverse As metabolism genes were present in these paddy soils, with varied abundances and distribution for different types and subtypes of these genes. Arsenate reduction genes (ars) dominated in all soil samples, and significant correlation existed between the abundance of arr (arsenate respiration), aio (arsenite oxidation), and arsM (arsenite methylation) genes, indicating the co-existence and close-relation of different As resistance systems of microbes in wetland environments similar to these paddy soils after long-term evolution. Among all soil parameters, pH was an important factor controlling the distribution of As metabolism gene in five paddy soils (p = 0.018). To the best of our knowledge, this is the first study using high-throughput sequencing and metagenomics approach in characterizing As metabolism genes in the five paddy soil, showing their great potential in As biotransformation, and therefore in mitigating arsenic risk to humans. Copyright © 2015 Elsevier Ltd. All rights reserved.

GENEASE: Real time bioinformatics tool for multi-omics and disease ontology exploration, analysis and visualization.

PubMed

Ghandikota, Sudhir; Hershey, Gurjit K Khurana; Mersha, Tesfaye B

2018-03-24

Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered. In this study, we present GENEASE, a web-based one-stop bioinformatics tool designed to not only query and explore multi-omics and phenotype databases (e.g., GTEx, ClinVar, dbGaP, GWAS Catalog, ENCODE, Roadmap Epigenomics, KEGG, Reactome, Gene and Phenotype Ontology) in a single web interface but also to perform seamless post genome-wide association downstream functional and overlap analysis for non-coding regulatory variants. GENEASE accesses over 50 different databases in public domain including model organism-specific databases to facilitate gene/variant and disease exploration, enrichment and overlap analysis in real time. It is a user-friendly tool with point-and-click interface containing links for support information including user manual and examples. GENEASE can be accessed freely at http://research.cchmc.org/mershalab/genease_new/login.html. Tesfaye.Mersha@cchmc.org, Sudhir.Ghandikota@cchmc.org. Supplementary data are available at Bioinformatics online.

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

Background One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments. PMID:23134636

Pathway Distiller - multisource biological pathway consolidation.

PubMed

Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

A novel gene: sawD related to the differentiation of streptomyces ansochromogenes.

PubMed

Gang, L; Wei, C; Yuqing, T; Huarong, T; Chater, K F; Buttner, M J

1999-01-01

A 1.3 kb DNA fragment was cloned from a total DNA library of Streptomyces ansochromogenes using Southern hybridization. Nucleotide sequencing analysis indicated that the 1320 bp DNA fragment contained a complete open reading frame (ORF). In search of databases, the deduced product of ORF containing 213 amino acids is homologous to the serine protease of Caulobacter cresceatus, and a conserved serine-catalytic active site (GPSAG) exists. The gene was designated as sawD. The function of this gene was studied with the strategy of gene disruption, and the result showed that the sawD may be related to sporulation and especially to the spore septation in Streptomyces ansochromogenes. The preliminary result indicated that sawD mutant could produce abundant pigment in contrast with the wild type, it seems that sawD gene may be involved in pigment biosynthesis, and this gene is also dispensable for biosynthesis of nikkomycin in Streptomyces ansochromogenes.

EuPathDB: the eukaryotic pathogen genomics database resource

PubMed Central

Aurrecoechea, Cristina; Barreto, Ana; Basenko, Evelina Y.; Brestelli, John; Brunk, Brian P.; Cade, Shon; Crouch, Kathryn; Doherty, Ryan; Falke, Dave; Fischer, Steve; Gajria, Bindu; Harb, Omar S.; Heiges, Mark; Hertz-Fowler, Christiane; Hu, Sufen; Iodice, John; Kissinger, Jessica C.; Lawrence, Cris; Li, Wei; Pinney, Deborah F.; Pulman, Jane A.; Roos, David S.; Shanmugasundram, Achchuthan; Silva-Franco, Fatima; Steinbiss, Sascha; Stoeckert, Christian J.; Spruill, Drew; Wang, Haiming; Warrenfeltz, Susanne; Zheng, Jie

2017-01-01

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions. PMID:27903906

Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

PubMed Central

Wei, Chih-Hsuan; Hakala, Kai; Pyysalo, Sampo; Ananiadou, Sophia; Kao, Hung-Yu; Lu, Zhiyong; Salakoski, Tapio; Van de Peer, Yves; Ginter, Filip

2013-01-01

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license. PMID:23613707

Defining the Human Macula Transcriptome and Candidate Retinal Disease Genes UsingEyeSAGE

PubMed Central

Rickman, Catherine Bowes; Ebright, Jessica N.; Zavodni, Zachary J.; Yu, Ling; Wang, Tianyuan; Daiger, Stephen P.; Wistow, Graeme; Boon, Kathy; Hauser, Michael A.

2009-01-01

Purpose To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). Methods Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. Results Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. Conclusions The EyeSAGE database, combining three different gene-profiling platforms including the authors’ multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions. PMID:16723438

Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE.

PubMed

Bowes Rickman, Catherine; Ebright, Jessica N; Zavodni, Zachary J; Yu, Ling; Wang, Tianyuan; Daiger, Stephen P; Wistow, Graeme; Boon, Kathy; Hauser, Michael A

2006-06-01

To develop large-scale, high-throughput annotation of the human macula transcriptome and to identify and prioritize candidate genes for inherited retinal dystrophies, based on ocular-expression profiles using serial analysis of gene expression (SAGE). Two human retina and two retinal pigment epithelium (RPE)/choroid SAGE libraries made from matched macula or midperipheral retina and adjacent RPE/choroid of morphologically normal 28- to 66-year-old donors and a human central retina longSAGE library made from 41- to 66-year-old donors were generated. Their transcription profiles were entered into a relational database, EyeSAGE, including microarray expression profiles of retina and publicly available normal human tissue SAGE libraries. EyeSAGE was used to identify retina- and RPE-specific and -associated genes, and candidate genes for retina and RPE disease loci. Differential and/or cell-type specific expression was validated by quantitative and single-cell RT-PCR. Cone photoreceptor-associated gene expression was elevated in the macula transcription profiles. Analysis of the longSAGE retina tags enhanced tag-to-gene mapping and revealed alternatively spliced genes. Analysis of candidate gene expression tables for the identified Bardet-Biedl syndrome disease gene (BBS5) in the BBS5 disease region table yielded BBS5 as the top candidate. Compelling candidates for inherited retina diseases were identified. The EyeSAGE database, combining three different gene-profiling platforms including the authors' multidonor-derived retina/RPE SAGE libraries and existing single-donor retina/RPE libraries, is a powerful resource for definition of the retina and RPE transcriptomes. It can be used to identify retina-specific genes, including alternatively spliced transcripts and to prioritize candidate genes within mapped retinal disease regions.

Genotator: a disease-agnostic tool for genetic annotation of disease.

PubMed

Wall, Dennis P; Pivovarov, Rimma; Tong, Mark; Jung, Jae-Yoon; Fusaro, Vincent A; DeLuca, Todd F; Tonellato, Peter J

2010-10-29

Disease-specific genetic information has been increasing at rapid rates as a consequence of recent improvements and massive cost reductions in sequencing technologies. Numerous systems designed to capture and organize this mounting sea of genetic data have emerged, but these resources differ dramatically in their disease coverage and genetic depth. With few exceptions, researchers must manually search a variety of sites to assemble a complete set of genetic evidence for a particular disease of interest, a process that is both time-consuming and error-prone. We designed a real-time aggregation tool that provides both comprehensive coverage and reliable gene-to-disease rankings for any disease. Our tool, called Genotator, automatically integrates data from 11 externally accessible clinical genetics resources and uses these data in a straightforward formula to rank genes in order of disease relevance. We tested the accuracy of coverage of Genotator in three separate diseases for which there exist specialty curated databases, Autism Spectrum Disorder, Parkinson's Disease, and Alzheimer Disease. Genotator is freely available at http://genotator.hms.harvard.edu. Genotator demonstrated that most of the 11 selected databases contain unique information about the genetic composition of disease, with 2514 genes found in only one of the 11 databases. These findings confirm that the integration of these databases provides a more complete picture than would be possible from any one database alone. Genotator successfully identified at least 75% of the top ranked genes for all three of our use cases, including a 90% concordance with the top 40 ranked candidates for Alzheimer Disease. As a meta-query engine, Genotator provides high coverage of both historical genetic research as well as recent advances in the genetic understanding of specific diseases. As such, Genotator provides a real-time aggregation of ranked data that remains current with the pace of research in the disease fields. Genotator's algorithm appropriately transforms query terms to match the input requirements of each targeted databases and accurately resolves named synonyms to ensure full coverage of the genetic results with official nomenclature. Genotator generates an excel-style output that is consistent across disease queries and readily importable to other applications.

HRGFish: A database of hypoxia responsive genes in fishes

NASA Astrophysics Data System (ADS)

Rashid, Iliyas; Nagpure, Naresh Sahebrao; Srivastava, Prachi; Kumar, Ravindra; Pathak, Ajey Kumar; Singh, Mahender; Kushwaha, Basdeo

2017-02-01

Several studies have highlighted the changes in the gene expression due to the hypoxia response in fishes, but the systematic organization of the information and the analytical platform for such genes are lacking. In the present study, an attempt was made to develop a database of hypoxia responsive genes in fishes (HRGFish), integrated with analytical tools, using LAMPP technology. Genes reported in hypoxia response for fishes were compiled through literature survey and the database presently covers 818 gene sequences and 35 gene types from 38 fishes. The upstream fragments (3,000 bp), covered in this database, enables to compute CG dinucleotides frequencies, motif finding of the hypoxia response element, identification of CpG island and mapping with the reference promoter of zebrafish. The database also includes functional annotation of genes and provides tools for analyzing sequences and designing primers for selected gene fragments. This may be the first database on the hypoxia response genes in fishes that provides a workbench to the scientific community involved in studying the evolution and ecological adaptation of the fish species in relation to hypoxia.

In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB

PubMed Central

2013-01-01

Background Though India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and “finishing” expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence. Description By in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly. Conclusion Being first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity. PMID:23336431

In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB.

PubMed

Sarika; Arora, Vasu; Iquebal, Mir Asif; Rai, Anil; Kumar, Dinesh

2013-01-19

Though India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and "finishing" expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence. By in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly. Being first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity.

Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism

PubMed Central

2013-01-01

Background A recent study of lateral septum (LS) suggested a large number of autism-related genes with altered expression in the postpartum state. However, formally testing the findings for enrichment of autism-associated genes proved to be problematic with existing software. Many gene-disease association databases have been curated which are not currently incorporated in popular, full-featured enrichment tools, and the use of custom gene lists in these programs can be difficult to perform and interpret. As a simple alternative, we have developed the Modular Single-set Enrichment Test (MSET), a minimal tool that enables one to easily evaluate expression data for enrichment of any conceivable gene list of interest. Results The MSET approach was validated by testing several publicly available expression data sets for expected enrichment in areas of autism, attention deficit hyperactivity disorder (ADHD), and arthritis. Using nine independent, unique autism gene lists extracted from association databases and two recent publications, a striking consensus of enrichment was detected within gene expression changes in LS of postpartum mice. A network of 160 autism-related genes was identified, representing developmental processes such as synaptic plasticity, neuronal morphogenesis, and differentiation. Additionally, maternal LS displayed enrichment for genes associated with bipolar disorder, schizophrenia, ADHD, and depression. Conclusions The transition to motherhood includes the most fundamental social bonding event in mammals and features naturally occurring changes in sociability. Some individuals with autism, schizophrenia, or other mental health disorders exhibit impaired social traits. Genes involved in these deficits may also contribute to elevated sociability in the maternal brain. To date, this is the first study to show a significant, quantitative link between the maternal brain and mental health disorders using large scale gene expression data. Thus, the postpartum brain may provide a novel and promising platform for understanding the complex genetics of improved sociability that may have direct relevance for multiple psychiatric illnesses. This study also provides an important new tool that fills a critical analysis gap and makes evaluation of enrichment using any database of interest possible with an emphasis on ease of use and methodological transparency. PMID:24245670

Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism.

PubMed

Eisinger, Brian E; Saul, Michael C; Driessen, Terri M; Gammie, Stephen C

2013-11-19

A recent study of lateral septum (LS) suggested a large number of autism-related genes with altered expression in the postpartum state. However, formally testing the findings for enrichment of autism-associated genes proved to be problematic with existing software. Many gene-disease association databases have been curated which are not currently incorporated in popular, full-featured enrichment tools, and the use of custom gene lists in these programs can be difficult to perform and interpret. As a simple alternative, we have developed the Modular Single-set Enrichment Test (MSET), a minimal tool that enables one to easily evaluate expression data for enrichment of any conceivable gene list of interest. The MSET approach was validated by testing several publicly available expression data sets for expected enrichment in areas of autism, attention deficit hyperactivity disorder (ADHD), and arthritis. Using nine independent, unique autism gene lists extracted from association databases and two recent publications, a striking consensus of enrichment was detected within gene expression changes in LS of postpartum mice. A network of 160 autism-related genes was identified, representing developmental processes such as synaptic plasticity, neuronal morphogenesis, and differentiation. Additionally, maternal LS displayed enrichment for genes associated with bipolar disorder, schizophrenia, ADHD, and depression. The transition to motherhood includes the most fundamental social bonding event in mammals and features naturally occurring changes in sociability. Some individuals with autism, schizophrenia, or other mental health disorders exhibit impaired social traits. Genes involved in these deficits may also contribute to elevated sociability in the maternal brain. To date, this is the first study to show a significant, quantitative link between the maternal brain and mental health disorders using large scale gene expression data. Thus, the postpartum brain may provide a novel and promising platform for understanding the complex genetics of improved sociability that may have direct relevance for multiple psychiatric illnesses. This study also provides an important new tool that fills a critical analysis gap and makes evaluation of enrichment using any database of interest possible with an emphasis on ease of use and methodological transparency.

A Bioinformatics Facility for NASA

NASA Technical Reports Server (NTRS)

Schweighofer, Karl; Pohorille, Andrew

2006-01-01

Building on an existing prototype, we have fielded a facility with bioinformatics technologies that will help NASA meet its unique requirements for biological research. This facility consists of a cluster of computers capable of performing computationally intensive tasks, software tools, databases and knowledge management systems. Novel computational technologies for analyzing and integrating new biological data and already existing knowledge have been developed. With continued development and support, the facility will fulfill strategic NASA s bioinformatics needs in astrobiology and space exploration. . As a demonstration of these capabilities, we will present a detailed analysis of how spaceflight factors impact gene expression in the liver and kidney for mice flown aboard shuttle flight STS-108. We have found that many genes involved in signal transduction, cell cycle, and development respond to changes in microgravity, but that most metabolic pathways appear unchanged.

Towards linked open gene mutations data

PubMed Central

2012-01-01

Background With the advent of high-throughput technologies, a great wealth of variation data is being produced. Such information may constitute the basis for correlation analyses between genotypes and phenotypes and, in the future, for personalized medicine. Several databases on gene variation exist, but this kind of information is still scarce in the Semantic Web framework. In this paper, we discuss issues related to the integration of mutation data in the Linked Open Data infrastructure, part of the Semantic Web framework. We present the development of a mapping from the IARC TP53 Mutation database to RDF and the implementation of servers publishing this data. Methods A version of the IARC TP53 Mutation database implemented in a relational database was used as first test set. Automatic mappings to RDF were first created by using D2RQ and later manually refined by introducing concepts and properties from domain vocabularies and ontologies, as well as links to Linked Open Data implementations of various systems of biomedical interest. Since D2RQ query performances are lower than those that can be achieved by using an RDF archive, generated data was also loaded into a dedicated system based on tools from the Jena software suite. Results We have implemented a D2RQ Server for TP53 mutation data, providing data on a subset of the IARC database, including gene variations, somatic mutations, and bibliographic references. The server allows to browse the RDF graph by using links both between classes and to external systems. An alternative interface offers improved performances for SPARQL queries. The resulting data can be explored by using any Semantic Web browser or application. Conclusions This has been the first case of a mutation database exposed as Linked Data. A revised version of our prototype, including further concepts and IARC TP53 Mutation database data sets, is under development. The publication of variation information as Linked Data opens new perspectives: the exploitation of SPARQL searches on mutation data and other biological databases may support data retrieval which is presently not possible. Moreover, reasoning on integrated variation data may support discoveries towards personalized medicine. PMID:22536974

Towards linked open gene mutations data.

PubMed

Zappa, Achille; Splendiani, Andrea; Romano, Paolo

2012-03-28

With the advent of high-throughput technologies, a great wealth of variation data is being produced. Such information may constitute the basis for correlation analyses between genotypes and phenotypes and, in the future, for personalized medicine. Several databases on gene variation exist, but this kind of information is still scarce in the Semantic Web framework. In this paper, we discuss issues related to the integration of mutation data in the Linked Open Data infrastructure, part of the Semantic Web framework. We present the development of a mapping from the IARC TP53 Mutation database to RDF and the implementation of servers publishing this data. A version of the IARC TP53 Mutation database implemented in a relational database was used as first test set. Automatic mappings to RDF were first created by using D2RQ and later manually refined by introducing concepts and properties from domain vocabularies and ontologies, as well as links to Linked Open Data implementations of various systems of biomedical interest. Since D2RQ query performances are lower than those that can be achieved by using an RDF archive, generated data was also loaded into a dedicated system based on tools from the Jena software suite. We have implemented a D2RQ Server for TP53 mutation data, providing data on a subset of the IARC database, including gene variations, somatic mutations, and bibliographic references. The server allows to browse the RDF graph by using links both between classes and to external systems. An alternative interface offers improved performances for SPARQL queries. The resulting data can be explored by using any Semantic Web browser or application. This has been the first case of a mutation database exposed as Linked Data. A revised version of our prototype, including further concepts and IARC TP53 Mutation database data sets, is under development.The publication of variation information as Linked Data opens new perspectives: the exploitation of SPARQL searches on mutation data and other biological databases may support data retrieval which is presently not possible. Moreover, reasoning on integrated variation data may support discoveries towards personalized medicine.

An informatics approach to integrating genetic and neurological data in speech and language neuroscience.

PubMed

Bohland, Jason W; Myers, Emma M; Kim, Esther

2014-01-01

A number of heritable disorders impair the normal development of speech and language processes and occur in large numbers within the general population. While candidate genes and loci have been identified, the gap between genotype and phenotype is vast, limiting current understanding of the biology of normal and disordered processes. This gap exists not only in our scientific knowledge, but also in our research communities, where genetics researchers and speech, language, and cognitive scientists tend to operate independently. Here we describe a web-based, domain-specific, curated database that represents information about genotype-phenotype relations specific to speech and language disorders, as well as neuroimaging results demonstrating focal brain differences in relevant patients versus controls. Bringing these two distinct data types into a common database ( http://neurospeech.org/sldb ) is a first step toward bringing molecular level information into cognitive and computational theories of speech and language function. One bridge between these data types is provided by densely sampled profiles of gene expression in the brain, such as those provided by the Allen Brain Atlases. Here we present results from exploratory analyses of human brain gene expression profiles for genes implicated in speech and language disorders, which are annotated in our database. We then discuss how such datasets can be useful in the development of computational models that bridge levels of analysis, necessary to provide a mechanistic understanding of heritable language disorders. We further describe our general approach to information integration, discuss important caveats and considerations, and offer a specific but speculative example based on genes implicated in stuttering and basal ganglia function in speech motor control.

Comparative transcriptome analysis between planarian Dugesia japonica and other platyhelminth species.

PubMed

Nishimura, Osamu; Hirao, Yukako; Tarui, Hiroshi; Agata, Kiyokazu

2012-06-29

Planarians are considered to be among the extant animals close to one of the earliest groups of organisms that acquired a central nervous system (CNS) during evolution. Planarians have a bilobed brain with nine lateral branches from which a variety of external signals are projected into different portions of the main lobes. Various interneurons process different signals to regulate behavior and learning/memory. Furthermore, planarians have robust regenerative ability and are attracting attention as a new model organism for the study of regeneration. Here we conducted large-scale EST analysis of the head region of the planarian Dugesia japonica to construct a database of the head-region transcriptome, and then performed comparative analyses among related species. A total of 54,752 high-quality EST reads were obtained from a head library of the planarian Dugesia japonica, and 13,167 unigene sequences were produced by de novo assembly. A new method devised here revealed that proteins related to metabolism and defense mechanisms have high flexibility of amino-acid substitutions within the planarian family. Eight-two CNS-development genes were found in the planarian (cf. C. elegans 3; chicken 129). Comparative analysis revealed that 91% of the planarian CNS-development genes could be mapped onto the schistosome genome, but one-third of these shared genes were not expressed in the schistosome. We constructed a database that is a useful resource for comparative planarian transcriptome studies. Analysis comparing homologous genes between two planarian species showed that the potential of genes is important for accumulation of amino-acid substitutions. The presence of many CNS-development genes in our database supports the notion that the planarian has a fundamental brain with regard to evolution and development at not only the morphological/functional, but also the genomic, level. In addition, our results indicate that the planarian CNS-development genes already existed before the divergence of planarians and schistosomes from their common ancestor.

Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

PubMed Central

Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

2014-01-01

We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Availability and implementation: Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. Database URL: http://rged.wall-eva.net PMID:25252782

Comparative Bacterial Proteomics: Analysis of the Core Genome Concept

PubMed Central

Callister, Stephen J.; McCue, Lee Ann; Turse, Joshua E.; Monroe, Matthew E.; Auberry, Kenneth J.; Smith, Richard D.; Adkins, Joshua N.; Lipton, Mary S.

2008-01-01

While comparative bacterial genomic studies commonly predict a set of genes indicative of common ancestry, experimental validation of the existence of this core genome requires extensive measurement and is typically not undertaken. Enabled by an extensive proteome database developed over six years, we have experimentally verified the expression of proteins predicted from genomic ortholog comparisons among 17 environmental and pathogenic bacteria. More exclusive relationships were observed among the expressed protein content of phenotypically related bacteria, which is indicative of the specific lifestyles associated with these organisms. Although genomic studies can establish relative orthologous relationships among a set of bacteria and propose a set of ancestral genes, our proteomics study establishes expressed lifestyle differences among conserved genes and proposes a set of expressed ancestral traits. PMID:18253490

PhytoREF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy.

PubMed

Decelle, Johan; Romac, Sarah; Stern, Rowena F; Bendif, El Mahdi; Zingone, Adriana; Audic, Stéphane; Guiry, Michael D; Guillou, Laure; Tessier, Désiré; Le Gall, Florence; Gourvil, Priscillia; Dos Santos, Adriana L; Probert, Ian; Vaulot, Daniel; de Vargas, Colomban; Christen, Richard

2015-11-01

Photosynthetic eukaryotes have a critical role as the main producers in most ecosystems of the biosphere. The ongoing environmental metabarcoding revolution opens the perspective for holistic ecosystems biological studies of these organisms, in particular the unicellular microalgae that often lack distinctive morphological characters and have complex life cycles. To interpret environmental sequences, metabarcoding necessarily relies on taxonomically curated databases containing reference sequences of the targeted gene (or barcode) from identified organisms. To date, no such reference framework exists for photosynthetic eukaryotes. In this study, we built the PhytoREF database that contains 6490 plastidial 16S rDNA reference sequences that originate from a large diversity of eukaryotes representing all known major photosynthetic lineages. We compiled 3333 amplicon sequences available from public databases and 879 sequences extracted from plastidial genomes, and generated 411 novel sequences from cultured marine microalgal strains belonging to different eukaryotic lineages. A total of 1867 environmental Sanger 16S rDNA sequences were also included in the database. Stringent quality filtering and a phylogeny-based taxonomic classification were applied for each 16S rDNA sequence. The database mainly focuses on marine microalgae, but sequences from land plants (representing half of the PhytoREF sequences) and freshwater taxa were also included to broaden the applicability of PhytoREF to different aquatic and terrestrial habitats. PhytoREF, accessible via a web interface (http://phytoref.fr), is a new resource in molecular ecology to foster the discovery, assessment and monitoring of the diversity of photosynthetic eukaryotes using high-throughput sequencing. © 2015 John Wiley & Sons Ltd.

Proteins of Unknown Biochemical Function: A Persistent Problem and a Roadmap to Help Overcome It.

PubMed

Niehaus, Thomas D; Thamm, Antje M K; de Crécy-Lagard, Valérie; Hanson, Andrew D

2015-11-01

The number of sequenced genomes is rapidly increasing, but functional annotation of the genes in these genomes lags far behind. Even in Arabidopsis (Arabidopsis thaliana), only approximately 40% of enzyme- and transporter-encoding genes have credible functional annotations, and this number is even lower in nonmodel plants. Functional characterization of unknown genes is a challenge, but various databases (e.g. for protein localization and coexpression) can be mined to provide clues. If homologous microbial genes exist-and about one-half the genes encoding unknown enzymes and transporters in Arabidopsis have microbial homologs-cross-kingdom comparative genomics can powerfully complement plant-based data. Multiple lines of evidence can strengthen predictions and warrant experimental characterization. In some cases, relatively quick tests in genetically tractable microbes can determine whether a prediction merits biochemical validation, which is costly and demands specialized skills. © 2015 American Society of Plant Biologists. All Rights Reserved.

PIPEMicroDB: microsatellite database and primer generation tool for pigeonpea genome

PubMed Central

Sarika; Arora, Vasu; Iquebal, M. A.; Rai, Anil; Kumar, Dinesh

2013-01-01

Molecular markers play a significant role for crop improvement in desirable characteristics, such as high yield, resistance to disease and others that will benefit the crop in long term. Pigeonpea (Cajanus cajan L.) is the recently sequenced legume by global consortium led by ICRISAT (Hyderabad, India) and been analysed for gene prediction, synteny maps, markers, etc. We present PIgeonPEa Microsatellite DataBase (PIPEMicroDB) with an automated primer designing tool for pigeonpea genome, based on chromosome wise as well as location wise search of primers. Total of 123 387 Short Tandem Repeats (STRs) were extracted from pigeonpea genome, available in public domain using MIcroSAtellite tool (MISA). The database is an online relational database based on ‘three-tier architecture’ that catalogues information of microsatellites in MySQL and user-friendly interface is developed using PHP. Search for STRs may be customized by limiting their location on chromosome as well as number of markers in that range. This is a novel approach and is not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of selected markers with left and right flankings of size up to 500 bp. This will enable researchers to select markers of choice at desired interval over the chromosome. Furthermore, one can use individual STRs of a targeted region over chromosome to narrow down location of gene of interest or linked Quantitative Trait Loci (QTLs). Although it is an in silico approach, markers’ search based on characteristics and location of STRs is expected to be beneficial for researchers. Database URL: http://cabindb.iasri.res.in/pigeonpea/ PMID:23396298

PIPEMicroDB: microsatellite database and primer generation tool for pigeonpea genome.

PubMed

Sarika; Arora, Vasu; Iquebal, M A; Rai, Anil; Kumar, Dinesh

2013-01-01

Molecular markers play a significant role for crop improvement in desirable characteristics, such as high yield, resistance to disease and others that will benefit the crop in long term. Pigeonpea (Cajanus cajan L.) is the recently sequenced legume by global consortium led by ICRISAT (Hyderabad, India) and been analysed for gene prediction, synteny maps, markers, etc. We present PIgeonPEa Microsatellite DataBase (PIPEMicroDB) with an automated primer designing tool for pigeonpea genome, based on chromosome wise as well as location wise search of primers. Total of 123 387 Short Tandem Repeats (STRs) were extracted from pigeonpea genome, available in public domain using MIcroSAtellite tool (MISA). The database is an online relational database based on 'three-tier architecture' that catalogues information of microsatellites in MySQL and user-friendly interface is developed using PHP. Search for STRs may be customized by limiting their location on chromosome as well as number of markers in that range. This is a novel approach and is not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of selected markers with left and right flankings of size up to 500 bp. This will enable researchers to select markers of choice at desired interval over the chromosome. Furthermore, one can use individual STRs of a targeted region over chromosome to narrow down location of gene of interest or linked Quantitative Trait Loci (QTLs). Although it is an in silico approach, markers' search based on characteristics and location of STRs is expected to be beneficial for researchers. Database URL: http://cabindb.iasri.res.in/pigeonpea/

Evaluating the quality of Marfan genotype-phenotype correlations in existing FBN1 databases.

PubMed

Groth, Kristian A; Von Kodolitsch, Yskert; Kutsche, Kerstin; Gaustadnes, Mette; Thorsen, Kasper; Andersen, Niels H; Gravholt, Claus H

2017-07-01

Genetic FBN1 testing is pivotal for confirming the clinical diagnosis of Marfan syndrome. In an effort to evaluate variant causality, FBN1 databases are often used. We evaluated the current databases regarding FBN1 variants and validated associated phenotype records with a new Marfan syndrome geno-phenotyping tool called the Marfan score. We evaluated four databases (UMD-FBN1, ClinVar, the Human Gene Mutation Database (HGMD), and Uniprot) containing 2,250 FBN1 variants supported by 4,904 records presented in 307 references. The Marfan score calculated for phenotype data from the records quantified variant associations with Marfan syndrome phenotype. We calculated a Marfan score for 1,283 variants, of which we confirmed the database diagnosis of Marfan syndrome in 77.1%. This represented only 35.8% of the total registered variants; 18.5-33.3% (UMD-FBN1 versus HGMD) of variants associated with Marfan syndrome in the databases could not be confirmed by the recorded phenotype. FBN1 databases can be imprecise and incomplete. Data should be used with caution when evaluating FBN1 variants. At present, the UMD-FBN1 database seems to be the biggest and best curated; therefore, it is the most comprehensive database. However, the need for better genotype-phenotype curated databases is evident, and we hereby present such a database.Genet Med advance online publication 01 December 2016.

Partial least squares based identification of Duchenne muscular dystrophy specific genes.

PubMed

An, Hui-bo; Zheng, Hua-cheng; Zhang, Li; Ma, Lin; Liu, Zheng-yan

2013-11-01

Large-scale parallel gene expression analysis has provided a greater ease for investigating the underlying mechanisms of Duchenne muscular dystrophy (DMD). Previous studies typically implemented variance/regression analysis, which would be fundamentally flawed when unaccounted sources of variability in the arrays existed. Here we aim to identify genes that contribute to the pathology of DMD using partial least squares (PLS) based analysis. We carried out PLS-based analysis with two datasets downloaded from the Gene Expression Omnibus (GEO) database to identify genes contributing to the pathology of DMD. Except for the genes related to inflammation, muscle regeneration and extracellular matrix (ECM) modeling, we found some genes with high fold change, which have not been identified by previous studies, such as SRPX, GPNMB, SAT1, and LYZ. In addition, downregulation of the fatty acid metabolism pathway was found, which may be related to the progressive muscle wasting process. Our results provide a better understanding for the downstream mechanisms of DMD.

The Matchmaker Exchange: a platform for rare disease gene discovery.

PubMed

Philippakis, Anthony A; Azzariti, Danielle R; Beltran, Sergi; Brookes, Anthony J; Brownstein, Catherine A; Brudno, Michael; Brunner, Han G; Buske, Orion J; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O M; den Dunnen, Johan T; Firth, Helen V; Gibbs, Richard A; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A; Hamosh, Ada; Holm, Ingrid A; Huang, Lijia; Hurles, Matthew E; Hutton, Ben; Krier, Joel B; Misyura, Andriy; Mungall, Christopher J; Paschall, Justin; Paten, Benedict; Robinson, Peter N; Schiettecatte, François; Sobreira, Nara L; Swaminathan, Ganesh J; Taschner, Peter E; Terry, Sharon F; Washington, Nicole L; Züchner, Stephan; Boycott, Kym M; Rehm, Heidi L

2015-10-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. © 2015 WILEY PERIODICALS, INC.

PedAM: a database for Pediatric Disease Annotation and Medicine.

PubMed

Jia, Jinmeng; An, Zhongxin; Ming, Yue; Guo, Yongli; Li, Wei; Li, Xin; Liang, Yunxiang; Guo, Dongming; Tai, Jun; Chen, Geng; Jin, Yaqiong; Liu, Zhimei; Ni, Xin; Shi, Tieliu

2018-01-04

There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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.

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

PubMed Central

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi; Brookes, Anthony J.; Brownstein, Catherine A.; Brudno, Michael; Brunner, Han G.; Buske, Orion J.; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O.M.; den Dunnen, Johan T.; Firth, Helen V.; Gibbs, Richard A.; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A.; Hamosh, Ada; Holm, Ingrid A.; Huang, Lijia; Hurles, Matthew E.; Hutton, Ben; Krier, Joel B.; Misyura, Andriy; Mungall, Christopher J.; Paschall, Justin; Paten, Benedict; Robinson, Peter N.; Schiettecatte, François; Sobreira, Nara L.; Swaminathan, Ganesh J.; Taschner, Peter E.; Terry, Sharon F.; Washington, Nicole L.; Züchner, Stephan; Boycott, Kym M.; Rehm, Heidi L.

2015-01-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. PMID:26295439

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

DOE PAGES

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi; ...

2015-09-17

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be amore » reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. In conclusion, three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.« less

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

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

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be amore » reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. In conclusion, three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.« less

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

MiRNA-TF-gene network analysis through ranking of biomolecules for multi-informative uterine leiomyoma dataset.

PubMed

Mallik, Saurav; Maulik, Ujjwal

2015-10-01

Gene ranking is an important problem in bioinformatics. Here, we propose a new framework for ranking biomolecules (viz., miRNAs, transcription-factors/TFs and genes) in a multi-informative uterine leiomyoma dataset having both gene expression and methylation data using (statistical) eigenvector centrality based approach. At first, genes that are both differentially expressed and methylated, are identified using Limma statistical test. A network, comprising these genes, corresponding TFs from TRANSFAC and ITFP databases, and targeter miRNAs from miRWalk database, is then built. The biomolecules are then ranked based on eigenvector centrality. Our proposed method provides better average accuracy in hub gene and non-hub gene classifications than other methods. Furthermore, pre-ranked Gene set enrichment analysis is applied on the pathway database as well as GO-term databases of Molecular Signatures Database with providing a pre-ranked gene-list based on different centrality values for comparing among the ranking methods. Finally, top novel potential gene-markers for the uterine leiomyoma are provided. Copyright © 2015 Elsevier Inc. All rights reserved.

BGDB: a database of bivalent genes.

PubMed

Li, Qingyan; Lian, Shuabin; Dai, Zhiming; Xiang, Qian; Dai, Xianhua

2013-01-01

Bivalent gene is a gene marked with both H3K4me3 and H3K27me3 epigenetic modification in the same area, and is proposed to play a pivotal role related to pluripotency in embryonic stem (ES) cells. Identification of these bivalent genes and understanding their functions are important for further research of lineage specification and embryo development. So far, lots of genome-wide histone modification data were generated in mouse and human ES cells. These valuable data make it possible to identify bivalent genes, but no comprehensive data repositories or analysis tools are available for bivalent genes currently. In this work, we develop BGDB, the database of bivalent genes. The database contains 6897 bivalent genes in human and mouse ES cells, which are manually collected from scientific literature. Each entry contains curated information, including genomic context, sequences, gene ontology and other relevant information. The web services of BGDB database were implemented with PHP + MySQL + JavaScript, and provide diverse query functions. Database URL: http://dailab.sysu.edu.cn/bgdb/

Generation and Analysis of Expressed Sequence Tags from Olea europaea L.

PubMed Central

Ozdemir Ozgenturk, Nehir; Oruç, Fatma; Sezerman, Ugur; Kuçukural, Alper; Vural Korkut, Senay; Toksoz, Feriha; Un, Cemal

2010-01-01

Olive (Olea europaea L.) is an important source of edible oil which was originated in Near-East region. In this study, two cDNA libraries were constructed from young olive leaves and immature olive fruits for generation of ESTs to discover the novel genes and search the function of unknown genes of olive. The randomly selected 3840 colonies were sequenced for EST collection from both libraries. Readable 2228 sequences for olive leaf and 1506 sequences for olive fruit were assembled into 205 and 69 contigs, respectively, whereas 2478 were singletons. Putative functions of all 2752 differentially expressed unique sequences were designated by gene homology based on BLAST and annotated using BLAST2GO. While 1339 ESTs show no homology to the database, 2024 ESTs have homology (under 80%) with hypothetical proteins, putative proteins, expressed proteins, and unknown proteins in NCBI-GenBank. 635 EST's unique genes sequence have been identified by over 80% homology to known function in other species which were not previously described in Olea family. Only 3.1% of total EST's was shown similarity with olive database existing in NCBI. This generated EST's data and consensus sequences were submitted to NCBI as valuable source for functional genome studies of olive. PMID:21197085

ExAtlas: An interactive online tool for meta-analysis of gene expression data.

PubMed

Sharov, Alexei A; Schlessinger, David; Ko, Minoru S H

2015-12-01

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

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.

The "GeneTrustee": a universal identification system that ensures privacy and confidentiality for human genetic databases.

PubMed

Burnett, Leslie; Barlow-Stewart, Kris; Proos, Anné L; Aizenberg, Harry

2003-05-01

This article describes a generic model for access to samples and information in human genetic databases. The model utilises a "GeneTrustee", a third-party intermediary independent of the subjects and of the investigators or database custodians. The GeneTrustee model has been implemented successfully in various community genetics screening programs and has facilitated research access to genetic databases while protecting the privacy and confidentiality of research subjects. The GeneTrustee model could also be applied to various types of non-conventional genetic databases, including neonatal screening Guthrie card collections, and to forensic DNA samples.

Gene annotation from scientific literature using mappings between keyword systems.

PubMed

Pérez, Antonio J; Perez-Iratxeta, Carolina; Bork, Peer; Thode, Guillermo; Andrade, Miguel A

2004-09-01

The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat

Application of Genetic/Genomic Approaches to Allergic Disorders

PubMed Central

Baye, Tesfaye M.; Martin, Lisa J.; Khurana Hershey, Gurjit K.

2010-01-01

Completion of the human genome project and rapid progress in genetics and bioinformatics have enabled the development of large public databases, which include genetic and genomic data linked to clinical health data. With the massive amount of information available, clinicians and researchers have the unique opportunity to complement and integrate their daily practice with the existing resources to clarify the underlying etiology of complex phenotypes such as allergic diseases. The genome itself is now often utilized as a starting point for many studies and multiple innovative approaches have emerged applying genetic/genomic strategies to key questions in the field of allergy and immunology. There have been several successes, which have uncovered new insights into the biologic underpinnings of allergic disorders. Herein, we will provide an in depth review of genomic approaches to identifying genes and biologic networks involved in allergic diseases. We will discuss genetic and phenotypic variation, statistical approaches for gene discovery, public databases, functional genomics, clinical implications, and the challenges that remain. PMID:20638111

The Complex Transcriptional Response of Acaryochloris marina to Different Oxygen Levels.

PubMed

Hernández-Prieto, Miguel A; Lin, Yuankui; Chen, Min

2017-02-09

Ancient oxygenic photosynthetic prokaryotes produced oxygen as a waste product, but existed for a long time under an oxygen-free (anoxic) atmosphere, before an oxic atmosphere emerged. The change in oxygen levels in the atmosphere influenced the chemistry and structure of many enzymes that contained prosthetic groups that were inactivated by oxygen. In the genome of Acaryochloris marina , multiple gene copies exist for proteins that are normally encoded by a single gene copy in other cyanobacteria. Using high throughput RNA sequencing to profile transcriptome responses from cells grown under microoxic and hyperoxic conditions, we detected 8446 transcripts out of the 8462 annotated genes in the Cyanobase database. Two-thirds of the 50 most abundant transcripts are key proteins in photosynthesis. Microoxic conditions negatively affected the levels of expression of genes encoding photosynthetic complexes, with the exception of some subunits. In addition to the known regulation of the multiple copies of psbA , we detected a similar transcriptional pattern for psbJ and psbU , which might play a key role in the altered components of photosystem II. Furthermore, regulation of genes encoding proteins important for reactive oxygen species-scavenging is discussed at genome level, including, for the first time, specific small RNAs having possible regulatory roles under varying oxygen levels. Copyright © 2017 Hernandez-Prieto et al.

The Complex Transcriptional Response of Acaryochloris marina to Different Oxygen Levels

PubMed Central

Hernández-Prieto, Miguel A.; Lin, Yuankui; Chen, Min

2016-01-01

Ancient oxygenic photosynthetic prokaryotes produced oxygen as a waste product, but existed for a long time under an oxygen-free (anoxic) atmosphere, before an oxic atmosphere emerged. The change in oxygen levels in the atmosphere influenced the chemistry and structure of many enzymes that contained prosthetic groups that were inactivated by oxygen. In the genome of Acaryochloris marina, multiple gene copies exist for proteins that are normally encoded by a single gene copy in other cyanobacteria. Using high throughput RNA sequencing to profile transcriptome responses from cells grown under microoxic and hyperoxic conditions, we detected 8446 transcripts out of the 8462 annotated genes in the Cyanobase database. Two-thirds of the 50 most abundant transcripts are key proteins in photosynthesis. Microoxic conditions negatively affected the levels of expression of genes encoding photosynthetic complexes, with the exception of some subunits. In addition to the known regulation of the multiple copies of psbA, we detected a similar transcriptional pattern for psbJ and psbU, which might play a key role in the altered components of photosystem II. Furthermore, regulation of genes encoding proteins important for reactive oxygen species-scavenging is discussed at genome level, including, for the first time, specific small RNAs having possible regulatory roles under varying oxygen levels. PMID:27974439

Human Ageing Genomic Resources: new and updated databases

PubMed Central

Tacutu, Robi; Thornton, Daniel; Johnson, Emily; Budovsky, Arie; Barardo, Diogo; Craig, Thomas; Diana, Eugene; Lehmann, Gilad; Toren, Dmitri; Wang, Jingwei; Fraifeld, Vadim E

2018-01-01

Abstract In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR’s main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/). PMID:29121237

SNPdbe: constructing an nsSNP functional impacts database.

PubMed

Schaefer, Christian; Meier, Alice; Rost, Burkhard; Bromberg, Yana

2012-02-15

Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe-SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; 'human' being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. http://www.rostlab.org/services/snpdbe.

SGDB: a database of synthetic genes re-designed for optimizing protein over-expression.

PubMed

Wu, Gang; Zheng, Yuanpu; Qureshi, Imran; Zin, Htar Thant; Beck, Tyler; Bulka, Blazej; Freeland, Stephen J

2007-01-01

Here we present the Synthetic Gene Database (SGDB): a relational database that houses sequences and associated experimental information on synthetic (artificially engineered) genes from all peer-reviewed studies published to date. At present, the database comprises information from more than 200 published experiments. This resource not only provides reference material to guide experimentalists in designing new genes that improve protein expression, but also offers a dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that influence gene expression. The SGDB was built under MySQL database management system. We also offer an XML schema for standardized data description of synthetic genes. Users can access the database at http://www.evolvingcode.net/codon/sgdb/index.php, or batch downloads all information through XML files. Moreover, users may visually compare the coding sequences of a synthetic gene and its natural counterpart with an integrated web tool at http://www.evolvingcode.net/codon/sgdb/aligner.php, and discuss questions, findings and related information on an associated e-forum at http://www.evolvingcode.net/forum/viewforum.php?f=27.

SASD: the Synthetic Alternative Splicing Database for identifying novel isoform from proteomics

PubMed Central

2013-01-01

Background Alternative splicing is an important and widespread mechanism for generating protein diversity and regulating protein expression. High-throughput identification and analysis of alternative splicing in the protein level has more advantages than in the mRNA level. The combination of alternative splicing database and tandem mass spectrometry provides a powerful technique for identification, analysis and characterization of potential novel alternative splicing protein isoforms from proteomics. Therefore, based on the peptidomic database of human protein isoforms for proteomics experiments, our objective is to design a new alternative splicing database to 1) provide more coverage of genes, transcripts and alternative splicing, 2) exclusively focus on the alternative splicing, and 3) perform context-specific alternative splicing analysis. Results We used a three-step pipeline to create a synthetic alternative splicing database (SASD) to identify novel alternative splicing isoforms and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. First, we extracted information on gene structures of all genes in the Ensembl Genes 71 database and incorporated the Integrated Pathway Analysis Database. Then, we compiled artificial splicing transcripts. Lastly, we translated the artificial transcripts into alternative splicing peptides. The SASD is a comprehensive database containing 56,630 genes (Ensembl gene IDs), 95,260 transcripts (Ensembl transcript IDs), and 11,919,779 Alternative Splicing peptides, and also covering about 1,956 pathways, 6,704 diseases, 5,615 drugs, and 52 organs. The database has a web-based user interface that allows users to search, display and download a single gene/transcript/protein, custom gene set, pathway, disease, drug, organ related alternative splicing. Moreover, the quality of the database was validated with comparison to other known databases and two case studies: 1) in liver cancer and 2) in breast cancer. Conclusions The SASD provides the scientific community with an efficient means to identify, analyze, and characterize novel Exon Skipping and Intron Retention protein isoforms from mass spectrometry and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. PMID:24267658

Macronutrients and the FTO gene expression in hypothalamus; a systematic review of experimental studies.

PubMed

Doaei, Saeid; Kalantari, Naser; Mohammadi, Nastaran Keshavarz; Tabesh, Ghasem Azizi; Gholamalizadeh, Maryam

The various studies have examined the relationship between FTO gene expression and macronutrients levels. In order to obtain better viewpoint from this interactions, all of existing studies were reviewed systematically. All published papers have been obtained and reviewed using standard and sensitive keywords from databases such as CINAHL, Embase, PubMed, PsycInfo, and the Cochrane, from 1990 to 2016. The results indicated that all of 6 studies that met the inclusion criteria (from a total of 428 published article) found FTO gene expression changes at short-term follow-ups. Four of six studies found an increased FTO gene expression after calorie restriction, while two of them indicated decreased FTO gene expression. The effect of protein, carbohydrate and fat were separately assessed and suggested by all of six studies. In Conclusion, The level of FTO gene expression in hypothalamus is related to macronutrients levels. Future research should evaluate the long-term impact of dietary interventions. Copyright © 2017. Published by Elsevier B.V.

BGDB: a database of bivalent genes

PubMed Central

Li, Qingyan; Lian, Shuabin; Dai, Zhiming; Xiang, Qian; Dai, Xianhua

2013-01-01

Bivalent gene is a gene marked with both H3K4me3 and H3K27me3 epigenetic modification in the same area, and is proposed to play a pivotal role related to pluripotency in embryonic stem (ES) cells. Identification of these bivalent genes and understanding their functions are important for further research of lineage specification and embryo development. So far, lots of genome-wide histone modification data were generated in mouse and human ES cells. These valuable data make it possible to identify bivalent genes, but no comprehensive data repositories or analysis tools are available for bivalent genes currently. In this work, we develop BGDB, the database of bivalent genes. The database contains 6897 bivalent genes in human and mouse ES cells, which are manually collected from scientific literature. Each entry contains curated information, including genomic context, sequences, gene ontology and other relevant information. The web services of BGDB database were implemented with PHP + MySQL + JavaScript, and provide diverse query functions. Database URL: http://dailab.sysu.edu.cn/bgdb/ PMID:23894186

Resolving the homology—function relationship through comparative genomics of membrane-trafficking machinery and parasite cell biology

PubMed Central

Klinger, Christen M.; Ramirez-Macias, Inmaculada; Herman, Emily K.; Turkewitz, Aaron P.; Field, Mark C.; Dacks, Joel B.

2016-01-01

With advances in DNA sequencing technology, it is increasingly common and tractable to informatically look for genes of interest in the genomic databases of parasitic organisms and infer cellular states. Assignment of a putative gene function based on homology to functionally characterized genes in other organisms, though powerful, relies on the implicit assumption of functional homology, i.e. that orthology indicates conserved function. Eukaryotes reveal a dazzling array of cellular features and structural organization, suggesting a concomitant diversity in their underlying molecular machinery. Significantly, examples of novel functions for pre-existing or new paralogues are not uncommon. Do these examples undermine the basic assumption of functional homology, especially in parasitic protists, which are often highly derived? Here we examine the extent to which functional homology exists between organisms spanning the eukaryotic lineage. By comparing membrane trafficking proteins between parasitic protists and traditional model organisms, where direct functional evidence is available, we find that function is indeed largely conserved between orthologues, albeit with significant adaptation arising from the unique biological features within each lineage. PMID:27444378

A Weighted Multipath Measurement Based on Gene Ontology for Estimating Gene Products Similarity

PubMed Central

Liu, Lizhen; Dai, Xuemin; Song, Wei; Lu, Jingli

2014-01-01

Abstract Many different methods have been proposed for calculating the semantic similarity of term pairs based on gene ontology (GO). Most existing methods are based on information content (IC), and the methods based on IC are used more commonly than those based on the structure of GO. However, most IC-based methods not only fail to handle identical annotations but also show a strong bias toward well-annotated proteins. We propose a new method called weighted multipath measurement (WMM) for estimating the semantic similarity of gene products based on the structure of the GO. We not only considered the contribution of every path between two GO terms but also took the depth of the lowest common ancestors into account. We assigned different weights for different kinds of edges in GO graph. The similarity values calculated by WMM can be reused because they are only relative to the characteristics of GO terms. Experimental results showed that the similarity values obtained by WMM have a higher accuracy. We compared the performance of WMM with that of other methods using GO data and gene annotation datasets for yeast and humans downloaded from the GO database. We found that WMM is more suited for prediction of gene function than most existing IC-based methods and that it can distinguish proteins with identical annotations (two proteins are annotated with the same terms) from each other. PMID:25229994

Gene regulation knowledge commons: community action takes care of DNA binding transcription factors

PubMed Central

Tripathi, Sushil; Vercruysse, Steven; Chawla, Konika; Christie, Karen R.; Blake, Judith A.; Huntley, Rachael P.; Orchard, Sandra; Hermjakob, Henning; Thommesen, Liv; Lægreid, Astrid; Kuiper, Martin

2016-01-01

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process. Database URL: http://www.tfcheckpoint.org PMID:27270715

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation

PubMed Central

Wang, Luman; Mo, Qiaochu; Wang, Jianxin

2015-01-01

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. PMID:26881263

MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation.

PubMed

Wang, Luman; Mo, Qiaochu; Wang, Jianxin

2015-01-01

Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches.

MitoRes: a resource of nuclear-encoded mitochondrial genes and their products in Metazoa.

PubMed

Catalano, Domenico; Licciulli, Flavio; Turi, Antonio; Grillo, Giorgio; Saccone, Cecilia; D'Elia, Domenica

2006-01-24

Mitochondria are sub-cellular organelles that have a central role in energy production and in other metabolic pathways of all eukaryotic respiring cells. In the last few years, with more and more genomes being sequenced, a huge amount of data has been generated providing an unprecedented opportunity to use the comparative analysis approach in studies of evolution and functional genomics with the aim of shedding light on molecular mechanisms regulating mitochondrial biogenesis and metabolism. In this context, the problem of the optimal extraction of representative datasets of genomic and proteomic data assumes a crucial importance. Specialised resources for nuclear-encoded mitochondria-related proteins already exist; however, no mitochondrial database is currently available with the same features of MitoRes, which is an update of the MitoNuc database extensively modified in its structure, data sources and graphical interface. It contains data on nuclear-encoded mitochondria-related products for any metazoan species for which this type of data is available and also provides comprehensive sequence datasets (gene, transcript and protein) as well as useful tools for their extraction and export. MitoRes http://www2.ba.itb.cnr.it/MitoRes/ consolidates information from publicly external sources and automatically annotates them into a relational database. Additionally, it also clusters proteins on the basis of their sequence similarity and interconnects them with genomic data. The search engine and sequence management tools allow the query/retrieval of the database content and the extraction and export of sequences (gene, transcript, protein) and related sub-sequences (intron, exon, UTR, CDS, signal peptide and gene flanking regions) ready to be used for in silico analysis. The tool we describe here has been developed to support lab scientists and bioinformaticians alike in the characterization of molecular features and evolution of mitochondrial targeting sequences. The way it provides for the retrieval and extraction of sequences allows the user to overcome the obstacles encountered in the integrative use of different bioinformatic resources and the completeness of the sequence collection allows intra- and interspecies comparison at different biological levels (gene, transcript and protein).

The origin and functional transition of P34.

PubMed

Li, Q-G; Zhang, Y-M

2013-03-01

P34, a storage protein and major soybean allergen, has undergone a functional transition from a cysteine peptidase to a syringolide receptor. An exploration of the evolutionary mechanism of this functional transition is made. To identify homologous genes of P34, syntenic network was constructed using syntenic relationships from the Plant Genome Duplication Database. The collected homologous genes, along with SPE31, a highly homologous protein to P34 from the seeds of Pachyrhizus erosus, were used to construct a phylogenetic tree. The results show that multiple gene duplications, exon shuffling and following granulin domain loss and some critical point mutations are associated with the functional transition. Although some tests suggested the existence of positive selection, the possibility that random fixation under relaxation of purifying selection results in the functional transition is also supported. In addition, the genes Glyma08g12340 and Medtr8g086470 may belong to a new group within the papain family.

The origin and functional transition of P34

PubMed Central

Li, Q-G; Zhang, Y-M

2013-01-01

P34, a storage protein and major soybean allergen, has undergone a functional transition from a cysteine peptidase to a syringolide receptor. An exploration of the evolutionary mechanism of this functional transition is made. To identify homologous genes of P34, syntenic network was constructed using syntenic relationships from the Plant Genome Duplication Database. The collected homologous genes, along with SPE31, a highly homologous protein to P34 from the seeds of Pachyrhizus erosus, were used to construct a phylogenetic tree. The results show that multiple gene duplications, exon shuffling and following granulin domain loss and some critical point mutations are associated with the functional transition. Although some tests suggested the existence of positive selection, the possibility that random fixation under relaxation of purifying selection results in the functional transition is also supported. In addition, the genes Glyma08g12340 and Medtr8g086470 may belong to a new group within the papain family. PMID:23211789

Drug-Path: a database for drug-induced pathways

PubMed Central

Zeng, Hui; Cui, Qinghua

2015-01-01

Some databases for drug-associated pathways have been built and are publicly available. However, the pathways curated in most of these databases are drug-action or drug-metabolism pathways. In recent years, high-throughput technologies such as microarray and RNA-sequencing have produced lots of drug-induced gene expression profiles. Interestingly, drug-induced gene expression profile frequently show distinct patterns, indicating that drugs normally induce the activation or repression of distinct pathways. Therefore, these pathways contribute to study the mechanisms of drugs and drug-repurposing. Here, we present Drug-Path, a database of drug-induced pathways, which was generated by KEGG pathway enrichment analysis for drug-induced upregulated genes and downregulated genes based on drug-induced gene expression datasets in Connectivity Map. Drug-Path provides user-friendly interfaces to retrieve, visualize and download the drug-induced pathway data in the database. In addition, the genes deregulated by a given drug are highlighted in the pathways. All data were organized using SQLite. The web site was implemented using Django, a Python web framework. Finally, we believe that this database will be useful for related researches. Database URL: http://www.cuilab.cn/drugpath PMID:26130661

The GermOnline cross-species systems browser provides comprehensive information on genes and gene products relevant for sexual reproduction.

PubMed

Gattiker, Alexandre; Niederhauser-Wiederkehr, Christa; Moore, James; Hermida, Leandro; Primig, Michael

2007-01-01

We report a novel release of the GermOnline knowledgebase covering genes relevant for the cell cycle, gametogenesis and fertility. GermOnline was extended into a cross-species systems browser including information on DNA sequence annotation, gene expression and the function of gene products. The database covers eight model organisms and Homo sapiens, for which complete genome annotation data are available. The database is now built around a sophisticated genome browser (Ensembl), our own microarray information management and annotation system (MIMAS) used to extensively describe experimental data obtained with high-density oligonucleotide microarrays (GeneChips) and a comprehensive system for online editing of database entries (MediaWiki). The RNA data include results from classical microarrays as well as tiling arrays that yield information on RNA expression levels, transcript start sites and lengths as well as exon composition. Members of the research community are solicited to help GermOnline curators keep database entries on genes and gene products complete and accurate. The database is accessible at http://www.germonline.org/.

DynGO: a tool for visualizing and mining of Gene Ontology and its associations

PubMed Central

Liu, Hongfang; Hu, Zhang-Zhi; Wu, Cathy H

2005-01-01

Background A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed. Results We present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. Conclusion We have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website . PMID:16091147

NeisseriaBase: a specialised Neisseria genomic resource and analysis platform.

PubMed

Zheng, Wenning; Mutha, Naresh V R; Heydari, Hamed; Dutta, Avirup; Siow, Cheuk Chuen; Jakubovics, Nicholas S; Wee, Wei Yee; Tan, Shi Yang; Ang, Mia Yang; Wong, Guat Jah; Choo, Siew Woh

2016-01-01

Background. The gram-negative Neisseria is associated with two of the most potent human epidemic diseases: meningococcal meningitis and gonorrhoea. In both cases, disease is caused by bacteria colonizing human mucosal membrane surfaces. Overall, the genus shows great diversity and genetic variation mainly due to its ability to acquire and incorporate genetic material from a diverse range of sources through horizontal gene transfer. Although a number of databases exist for the Neisseria genomes, they are mostly focused on the pathogenic species. In this present study we present the freely available NeisseriaBase, a database dedicated to the genus Neisseria encompassing the complete and draft genomes of 15 pathogenic and commensal Neisseria species. Methods. The genomic data were retrieved from National Center for Biotechnology Information (NCBI) and annotated using the RAST server which were then stored into the MySQL database. The protein-coding genes were further analyzed to obtain information such as calculation of GC content (%), predicted hydrophobicity and molecular weight (Da) using in-house Perl scripts. The web application was developed following the secure four-tier web application architecture: (1) client workstation, (2) web server, (3) application server, and (4) database server. The web interface was constructed using PHP, JavaScript, jQuery, AJAX and CSS, utilizing the model-view-controller (MVC) framework. The in-house developed bioinformatics tools implemented in NeisseraBase were developed using Python, Perl, BioPerl and R languages. Results. Currently, NeisseriaBase houses 603,500 Coding Sequences (CDSs), 16,071 RNAs and 13,119 tRNA genes from 227 Neisseria genomes. The database is equipped with interactive web interfaces. Incorporation of the JBrowse genome browser in the database enables fast and smooth browsing of Neisseria genomes. NeisseriaBase includes the standard BLAST program to facilitate homology searching, and for Virulence Factor Database (VFDB) specific homology searches, the VFDB BLAST is also incorporated into the database. In addition, NeisseriaBase is equipped with in-house designed tools such as the Pairwise Genome Comparison tool (PGC) for comparative genomic analysis and the Pathogenomics Profiling Tool (PathoProT) for the comparative pathogenomics analysis of Neisseria strains. Discussion. This user-friendly database not only provides access to a host of genomic resources on Neisseria but also enables high-quality comparative genome analysis, which is crucial for the expanding scientific community interested in Neisseria research. This database is freely available at http://neisseria.um.edu.my.

NeisseriaBase: a specialised Neisseria genomic resource and analysis platform

PubMed Central

Zheng, Wenning; Mutha, Naresh V.R.; Heydari, Hamed; Dutta, Avirup; Siow, Cheuk Chuen; Jakubovics, Nicholas S.; Wee, Wei Yee; Tan, Shi Yang; Ang, Mia Yang; Wong, Guat Jah

2016-01-01

Background. The gram-negative Neisseria is associated with two of the most potent human epidemic diseases: meningococcal meningitis and gonorrhoea. In both cases, disease is caused by bacteria colonizing human mucosal membrane surfaces. Overall, the genus shows great diversity and genetic variation mainly due to its ability to acquire and incorporate genetic material from a diverse range of sources through horizontal gene transfer. Although a number of databases exist for the Neisseria genomes, they are mostly focused on the pathogenic species. In this present study we present the freely available NeisseriaBase, a database dedicated to the genus Neisseria encompassing the complete and draft genomes of 15 pathogenic and commensal Neisseria species. Methods. The genomic data were retrieved from National Center for Biotechnology Information (NCBI) and annotated using the RAST server which were then stored into the MySQL database. The protein-coding genes were further analyzed to obtain information such as calculation of GC content (%), predicted hydrophobicity and molecular weight (Da) using in-house Perl scripts. The web application was developed following the secure four-tier web application architecture: (1) client workstation, (2) web server, (3) application server, and (4) database server. The web interface was constructed using PHP, JavaScript, jQuery, AJAX and CSS, utilizing the model-view-controller (MVC) framework. The in-house developed bioinformatics tools implemented in NeisseraBase were developed using Python, Perl, BioPerl and R languages. Results. Currently, NeisseriaBase houses 603,500 Coding Sequences (CDSs), 16,071 RNAs and 13,119 tRNA genes from 227 Neisseria genomes. The database is equipped with interactive web interfaces. Incorporation of the JBrowse genome browser in the database enables fast and smooth browsing of Neisseria genomes. NeisseriaBase includes the standard BLAST program to facilitate homology searching, and for Virulence Factor Database (VFDB) specific homology searches, the VFDB BLAST is also incorporated into the database. In addition, NeisseriaBase is equipped with in-house designed tools such as the Pairwise Genome Comparison tool (PGC) for comparative genomic analysis and the Pathogenomics Profiling Tool (PathoProT) for the comparative pathogenomics analysis of Neisseria strains. Discussion. This user-friendly database not only provides access to a host of genomic resources on Neisseria but also enables high-quality comparative genome analysis, which is crucial for the expanding scientific community interested in Neisseria research. This database is freely available at http://neisseria.um.edu.my. PMID:27017950

Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease.

PubMed

Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

2014-01-01

We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. http://rged.wall-eva.net. © The Author(s) 2014. Published by Oxford University Press.

SoyFN: a knowledge database of soybean functional networks.

PubMed

Xu, Yungang; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Many databases for soybean genomic analysis have been built and made publicly available, but few of them contain knowledge specifically targeting the omics-level gene-gene, gene-microRNA (miRNA) and miRNA-miRNA interactions. Here, we present SoyFN, a knowledge database of soybean functional gene networks and miRNA functional networks. SoyFN provides user-friendly interfaces to retrieve, visualize, analyze and download the functional networks of soybean genes and miRNAs. In addition, it incorporates much information about KEGG pathways, gene ontology annotations and 3'-UTR sequences as well as many useful tools including SoySearch, ID mapping, Genome Browser, eFP Browser and promoter motif scan. SoyFN is a schema-free database that can be accessed as a Web service from any modern programming language using a simple Hypertext Transfer Protocol call. The Web site is implemented in Java, JavaScript, PHP, HTML and Apache, with all major browsers supported. We anticipate that this database will be useful for members of research communities both in soybean experimental science and bioinformatics. Database URL: http://nclab.hit.edu.cn/SoyFN.

Database resources of the National Center for Biotechnology Information

PubMed Central

2015-01-01

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (Bookshelf, PubMed Central (PMC) and PubReader); medical genetics (ClinVar, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen); genes and genomics (BioProject, BioSample, dbSNP, dbVar, Epigenomics, Gene, Gene Expression Omnibus (GEO), Genome, HomoloGene, the Map Viewer, Nucleotide, PopSet, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser, Trace Archive and UniGene); and proteins and chemicals (Biosystems, COBALT, the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB), Protein Clusters, Protein and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for many of these databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov. PMID:25398906

Database resources of the National Center for Biotechnology Information

PubMed Central

2016-01-01

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (PubMed Central (PMC), Bookshelf and PubReader), health (ClinVar, dbGaP, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen), genomes (BioProject, Assembly, Genome, BioSample, dbSNP, dbVar, Epigenomics, the Map Viewer, Nucleotide, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser and the Trace Archive), genes (Gene, Gene Expression Omnibus (GEO), HomoloGene, PopSet and UniGene), proteins (Protein, the Conserved Domain Database (CDD), COBALT, Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB) and Protein Clusters) and chemicals (Biosystems and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for most of these databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. PMID:26615191

Vγ9 and Vδ2 T cell antigen receptor genes and butyrophilin 3 (BTN3) emerged with placental mammals and are concomitantly preserved in selected species like alpaca (Vicugna pacos).

PubMed

Karunakaran, Mohindar M; Göbel, Thomas W; Starick, Lisa; Walter, Lutz; Herrmann, Thomas

2014-04-01

Human Vγ9Vδ2 T cells recognize phosphorylated products of isoprenoid metabolism (phosphoantigens) PAg with TCR comprising Vγ9JP γ-chains and Vδ2 δ-chains dependent on butyrophilin 3 (BTN3) expressed by antigen-presenting cells. They are massively activated in many infections and show anti-tumor activity and so far, they have been considered to exist only in higher primates. We performed a comprehensive analysis of databases and identified the three genes in species of both placental magnorders, but not in rodents. The common occurrence or loss of in silico translatable Vγ9, Vδ2, and BTN3 genes suggested their co-evolution based on a functional relationship. In the peripheral lymphocytes of alpaca (Vicugna pacos), characteristic Vγ9JP rearrangements and in-frame Vδ2 rearrangements were found and could be co-expressed in a TCR-negative mouse T cell hybridoma where they rescued CD3 expression and function. Finally, database sequence analysis of the extracellular domain of alpaca BTN3 revealed complete conservation of proposed PAg binding residues of human BTN3A1. In summary, we show emergence and preservation of Vγ9 and Vδ2 TCR genes with the gene of the putative antigen-presenting molecule BTN3 in placental mammals and lay the ground for analysis of alpaca as candidate for a first non-primate species to possess Vγ9Vδ2 T cells.

The 2014 Nucleic Acids Research Database Issue and an updated NAR online Molecular Biology Database Collection.

PubMed

Fernández-Suárez, Xosé M; Rigden, Daniel J; Galperin, Michael Y

2014-01-01

The 2014 Nucleic Acids Research Database Issue includes descriptions of 58 new molecular biology databases and recent updates to 123 databases previously featured in NAR or other journals. For convenience, the issue is now divided into eight sections that reflect major subject categories. Among the highlights of this issue are six databases of the transcription factor binding sites in various organisms and updates on such popular databases as CAZy, Database of Genomic Variants (DGV), dbGaP, DrugBank, KEGG, miRBase, Pfam, Reactome, SEED, TCDB and UniProt. There is a strong block of structural databases, which includes, among others, the new RNA Bricks database, updates on PDBe, PDBsum, ArchDB, Gene3D, ModBase, Nucleic Acid Database and the recently revived iPfam database. An update on the NCBI's MMDB describes VAST+, an improved tool for protein structure comparison. Two articles highlight the development of the Structural Classification of Proteins (SCOP) database: one describes SCOPe, which automates assignment of new structures to the existing SCOP hierarchy; the other one describes the first version of SCOP2, with its more flexible approach to classifying protein structures. This issue also includes a collection of articles on bacterial taxonomy and metagenomics, which includes updates on the List of Prokaryotic Names with Standing in Nomenclature (LPSN), Ribosomal Database Project (RDP), the Silva/LTP project and several new metagenomics resources. The NAR online Molecular Biology Database Collection, http://www.oxfordjournals.org/nar/database/c/, has been expanded to 1552 databases. The entire Database Issue is freely available online on the Nucleic Acids Research website (http://nar.oxfordjournals.org/).

Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics.

PubMed

Sakai, Hiroaki; Lee, Sung Shin; Tanaka, Tsuyoshi; Numa, Hisataka; Kim, Jungsok; Kawahara, Yoshihiro; Wakimoto, Hironobu; Yang, Ching-chia; Iwamoto, Masao; Abe, Takashi; Yamada, Yuko; Muto, Akira; Inokuchi, Hachiro; Ikemura, Toshimichi; Matsumoto, Takashi; Sasaki, Takuji; Itoh, Takeshi

2013-02-01

The Rice Annotation Project Database (RAP-DB, http://rapdb.dna.affrc.go.jp/) has been providing a comprehensive set of gene annotations for the genome sequence of rice, Oryza sativa (japonica group) cv. Nipponbare. Since the first release in 2005, RAP-DB has been updated several times along with the genome assembly updates. Here, we present our newest RAP-DB based on the latest genome assembly, Os-Nipponbare-Reference-IRGSP-1.0 (IRGSP-1.0), which was released in 2011. We detected 37,869 loci by mapping transcript and protein sequences of 150 monocot species. To provide plant researchers with highly reliable and up to date rice gene annotations, we have been incorporating literature-based manually curated data, and 1,626 loci currently incorporate literature-based annotation data, including commonly used gene names or gene symbols. Transcriptional activities are shown at the nucleotide level by mapping RNA-Seq reads derived from 27 samples. We also mapped the Illumina reads of a Japanese leading japonica cultivar, Koshihikari, and a Chinese indica cultivar, Guangluai-4, to the genome and show alignments together with the single nucleotide polymorphisms (SNPs) and gene functional annotations through a newly developed browser, Short-Read Assembly Browser (S-RAB). We have developed two satellite databases, Plant Gene Family Database (PGFD) and Integrative Database of Cereal Gene Phylogeny (IDCGP), which display gene family and homologous gene relationships among diverse plant species. RAP-DB and the satellite databases offer simple and user-friendly web interfaces, enabling plant and genome researchers to access the data easily and facilitating a broad range of plant research topics.

PlantGI: a database for searching gene indices in agricultural plants developed at NIAB, Korea

PubMed Central

Kim, Chang Kug; Choi, Ji Weon; Park, DongSuk; Kang, Man Jung; Seol, Young-Joo; Hyun, Do Yoon; Hahn, Jang Ho

2008-01-01

The Plant Gene Index (PlantGI) database is developed as a web-based search system with search capabilities for keywords to provide information on gene indices specifically for agricultural plants. The database contains specific Gene Index information for ten agricultural species, namely, rice, Chinese cabbage, wheat, maize, soybean, barley, mushroom, Arabidopsis, hot pepper and tomato. PlantGI differs from other Gene Index databases in being specific to agricultural plant species and thus complements services from similar other developments. The database includes options for interactive mining of EST CONTIGS and assembled EST data for user specific keyword queries. The current version of PlantGI contains a total of 34,000 EST CONTIGS data for rice (8488 records), wheat (8560 records), maize (4570 records), soybean (3726 records), barley (3417 records), Chinese cabbage (3602 records), tomato (1236 records), hot pepper (998 records), mushroom (130 records) and Arabidopsis (8 records). Availability The database is available for free at http://www.niab.go.kr/nabic/. PMID:18685722

Validation and discovery of genotype-phenotype associations in chronic diseases using linked data.

PubMed

Pathak, Jyotishman; Kiefer, Richard; Freimuth, Robert; Chute, Christopher

2012-01-01

This study investigates federated SPARQL queries over Linked Open Data (LOD) in the Semantic Web to validate existing, and potentially discover new genotype-phenotype associations from public datasets. In particular, we report our preliminary findings for identifying such associations for commonly occurring chronic diseases using the Online Mendelian Inheritance in Man (OMIM) and Database for SNPs (dbSNP) within the LOD knowledgebase and compare them with Gene Wiki for coverage and completeness. Our results indicate that Semantic Web technologies can play an important role for in-silico identification of novel disease-gene-SNP associations, although additional verification is required before such information can be applied and used effectively.

Genomic Comparison of the P-ATPase Gene Family in Four Cotton Species and Their Expression Patterns in Gossypium hirsutum.

PubMed

Chen, Wen; Si, Guo-Yang; Zhao, Gang; Abdullah, Muhammad; Guo, Ning; Li, Da-Hui; Sun, Xu; Cai, Yong-Ping; Lin, Yi; Gao, Jun-Shan

2018-05-05

Plant P-type H⁺-ATPase (P-ATPase) is a membrane protein existing in the plasma membrane that plays an important role in the transmembrane transport of plant cells. To understand the variety and quantity of P-ATPase proteins in different cotton species, we combined four databases from two diploid cotton species ( Gossypium raimondii and G. arboreum ) and two tetraploid cotton species ( G. hirsutum and G. barbadense ) to screen the P-ATPase gene family and resolved the evolutionary relationships between the former cotton species. We identified 53, 51, 99 and 98 P-ATPase genes from G. arboretum, G. raimondii , G. barbadense and G. hirsutum , respectively. The structural and phylogenetic analyses revealed that the gene structure was consistent between P-ATPase genes, with a close evolutionary relationship. The expression analysis of P-ATPase genes showed that many P-ATPase genes were highly expressed in various tissues and at different fiber developmental stages in G. hirsutum , suggesting that they have potential functions during growth and fiber development in cotton.

IntPath--an integrated pathway gene relationship database for model organisms and important pathogens.

PubMed

Zhou, Hufeng; Jin, Jingjing; Zhang, Haojun; Yi, Bo; Wozniak, Michal; Wong, Limsoon

2012-01-01

Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases (KEGG, WikiPathways, BioCyc, etc). We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms--S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data (e.g., the gene ID errors in WikiPathways and relationship errors in KEGG). We turn complicated and incompatible xml data formats and inconsistent gene and gene relationship representations from different source databases into normalized and unified pathway-gene and pathway-gene pair relationships neatly recorded in simple tab-delimited text format and MySQL tables, which facilitates convenient automatic computation and large-scale referencing in many related studies. IntPath data can be downloaded in text format or MySQL dump. IntPath data can also be retrieved and analyzed conveniently through web service by local programs or through web interface by mouse clicks. Several useful analysis tools are also provided in IntPath. We have overcome in IntPath the issues of compatibility, consistency, and comprehensiveness that often hamper effective use of pathway databases. We have included four organisms in the current release of IntPath. Our methodology and programs described in this work can be easily applied to other organisms; and we will include more model organisms and important pathogens in future releases of IntPath. IntPath maintains regular updates and is freely available at http://compbio.ddns.comp.nus.edu.sg:8080/IntPath.

The Essential Genome of Escherichia coli K-12

PubMed Central

2018-01-01

ABSTRACT Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in Escherichia coli K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry. PMID:29463657

tRNAscan-SE On-line: integrating search and context for analysis of transfer RNA genes.

PubMed

Lowe, Todd M; Chan, Patricia P

2016-07-08

High-throughput genome sequencing continues to grow the need for rapid, accurate genome annotation and tRNA genes constitute the largest family of essential, ever-present non-coding RNA genes. Newly developed tRNAscan-SE 2.0 has advanced the state-of-the-art methodology in tRNA gene detection and functional prediction, captured by rich new content of the companion Genomic tRNA Database. Previously, web-server tRNA detection was isolated from knowledge of existing tRNAs and their annotation. In this update of the tRNAscan-SE On-line resource, we tie together improvements in tRNA classification with greatly enhanced biological context via dynamically generated links between web server search results, the most relevant genes in the GtRNAdb and interactive, rich genome context provided by UCSC genome browsers. The tRNAscan-SE On-line web server can be accessed at http://trna.ucsc.edu/tRNAscan-SE/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

TubercuList--10 years after.

PubMed

Lew, Jocelyne M; Kapopoulou, Adamandia; Jones, Louis M; Cole, Stewart T

2011-01-01

TubercuList (http://tuberculist.epfl.ch/), the relational database that presents genome-derived information about H37Rv, the paradigm strain of Mycobacterium tuberculosis, has been active for ten years and now presents its twentieth release. Here, we describe some of the recent changes that have resulted from manual annotation with information from the scientific literature. Through manual curation, TubercuList strives to provide current gene-based information and is thus distinguished from other online sources of genome sequence data for M. tuberculosis. New, mostly small, genes have been discovered and the coordinates of some existing coding sequences have been changed when bioinformatics or experimental data suggest that this is required. Nucleotides that are polymorphic between different sources of H37Rv are annotated and gene essentiality data have been updated. A host of functional information has been gleaned from the literature and many new activities of proteins and RNAs have been included. To facilitate basic and translational research, TubercuList also provides links to other specialized databases that present diverse datasets such as 3D-structures, expression profiles, drug development criteria and drug resistance information, in addition to direct access to PubMed articles pertinent to particular genes. TubercuList has been and remains a highly valuable tool for the tuberculosis research community with >75,000 visitors per month. Copyright © 2010 Elsevier Ltd. All rights reserved.

Aspergillus flavus Blast2GO gene ontology database: elevated growth temperature alters amino acid metabolism

USDA-ARS?s Scientific Manuscript database

The availability of a representative gene ontology (GO) database is a prerequisite for a successful functional genomics study. Using online Blast2GO resources we constructed a GO database of Aspergillus flavus. Of the predicted total 13,485 A. flavus genes 8,987 were annotated with GO terms. The mea...

Genic insights from integrated human proteomics in GeneCards.

PubMed

Fishilevich, Simon; Zimmerman, Shahar; Kohn, Asher; Iny Stein, Tsippi; Olender, Tsviya; Kolker, Eugene; Safran, Marilyn; Lancet, Doron

2016-01-01

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/. © The Author(s) 2016. Published by Oxford University Press.

OGRO: The Overview of functionally characterized Genes in Rice online database.

PubMed

Yamamoto, Eiji; Yonemaru, Jun-Ichi; Yamamoto, Toshio; Yano, Masahiro

2012-12-01

The high-quality sequence information and rich bioinformatics tools available for rice have contributed to remarkable advances in functional genomics. To facilitate the application of gene function information to the study of natural variation in rice, we comprehensively searched for articles related to rice functional genomics and extracted information on functionally characterized genes. As of 31 March 2012, 702 functionally characterized genes were annotated. This number represents about 1.6% of the predicted loci in the Rice Annotation Project Database. The compiled gene information is organized to facilitate direct comparisons with quantitative trait locus (QTL) information in the Q-TARO database. Comparison of genomic locations between functionally characterized genes and the QTLs revealed that QTL clusters were often co-localized with high-density gene regions, and that the genes associated with the QTLs in these clusters were different genes, suggesting that these QTL clusters are likely to be explained by tightly linked but distinct genes. Information on the functionally characterized genes compiled during this study is now available in the O verview of Functionally Characterized G enes in R ice O nline database (OGRO) on the Q-TARO website ( http://qtaro.abr.affrc.go.jp/ogro ). The database has two interfaces: a table containing gene information, and a genome viewer that allows users to compare the locations of QTLs and functionally characterized genes. OGRO on Q-TARO will facilitate a candidate-gene approach to identifying the genes responsible for QTLs. Because the QTL descriptions in Q-TARO contain information on agronomic traits, such comparisons will also facilitate the annotation of functionally characterized genes in terms of their effects on traits important for rice breeding. The increasing amount of information on rice gene function being generated from mutant panels and other types of studies will make the OGRO database even more valuable in the future.

Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases

PubMed Central

2005-01-01

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB. PMID:16046824

Entamoeba histolytica: construction and applications of subgenomic databases.

PubMed

Hofer, Margit; Duchêne, Michael

2005-07-01

Knowledge about the influence of environmental stress such as the action of chemotherapeutic agents on gene expression in Entamoeba histolytica is limited. We plan to use oligonucleotide microarray hybridization to approach these questions. As the basis for our array, sequence data from the genome project carried out by the Institute for Genomic Research (TIGR) and the Sanger Institute were used to annotate parts of the parasite genome. Three subgenomic databases containing enzymes, cytoskeleton genes, and stress genes were compiled with the help of the ExPASy proteomics website and the BLAST servers at the two genome project sites. The known sequences from reference species, mostly human and Escherichia coli, were searched against TIGR and Sanger E. histolytica sequence contigs and the homologs were copied into a Microsoft Access database. In a similar way, two additional databases of cytoskeletal genes and stress genes were generated. Metabolic pathways could be assembled from our enzyme database, but sometimes they were incomplete as is the case for the sterol biosynthesis pathway. The raw databases contained a significant number of duplicate entries which were merged to obtain curated non-redundant databases. This procedure revealed that some E. histolytica genes may have several putative functions. Representative examples such as the case of the delta-aminolevulinate synthase/serine palmitoyltransferase are discussed.

Transcriptome database resource and gene expression atlas for the rose

PubMed Central

2012-01-01

Background For centuries roses have been selected based on a number of traits. Little information exists on the genetic and molecular basis that contributes to these traits, mainly because information on expressed genes for this economically important ornamental plant is scarce. Results Here, we used a combination of Illumina and 454 sequencing technologies to generate information on Rosa sp. transcripts using RNA from various tissues and in response to biotic and abiotic stresses. A total of 80714 transcript clusters were identified and 76611 peptides have been predicted among which 20997 have been clustered into 13900 protein families. BLASTp hits in closely related Rosaceae species revealed that about half of the predicted peptides in the strawberry and peach genomes have orthologs in Rosa dataset. Digital expression was obtained using RNA samples from organs at different development stages and under different stress conditions. qPCR validated the digital expression data for a selection of 23 genes with high or low expression levels. Comparative gene expression analyses between the different tissues and organs allowed the identification of clusters that are highly enriched in given tissues or under particular conditions, demonstrating the usefulness of the digital gene expression analysis. A web interface ROSAseq was created that allows data interrogation by BLAST, subsequent analysis of DNA clusters and access to thorough transcript annotation including best BLAST matches on Fragaria vesca, Prunus persica and Arabidopsis. The rose peptides dataset was used to create the ROSAcyc resource pathway database that allows access to the putative genes and enzymatic pathways. Conclusions The study provides useful information on Rosa expressed genes, with thorough annotation and an overview of expression patterns for transcripts with good accuracy. PMID:23164410

miRnalyze: an interactive database linking tool to unlock intuitive microRNA regulation of cell signaling pathways

PubMed Central

Subhra Das, Sankha; James, Mithun; Paul, Sandip

2017-01-01

Abstract The various pathophysiological processes occurring in living systems are known to be orchestrated by delicate interplays and cross-talks between different genes and their regulators. Among the various regulators of genes, there is a class of small non-coding RNA molecules known as microRNAs. Although, the relative simplicity of miRNAs and their ability to modulate cellular processes make them attractive therapeutic candidates, their presence in large numbers make it challenging for experimental researchers to interpret the intricacies of the molecular processes they regulate. Most of the existing bioinformatic tools fail to address these challenges. Here, we present a new web resource ‘miRnalyze’ that has been specifically designed to directly identify the putative regulation of cell signaling pathways by miRNAs. The tool integrates miRNA-target predictions with signaling cascade members by utilizing TargetScanHuman 7.1 miRNA-target prediction tool and the KEGG pathway database, and thus provides researchers with in-depth insights into modulation of signal transduction pathways by miRNAs. miRnalyze is capable of identifying common miRNAs targeting more than one gene in the same signaling pathway—a feature that further increases the probability of modulating the pathway and downstream reactions when using miRNA modulators. Additionally, miRnalyze can sort miRNAs according to the seed-match types and TargetScan Context ++ score, thus providing a hierarchical list of most valuable miRNAs. Furthermore, in order to provide users with comprehensive information regarding miRNAs, genes and pathways, miRnalyze also links to expression data of miRNAs (miRmine) and genes (TiGER) and proteome abundance (PaxDb) data. To validate the capability of the tool, we have documented the correlation of miRnalyze’s prediction with experimental confirmation studies. Database URL: http://www.mirnalyze.in PMID:28365733

Cry-Bt identifier: a biological database for PCR detection of Cry genes present in transgenic plants.

PubMed

Singh, Vinay Kumar; Ambwani, Sonu; Marla, Soma; Kumar, Anil

2009-10-23

We describe the development of a user friendly tool that would assist in the retrieval of information relating to Cry genes in transgenic crops. The tool also helps in detection of transformed Cry genes from Bacillus thuringiensis present in transgenic plants by providing suitable designed primers for PCR identification of these genes. The tool designed based on relational database model enables easy retrieval of information from the database with simple user queries. The tool also enables users to access related information about Cry genes present in various databases by interacting with different sources (nucleotide sequences, protein sequence, sequence comparison tools, published literature, conserved domains, evolutionary and structural data). http://insilicogenomics.in/Cry-btIdentifier/welcome.html.

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.

PubMed

Miannay, Bertrand; Minvielle, Stéphane; Magrangeas, Florence; Guziolowski, Carito

2018-03-21

The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.

Mimvec: a deep learning approach for analyzing the human phenome.

PubMed

Gan, Mingxin; Li, Wenran; Zeng, Wanwen; Wang, Xiaojian; Jiang, Rui

2017-09-21

The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing. We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones. Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies.

PubMed

Yang, Tsun-Po; Beazley, Claude; Montgomery, Stephen B; Dimas, Antigone S; Gutierrez-Arcelus, Maria; Stranger, Barbara E; Deloukas, Panos; Dermitzakis, Emmanouil T

2010-10-01

Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols. http://www.sanger.ac.uk/resources/software/genevar.

Drug-Path: a database for drug-induced pathways.

PubMed

Zeng, Hui; Qiu, Chengxiang; Cui, Qinghua

2015-01-01

Some databases for drug-associated pathways have been built and are publicly available. However, the pathways curated in most of these databases are drug-action or drug-metabolism pathways. In recent years, high-throughput technologies such as microarray and RNA-sequencing have produced lots of drug-induced gene expression profiles. Interestingly, drug-induced gene expression profile frequently show distinct patterns, indicating that drugs normally induce the activation or repression of distinct pathways. Therefore, these pathways contribute to study the mechanisms of drugs and drug-repurposing. Here, we present Drug-Path, a database of drug-induced pathways, which was generated by KEGG pathway enrichment analysis for drug-induced upregulated genes and downregulated genes based on drug-induced gene expression datasets in Connectivity Map. Drug-Path provides user-friendly interfaces to retrieve, visualize and download the drug-induced pathway data in the database. In addition, the genes deregulated by a given drug are highlighted in the pathways. All data were organized using SQLite. The web site was implemented using Django, a Python web framework. Finally, we believe that this database will be useful for related researches. © The Author(s) 2015. Published by Oxford University Press.

Integrative Functional Genomics for Systems Genetics in GeneWeaver.org.

PubMed

Bubier, Jason A; Langston, Michael A; Baker, Erich J; Chesler, Elissa J

2017-01-01

The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.

Production of individualized V gene databases reveals high levels of immunoglobulin genetic diversity

NASA Astrophysics Data System (ADS)

Corcoran, Martin M.; Phad, Ganesh E.; Bernat, Néstor Vázquez; Stahl-Hennig, Christiane; Sumida, Noriyuki; Persson, Mats A. A.; Martin, Marcel; Hedestam, Gunilla B. Karlsson

2016-12-01

Comprehensive knowledge of immunoglobulin genetics is required to advance our understanding of B cell biology. Validated immunoglobulin variable (V) gene databases are close to completion only for human and mouse. We present a novel computational approach, IgDiscover, that identifies germline V genes from expressed repertoires to a specificity of 100%. IgDiscover uses a cluster identification process to produce candidate sequences that, once filtered, results in individualized germline V gene databases. IgDiscover was tested in multiple species, validated by genomic cloning and cross library comparisons and produces comprehensive gene databases even where limited genomic sequence is available. IgDiscover analysis of the allelic content of the Indian and Chinese-origin rhesus macaques reveals high levels of immunoglobulin gene diversity in this species. Further, we describe a novel human IGHV3-21 allele and confirm significant gene differences between Balb/c and C57BL6 mouse strains, demonstrating the power of IgDiscover as a germline V gene discovery tool.

Production of individualized V gene databases reveals high levels of immunoglobulin genetic diversity

PubMed Central

Corcoran, Martin M.; Phad, Ganesh E.; Bernat, Néstor Vázquez; Stahl-Hennig, Christiane; Sumida, Noriyuki; Persson, Mats A.A.; Martin, Marcel; Hedestam, Gunilla B. Karlsson

2016-01-01

Comprehensive knowledge of immunoglobulin genetics is required to advance our understanding of B cell biology. Validated immunoglobulin variable (V) gene databases are close to completion only for human and mouse. We present a novel computational approach, IgDiscover, that identifies germline V genes from expressed repertoires to a specificity of 100%. IgDiscover uses a cluster identification process to produce candidate sequences that, once filtered, results in individualized germline V gene databases. IgDiscover was tested in multiple species, validated by genomic cloning and cross library comparisons and produces comprehensive gene databases even where limited genomic sequence is available. IgDiscover analysis of the allelic content of the Indian and Chinese-origin rhesus macaques reveals high levels of immunoglobulin gene diversity in this species. Further, we describe a novel human IGHV3-21 allele and confirm significant gene differences between Balb/c and C57BL6 mouse strains, demonstrating the power of IgDiscover as a germline V gene discovery tool. PMID:27995928

The Molecular Signatures Database (MSigDB) hallmark gene set collection.

PubMed

Liberzon, Arthur; Birger, Chet; Thorvaldsdóttir, Helga; Ghandi, Mahmoud; Mesirov, Jill P; Tamayo, Pablo

2015-12-23

The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

Development of a gene expression database and related analysis programs for evaluation of anticancer compounds.

PubMed

Ushijima, Masaru; Mashima, Tetsuo; Tomida, Akihiro; Dan, Shingo; Saito, Sakae; Furuno, Aki; Tsukahara, Satomi; Seimiya, Hiroyuki; Yamori, Takao; Matsuura, Masaaki

2013-03-01

Genome-wide transcriptional expression analysis is a powerful strategy for characterizing the biological activity of anticancer compounds. It is often instructive to identify gene sets involved in the activity of a given drug compound for comparison with different compounds. Currently, however, there is no comprehensive gene expression database and related application system that is; (i) specialized in anticancer agents; (ii) easy to use; and (iii) open to the public. To develop a public gene expression database of antitumor agents, we first examined gene expression profiles in human cancer cells after exposure to 35 compounds including 25 clinically used anticancer agents. Gene signatures were extracted that were classified as upregulated or downregulated after exposure to the drug. Hierarchical clustering showed that drugs with similar mechanisms of action, such as genotoxic drugs, were clustered. Connectivity map analysis further revealed that our gene signature data reflected modes of action of the respective agents. Together with the database, we developed analysis programs that calculate scores for ranking changes in gene expression and for searching statistically significant pathways from the Kyoto Encyclopedia of Genes and Genomes database in order to analyze the datasets more easily. Our database and the analysis programs are available online at our website (http://scads.jfcr.or.jp/db/cs/). Using these systems, we successfully showed that proteasome inhibitors are selectively classified as endoplasmic reticulum stress inducers and induce atypical endoplasmic reticulum stress. Thus, our public access database and related analysis programs constitute a set of efficient tools to evaluate the mode of action of novel compounds and identify promising anticancer lead compounds. © 2012 Japanese Cancer Association.

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

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.

Oral cancer databases: A comprehensive review.

PubMed

Sarode, Gargi S; Sarode, Sachin C; Maniyar, Nikunj; Anand, Rahul; Patil, Shankargouda

2017-11-29

Cancer database is a systematic collection and analysis of information on various human cancers at genomic and molecular level that can be utilized to understand various steps in carcinogenesis and for therapeutic advancement in cancer field. Oral cancer is one of the leading causes of morbidity and mortality all over the world. The current research efforts in this field are aimed at cancer etiology and therapy. Advanced genomic technologies including microarrays, proteomics, transcrpitomics, and gene sequencing development have culminated in generation of extensive data and subjection of several genes and microRNAs that are distinctively expressed and this information is stored in the form of various databases. Extensive data from various resources have brought the need for collaboration and data sharing to make effective use of this new knowledge. The current review provides comprehensive information of various publicly accessible databases that contain information pertinent to oral squamous cell carcinoma (OSCC) and databases designed exclusively for OSCC. The databases discussed in this paper are Protein-Coding Gene Databases and microRNA Databases. This paper also describes gene overlap in various databases, which will help researchers to reduce redundancy and focus on only those genes, which are common to more than one databases. We hope such introduction will promote awareness and facilitate the usage of these resources in the cancer research community, and researchers can explore the molecular mechanisms involved in the development of cancer, which can help in subsequent crafting of therapeutic strategies. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Integrative genomic analyses of the histamine H1 receptor and its role in cancer prediction.

PubMed

Wang, Minghai; Wei, Xiaolong; Shi, Lianghui; Chen, Bin; Zhao, Guohai; Yang, Haiwei

2014-04-01

The human histamine receptor H1 (HRH1) gene is located on chromosome 3p25 and encodes for a 487 amino acid G protein-coupled receptor (GPCR) with a long third intracellular loop (IL3). The HRH1 predominantly couples to Gαq/11 proteins, leading to the activation of phospholipase C (PLC) and subsequent release of the second messengers inositol trisphosphate (IP3) and diacylglycerol (DAG) followed by the activation of PKC and the release of [Ca2+]i. In the present study, we identified HRH1 genes from 14 vertebrate genomes and found that HRH1 exists in all types of vertebrates including fish, amphibians, birds and mammals. We identified 88 SNPs including 4 available alleles disrupting an existing exonic splicing enhancer and 84 SNPs causing missense mutation, which may impact the effect of histamine on the HRH1 protein. We found that the human HRH1 gene was expressed in many tissues or organs, and predominant expression of HRH1 was shown in the bone marrow, whole blood, lymph node, thymus, brain, cerebellum, retina, spinal cord, heart, smooth muscle, skeletal muscle, small intestine, colon, adipocytes, kidney, liver, lung, pancreas, thyroid salivary gland, skin, ovary, uterus, placenta, prostate and testis. When searched in the PrognoScan database, human HRH1 was also found to be expressed in bladder cancer, blood cancer, brain cancer, breast cancer, colorectal cancer, eye cancer, head and neck cancer, lung cancer, ovarian cancer, skin cancer and soft tissue cancer tissues. The relationship between the expression of HRH1 and prognosis was found to vary in different types of cancers, even in the same cancer from different databases. This implies that the function of HRH1 in these tumors may be multidimensional. GR, STAT5A and c-Myb regulatory transcription factor binding sites were identified in the HRH1 gene upstream (promoter) region, which may be involved in the effect of HRH1 in tumors.

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database

PubMed Central

Davis, Allan Peter; Wiegers, Thomas C.; King, Benjamin L.; Wiegers, Jolene; Grondin, Cynthia J.; Sciaky, Daniela; Johnson, Robin J.; Mattingly, Carolyn J.

2016-01-01

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD’s gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects. PMID:27171405

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

PubMed

Davis, Allan Peter; Wiegers, Thomas C; King, Benjamin L; Wiegers, Jolene; Grondin, Cynthia J; Sciaky, Daniela; Johnson, Robin J; Mattingly, Carolyn J

2016-01-01

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD's gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects.

MultitaskProtDB: a database of multitasking proteins.

PubMed

Hernández, Sergio; Ferragut, Gabriela; Amela, Isaac; Perez-Pons, JosepAntoni; Piñol, Jaume; Mozo-Villarias, Angel; Cedano, Juan; Querol, Enrique

2014-01-01

We have compiled MultitaskProtDB, available online at http://wallace.uab.es/multitask, to provide a repository where the many multitasking proteins found in the literature can be stored. Multitasking or moonlighting is the capability of some proteins to execute two or more biological functions. Usually, multitasking proteins are experimentally revealed by serendipity. This ability of proteins to perform multitasking functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Even so, the study of this phenomenon is complex because, among other things, there is no database of moonlighting proteins. The existence of such a tool facilitates the collection and dissemination of these important data. This work reports the database, MultitaskProtDB, which is designed as a friendly user web page containing >288 multitasking proteins with their NCBI and UniProt accession numbers, canonical and additional biological functions, monomeric/oligomeric states, PDB codes when available and bibliographic references. This database also serves to gain insight into some characteristics of multitasking proteins such as frequencies of the different pairs of functions, phylogenetic conservation and so forth.

Human Variome Project Quality Assessment Criteria for Variation Databases.

PubMed

Vihinen, Mauno; Hancock, John M; Maglott, Donna R; Landrum, Melissa J; Schaafsma, Gerard C P; Taschner, Peter

2016-06-01

Numerous databases containing information about DNA, RNA, and protein variations are available. Gene-specific variant databases (locus-specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research, and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. This report elaborates on the developed quality criteria and how implementation of the quality scheme can be achieved. Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance. © 2016 WILEY PERIODICALS, INC.

GRBase, a new gene regulation data base available by anonymous ftp.

PubMed Central

Collier, B; Danielsen, M

1994-01-01

The Gene Regulation Database (GRBase) is a compendium of information on the structure and function of proteins involved in the control of gene expression in eukaryotes. These proteins include transcription factors, proteins involved in signal transduction, and receptors. The database can be obtained by FTP in Filemaker Pro, text, and postscript formats. The database will be expanded in the coming year to include reviews on families of proteins involved in gene regulation and to allow online searching. PMID:7937071

IntPath--an integrated pathway gene relationship database for model organisms and important pathogens

PubMed Central

2012-01-01

Background Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. Results In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases (KEGG, WikiPathways, BioCyc, etc). We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms--S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data (e.g., the gene ID errors in WikiPathways and relationship errors in KEGG). We turn complicated and incompatible xml data formats and inconsistent gene and gene relationship representations from different source databases into normalized and unified pathway-gene and pathway-gene pair relationships neatly recorded in simple tab-delimited text format and MySQL tables, which facilitates convenient automatic computation and large-scale referencing in many related studies. IntPath data can be downloaded in text format or MySQL dump. IntPath data can also be retrieved and analyzed conveniently through web service by local programs or through web interface by mouse clicks. Several useful analysis tools are also provided in IntPath. Conclusions We have overcome in IntPath the issues of compatibility, consistency, and comprehensiveness that often hamper effective use of pathway databases. We have included four organisms in the current release of IntPath. Our methodology and programs described in this work can be easily applied to other organisms; and we will include more model organisms and important pathogens in future releases of IntPath. IntPath maintains regular updates and is freely available at http://compbio.ddns.comp.nus.edu.sg:8080/IntPath. PMID:23282057

Implementation of GenePattern within the Stanford Microarray Database.

PubMed

Hubble, Jeremy; Demeter, Janos; Jin, Heng; Mao, Maria; Nitzberg, Michael; Reddy, T B K; Wymore, Farrell; Zachariah, Zachariah K; Sherlock, Gavin; Ball, Catherine A

2009-01-01

Hundreds of researchers across the world use the Stanford Microarray Database (SMD; http://smd.stanford.edu/) to store, annotate, view, analyze and share microarray data. In addition to providing registered users at Stanford access to their own data, SMD also provides access to public data, and tools with which to analyze those data, to any public user anywhere in the world. Previously, the addition of new microarray data analysis tools to SMD has been limited by available engineering resources, and in addition, the existing suite of tools did not provide a simple way to design, execute and share analysis pipelines, or to document such pipelines for the purposes of publication. To address this, we have incorporated the GenePattern software package directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD. In this article, we describe our implementation of the GenePattern microarray analysis software package into the SMD code base. This extension is available with the SMD source code that is fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD with an enriched data analysis capability.

Integrating metagenomic and amplicon databases to resolve the phylogenetic and ecological diversity of the Chlamydiae

PubMed Central

Lagkouvardos, Ilias; Weinmaier, Thomas; Lauro, Federico M; Cavicchioli, Ricardo; Rattei, Thomas; Horn, Matthias

2014-01-01

In the era of metagenomics and amplicon sequencing, comprehensive analyses of available sequence data remain a challenge. Here we describe an approach exploiting metagenomic and amplicon data sets from public databases to elucidate phylogenetic diversity of defined microbial taxa. We investigated the phylum Chlamydiae whose known members are obligate intracellular bacteria that represent important pathogens of humans and animals, as well as symbionts of protists. Despite their medical relevance, our knowledge about chlamydial diversity is still scarce. Most of the nine known families are represented by only a few isolates, while previous clone library-based surveys suggested the existence of yet uncharacterized members of this phylum. Here we identified more than 22 000 high quality, non-redundant chlamydial 16S rRNA gene sequences in diverse databases, as well as 1900 putative chlamydial protein-encoding genes. Even when applying the most conservative approach, clustering of chlamydial 16S rRNA gene sequences into operational taxonomic units revealed an unexpectedly high species, genus and family-level diversity within the Chlamydiae, including 181 putative families. These in silico findings were verified experimentally in one Antarctic sample, which contained a high diversity of novel Chlamydiae. In our analysis, the Rhabdochlamydiaceae, whose known members infect arthropods, represents the most diverse and species-rich chlamydial family, followed by the protist-associated Parachlamydiaceae, and a putative new family (PCF8) with unknown host specificity. Available information on the origin of metagenomic samples indicated that marine environments contain the majority of the newly discovered chlamydial lineages, highlighting this environment as an important chlamydial reservoir. PMID:23949660

WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

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

Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian

With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don’t exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomicmore » data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.« less

WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

DOE PAGES

Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian; ...

2017-03-06

With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don’t exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomicmore » data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.« less

The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

PubMed

Hermjakob, Henning; Montecchi-Palazzi, Luisa; Bader, Gary; Wojcik, Jérôme; Salwinski, Lukasz; Ceol, Arnaud; Moore, Susan; Orchard, Sandra; Sarkans, Ugis; von Mering, Christian; Roechert, Bernd; Poux, Sylvain; Jung, Eva; Mersch, Henning; Kersey, Paul; Lappe, Michael; Li, Yixue; Zeng, Rong; Rana, Debashis; Nikolski, Macha; Husi, Holger; Brun, Christine; Shanker, K; Grant, Seth G N; Sander, Chris; Bork, Peer; Zhu, Weimin; Pandey, Akhilesh; Brazma, Alvis; Jacq, Bernard; Vidal, Marc; Sherman, David; Legrain, Pierre; Cesareni, Gianni; Xenarios, Ioannis; Eisenberg, David; Steipe, Boris; Hogue, Chris; Apweiler, Rolf

2004-02-01

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).

SNPversity: a web-based tool for visualizing diversity

PubMed Central

Schott, David A; Vinnakota, Abhinav G; Portwood, John L; Andorf, Carson M

2018-01-01

Abstract Many stand-alone desktop software suites exist to visualize single nucleotide polymorphism (SNP) diversity, but web-based software that can be easily implemented and used for biological databases is absent. SNPversity was created to answer this need by building an open-source visualization tool that can be implemented on a Unix-like machine and served through a web browser that can be accessible worldwide. SNPversity consists of a HDF5 database back-end for SNPs, a data exchange layer powered by TASSEL libraries that represent data in JSON format, and an interface layer using PHP to visualize SNP information. SNPversity displays data in real-time through a web browser in grids that are color-coded according to a given SNP’s allelic status and mutational state. SNPversity is currently available at MaizeGDB, the maize community’s database, and will be soon available at GrainGenes, the clade-oriented database for Triticeae and Avena species, including wheat, barley, rye, and oat. The code and documentation are uploaded onto github, and they are freely available to the public. We expect that the tool will be highly useful for other biological databases with a similar need to display SNP diversity through their web interfaces. Database URL: https://www.maizegdb.org/snpversity PMID:29688387

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events.

PubMed

Korla, Praveen Kumar; Cheng, Jack; Huang, Chien-Hung; Tsai, Jeffrey J P; Liu, Yu-Hsuan; Kurubanjerdjit, Nilubon; Hsieh, Wen-Tsong; Chen, Huey-Yi; Ng, Ka-Lok

2015-01-01

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain-domain interactions, protein-protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist's mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop 'novel' therapeutic approaches. Database URL: http://ppi.bioinfo.asia.edu.tw/FARE-CAFE. © The Author(s) 2015. Published by Oxford University Press.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events

PubMed Central

Korla, Praveen Kumar; Cheng, Jack; Huang, Chien-Hung; Tsai, Jeffrey J. P.; Liu, Yu-Hsuan; Kurubanjerdjit, Nilubon; Hsieh, Wen-Tsong; Chen, Huey-Yi; Ng, Ka-Lok

2015-01-01

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist’s mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop ‘novel’ therapeutic approaches. Database URL: http://ppi.bioinfo.asia.edu.tw/FARE-CAFE PMID:26384373

An Assessment of Database-Validated microRNA Target Genes in Normal Colonic Mucosa: Implications for Pathway Analysis.

PubMed

Slattery, Martha L; Herrick, Jennifer S; Stevens, John R; Wolff, Roger K; Mullany, Lila E

2017-01-01

Determination of functional pathways regulated by microRNAs (miRNAs), while an essential step in developing therapeutics, is challenging. Some miRNAs have been studied extensively; others have limited information. In this study, we focus on 254 miRNAs previously identified as being associated with colorectal cancer and their database-identified validated target genes. We use RNA-Seq data to evaluate messenger RNA (mRNA) expression for 157 subjects who also had miRNA expression data. In the replication phase of the study, we replicated associations between 254 miRNAs associated with colorectal cancer and mRNA expression of database-identified target genes in normal colonic mucosa. In the discovery phase of the study, we evaluated expression of 18 miR-NAs (those with 20 or fewer database-identified target genes along with miR-21-5p, miR-215-5p, and miR-124-3p which have more than 500 database-identified target genes) with expression of 17 434 mRNAs to identify new targets in colon tissue. Seed region matches between miRNA and newly identified targeted mRNA were used to help determine direct miRNA-mRNA associations. From the replication of the 121 miRNAs that had at least 1 database-identified target gene using mRNA expression methods, 97.9% were expressed in normal colonic mucosa. Of the 8622 target miRNA-mRNA associations identified in the database, 2658 (30.2%) were associated with gene expression in normal colonic mucosa after adjusting for multiple comparisons. Of the 133 miRNAs with database-identified target genes by non-mRNA expression methods, 97.2% were expressed in normal colonic mucosa. After adjustment for multiple comparisons, 2416 miRNA-mRNA associations remained significant (19.8%). Results from the discovery phase based on detailed examination of 18 miRNAs identified more than 80 000 miRNA-mRNA associations that had not previously linked to the miRNA. Of these miRNA-mRNA associations, 15.6% and 14.8% had seed matches for CRCh38 and CRCh37, respectively. Our data suggest that miRNA target gene databases are incomplete; pathways derived from these databases have similar deficiencies. Although we know a lot about several miRNAs, little is known about other miRNAs in terms of their targeted genes. We encourage others to use their data to continue to further identify and validate miRNA-targeted genes.

Gene: a gene-centered information resource at NCBI.

PubMed

Brown, Garth R; Hem, Vichet; Katz, Kenneth S; Ovetsky, Michael; Wallin, Craig; Ermolaeva, Olga; Tolstoy, Igor; Tatusova, Tatiana; Pruitt, Kim D; Maglott, Donna R; Murphy, Terence D

2015-01-01

The National Center for Biotechnology Information's (NCBI) Gene database (www.ncbi.nlm.nih.gov/gene) integrates gene-specific information from multiple data sources. NCBI Reference Sequence (RefSeq) genomes for viruses, prokaryotes and eukaryotes are the primary foundation for Gene records in that they form the critical association between sequence and a tracked gene upon which additional functional and descriptive content is anchored. Additional content is integrated based on the genomic location and RefSeq transcript and protein sequence data. The content of a Gene record represents the integration of curation and automated processing from RefSeq, collaborating model organism databases, consortia such as Gene Ontology, and other databases within NCBI. Records in Gene are assigned unique, tracked integers as identifiers. The content (citations, nomenclature, genomic location, gene products and their attributes, phenotypes, sequences, interactions, variation details, maps, expression, homologs, protein domains and external databases) is available via interactive browsing through NCBI's Entrez system, via NCBI's Entrez programming utilities (E-Utilities and Entrez Direct) and for bulk transfer by FTP. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

In silico analysis of cacao (Theobroma cacao L.) genes that involved in pathogen and disease responses

NASA Astrophysics Data System (ADS)

Agung, Muhammad Budi; Budiarsa, I. Made; Suwastika, I. Nengah

2017-02-01

Cocoa bean is one of the main commodities from Indonesia for the world, which still have problem regarding yield degradation due to pathogens and disease attack. Developing robust cacao plant that genetically resistant to pathogen and disease attack is an ideal solution in over taking on this problem. The aim of this study was to identify Theobroma cacao genes on database of cacao genome that homolog to response genes of pathogen and disease attack in other plant, through in silico analysis. Basic information survey and gene identification were performed in GenBank and The Arabidopsis Information Resource database. The In silico analysis contains protein BLAST, homology test of each gene's protein candidates, and identification of homologue gene in Cacao Genome Database using data source "Theobroma cacao cv. Matina 1-6 v1.1" genome. Identification found that Thecc1EG011959t1 (EDS1), Thecc1EG006803t1 (EDS5), Thecc1EG013842t1 (ICS1), and Thecc1EG015614t1 (BG_PPAP) gene of Cacao Genome Database were Theobroma cacao genes that homolog to plant's resistance genes which highly possible to have similar functions of each gene's homologue gene.

IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data.

PubMed

Mallik, Saurav; Sen, Sagnik; Maulik, Ujjwal

2016-07-15

Involvement of intrinsically disordered proteins (IDPs) with various dreadful diseases like cancer is an interesting research topic. In order to gain novel insights into the regulation of IDPs, in this article, we perform a transcriptomic analysis of mRNAs (genes) for transcripts encoding IDPs on a human multi-omics prostate carcinoma dataset having both gene expression and methylation data. In this regard, firstly the genes that consist of both the expression and methylation data, and that are corresponding to the cancer-related prostate-tissue-specific disordered proteins of MobiDb database, are selected. We apply standard t-test for determining differentially expressed genes as well as differentially methylated genes. A network having these genes and their targeter miRNAs from Diana Tarbase v7.0 database and corresponding Transcription Factors from TRANSFAC and ITFP databases, is then built. Thereafter, we perform literature search, and KEGG pathway and Gene Ontology analyses using DAVID database. Finally, we report several significant potential gene-markers (with the corresponding IDPs) that have inverse relationship between differential expression and methylation patterns, and that are hub genes of the TF-miRNA-gene network. Copyright © 2016 Elsevier B.V. All rights reserved.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

PubMed

López, Yosvany; Nakai, Kenta; Patil, Ashwini

2015-01-01

HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

Use of Graph Database for the Integration of Heterogeneous Biological Data.

PubMed

Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young

2017-03-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Use of Graph Database for the Integration of Heterogeneous Biological Data

PubMed Central

Yoon, Byoung-Ha; Kim, Seon-Kyu

2017-01-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data. PMID:28416946

Gene and protein nomenclature in public databases

PubMed Central

Fundel, Katrin; Zimmer, Ralf

2006-01-01

Background Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap. Results We compiled five gene and protein name dictionaries for each of the five model organisms (yeast, fly, mouse, rat, and human) from different organism-specific and general public databases. We analyzed the degree of ambiguity of gene and protein names within and between dictionaries, to a lexicon of common English words and domain-related non-gene terms, and we compared different data sources in terms of size of extracted dictionaries and overlap of synonyms between those. The study shows that the number of genes/proteins and synonyms covered in individual databases varies significantly for a given organism, and that the degree of ambiguity of synonyms varies significantly between different organisms. Furthermore, it shows that, despite considerable efforts of co-curation, the overlap of synonyms in different data sources is rather moderate and that the degree of ambiguity of gene names with common English words and domain-related non-gene terms varies depending on the considered organism. Conclusion In conclusion, these results indicate that the combination of data contained in different databases allows the generation of gene and protein name dictionaries that contain significantly more used names than dictionaries obtained from individual data sources. Furthermore, curation of combined dictionaries considerably increases size and decreases ambiguity. The entries of the curated synonym dictionary are available for manual querying, editing, and PubMed- or Google-search via the ProThesaurus-wiki. For automated querying via custom software, we offer a web service and an exemplary client application. PMID:16899134

IDENTIFICATION OF BIOLOGICALLY RELEVANT GENES USING A DATABASE OF RAT LIVER AND KIDNEY BASELINE GENE EXPRESSION

EPA Science Inventory

Microarray data from independent labs and studies can be compared to potentially identify toxicologically and biologically relevant genes. The Baseline Animal Database working group of HESI was formed to assess baseline gene expression from microarray data derived from control or...

KMeyeDB: a graphical database of mutations in genes that cause eye diseases.

PubMed

Kawamura, Takashi; Ohtsubo, Masafumi; Mitsuyama, Susumu; Ohno-Nakamura, Saho; Shimizu, Nobuyoshi; Minoshima, Shinsei

2010-06-01

KMeyeDB (http://mutview.dmb.med.keio.ac.jp/) is a database of human gene mutations that cause eye diseases. We have substantially enriched the amount of data in the database, which now contains information about the mutations of 167 human genes causing eye-related diseases including retinitis pigmentosa, cone-rod dystrophy, night blindness, Oguchi disease, Stargardt disease, macular degeneration, Leber congenital amaurosis, corneal dystrophy, cataract, glaucoma, retinoblastoma, Bardet-Biedl syndrome, and Usher syndrome. KMeyeDB is operated using the database software MutationView, which deals with various characters of mutations, gene structure, protein functional domains, and polymerase chain reaction (PCR) primers, as well as clinical data for each case. Users can access the database using an ordinary Internet browser with smooth user-interface, without user registration. The results are displayed on the graphical windows together with statistical calculations. All mutations and associated data have been collected from published articles. Careful data analysis with KMeyeDB revealed many interesting features regarding the mutations in 167 genes that cause 326 different types of eye diseases. Some genes are involved in multiple types of eye diseases, whereas several eye diseases are caused by different mutations in one gene.

PLMItRNA, a database for mitochondrial tRNA genes and tRNAs in photosynthetic eukaryotes.

PubMed

Damiano, F; Gallerani, R; Liuni, S; Licciulli, F; Ceci, L R

2001-01-01

The PLMItRNA database for mitochondrial tRNA molecules and genes in VIRIDIPLANTAE: (green plants) [Volpetti,V., Gallerani,R., DeBenedetto,C., Liuni,S., Licciulli,F. and Ceci,L.R. (2000) Nucleic Acids Res., 28, 159-162] has been enlarged to include algae. The database now contains 436 genes and 16 tRNA entries relative to 25 higher plants, eight green algae, four red algae (RHODOPHYTAE:) and two STRAMENOPILES: The PLMItRNA database is accessible via the WWW at http://bio-www.ba.cnr.it:8000/PLMItRNA.

Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies

PubMed Central

Yang, Tsun-Po; Beazley, Claude; Montgomery, Stephen B.; Dimas, Antigone S.; Gutierrez-Arcelus, Maria; Stranger, Barbara E.; Deloukas, Panos; Dermitzakis, Emmanouil T.

2010-01-01

Summary: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols. Availability: http://www.sanger.ac.uk/resources/software/genevar Contact: emmanouil.dermitzakis@unige.ch PMID:20702402

The construction of an EST database for Bombyx mori and its application

PubMed Central

Mita, Kazuei; Morimyo, Mitsuoki; Okano, Kazuhiro; Koike, Yoshiko; Nohata, Junko; Kawasaki, Hideki; Kadono-Okuda, Keiko; Yamamoto, Kimiko; Suzuki, Masataka G.; Shimada, Toru; Goldsmith, Marian R.; Maeda, Susumu

2003-01-01

To build a foundation for the complete genome analysis of Bombyx mori, we have constructed an EST database. Because gene expression patterns deeply depend on tissues as well as developmental stages, we analyzed many cDNA libraries prepared from various tissues and different developmental stages to cover the entire set of Bombyx genes. So far, the Bombyx EST database contains 35,000 ESTs from 36 cDNA libraries, which are grouped into ≈11,000 nonredundant ESTs with the average length of 1.25 kb. The comparison with FlyBase suggests that the present EST database, SilkBase, covers >55% of all genes of Bombyx. The fraction of library-specific ESTs in each cDNA library indicates that we have not yet reached saturation, showing the validity of our strategy for constructing an EST database to cover all genes. To tackle the coming saturation problem, we have checked two methods, subtraction and normalization, to increase coverage and decrease the number of housekeeping genes, resulting in a 5–11% increase of library-specific ESTs. The identification of a number of genes and comprehensive cloning of gene families have already emerged from the SilkBase search. Direct links of SilkBase with FlyBase and WormBase provide ready identification of candidate Lepidoptera-specific genes. PMID:14614147

A comprehensive aligned nifH gene database: a multipurpose tool for studies of nitrogen-fixing bacteria.

PubMed

Gaby, John Christian; Buckley, Daniel H

2014-01-01

We describe a nitrogenase gene sequence database that facilitates analysis of the evolution and ecology of nitrogen-fixing organisms. The database contains 32 954 aligned nitrogenase nifH sequences linked to phylogenetic trees and associated sequence metadata. The database includes 185 linked multigene entries including full-length nifH, nifD, nifK and 16S ribosomal RNA (rRNA) gene sequences. Evolutionary analyses enabled by the multigene entries support an ancient horizontal transfer of nitrogenase genes between Archaea and Bacteria and provide evidence that nifH has a different history of horizontal gene transfer from the nifDK enzyme core. Further analyses show that lineages in nitrogenase cluster I and cluster III have different rates of substitution within nifD, suggesting that nifD is under different selection pressure in these two lineages. Finally, we find that that the genetic divergence of nifH and 16S rRNA genes does not correlate well at sequence dissimilarity values used commonly to define microbial species, as stains having <3% sequence dissimilarity in their 16S rRNA genes can have up to 23% dissimilarity in nifH. The nifH database has a number of uses including phylogenetic and evolutionary analyses, the design and assessment of primers/probes and the evaluation of nitrogenase sequence diversity. Database URL: http://www.css.cornell.edu/faculty/buckley/nifh.htm.

A comprehensive aligned nifH gene database: a multipurpose tool for studies of nitrogen-fixing bacteria

PubMed Central

Gaby, John Christian; Buckley, Daniel H.

2014-01-01

We describe a nitrogenase gene sequence database that facilitates analysis of the evolution and ecology of nitrogen-fixing organisms. The database contains 32 954 aligned nitrogenase nifH sequences linked to phylogenetic trees and associated sequence metadata. The database includes 185 linked multigene entries including full-length nifH, nifD, nifK and 16S ribosomal RNA (rRNA) gene sequences. Evolutionary analyses enabled by the multigene entries support an ancient horizontal transfer of nitrogenase genes between Archaea and Bacteria and provide evidence that nifH has a different history of horizontal gene transfer from the nifDK enzyme core. Further analyses show that lineages in nitrogenase cluster I and cluster III have different rates of substitution within nifD, suggesting that nifD is under different selection pressure in these two lineages. Finally, we find that that the genetic divergence of nifH and 16S rRNA genes does not correlate well at sequence dissimilarity values used commonly to define microbial species, as stains having <3% sequence dissimilarity in their 16S rRNA genes can have up to 23% dissimilarity in nifH. The nifH database has a number of uses including phylogenetic and evolutionary analyses, the design and assessment of primers/probes and the evaluation of nitrogenase sequence diversity. Database URL: http://www.css.cornell.edu/faculty/buckley/nifh.htm PMID:24501396

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

Implementation of Whole Genome Sequencing (WGS) for Identification and Characterization of Shiga Toxin-Producing Escherichia coli (STEC) in the United States

PubMed Central

Lindsey, Rebecca L.; Pouseele, Hannes; Chen, Jessica C.; Strockbine, Nancy A.; Carleton, Heather A.

2016-01-01

Shiga toxin-producing Escherichia coli (STEC) is an important foodborne pathogen capable of causing severe disease in humans. Rapid and accurate identification and characterization techniques are essential during outbreak investigations. Current methods for characterization of STEC are expensive and time-consuming. With the advent of rapid and cheap whole genome sequencing (WGS) benchtop sequencers, the potential exists to replace traditional workflows with WGS. The aim of this study was to validate tools to do reference identification and characterization from WGS for STEC in a single workflow within an easy to use commercially available software platform. Publically available serotype, virulence, and antimicrobial resistance databases were downloaded from the Center for Genomic Epidemiology (CGE) (www.genomicepidemiology.org) and integrated into a genotyping plug-in with in silico PCR tools to confirm some of the virulence genes detected from WGS data. Additionally, down sampling experiments on the WGS sequence data were performed to determine a threshold for sequence coverage needed to accurately predict serotype and virulence genes using the established workflow. The serotype database was tested on a total of 228 genomes and correctly predicted from WGS for 96.1% of O serogroups and 96.5% of H serogroups identified by conventional testing techniques. A total of 59 genomes were evaluated to determine the threshold of coverage to detect the different WGS targets, 40 were evaluated for serotype and virulence gene detection and 19 for the stx gene subtypes. For serotype, 95% of the O and 100% of the H serogroups were detected at > 40x and ≥ 30x coverage, respectively. For virulence targets and stx gene subtypes, nearly all genes were detected at > 40x, though some targets were 100% detectable from genomes with coverage ≥20x. The resistance detection tool was 97% concordant with phenotypic testing results. With isolates sequenced to > 40x coverage, the different databases accurately predicted serotype, virulence, and resistance from WGS data, providing a fast and cheaper alternative to conventional typing techniques. PMID:27242777

Implementation of Whole Genome Sequencing (WGS) for Identification and Characterization of Shiga Toxin-Producing Escherichia coli (STEC) in the United States.

PubMed

Lindsey, Rebecca L; Pouseele, Hannes; Chen, Jessica C; Strockbine, Nancy A; Carleton, Heather A

2016-01-01

Shiga toxin-producing Escherichia coli (STEC) is an important foodborne pathogen capable of causing severe disease in humans. Rapid and accurate identification and characterization techniques are essential during outbreak investigations. Current methods for characterization of STEC are expensive and time-consuming. With the advent of rapid and cheap whole genome sequencing (WGS) benchtop sequencers, the potential exists to replace traditional workflows with WGS. The aim of this study was to validate tools to do reference identification and characterization from WGS for STEC in a single workflow within an easy to use commercially available software platform. Publically available serotype, virulence, and antimicrobial resistance databases were downloaded from the Center for Genomic Epidemiology (CGE) (www.genomicepidemiology.org) and integrated into a genotyping plug-in with in silico PCR tools to confirm some of the virulence genes detected from WGS data. Additionally, down sampling experiments on the WGS sequence data were performed to determine a threshold for sequence coverage needed to accurately predict serotype and virulence genes using the established workflow. The serotype database was tested on a total of 228 genomes and correctly predicted from WGS for 96.1% of O serogroups and 96.5% of H serogroups identified by conventional testing techniques. A total of 59 genomes were evaluated to determine the threshold of coverage to detect the different WGS targets, 40 were evaluated for serotype and virulence gene detection and 19 for the stx gene subtypes. For serotype, 95% of the O and 100% of the H serogroups were detected at > 40x and ≥ 30x coverage, respectively. For virulence targets and stx gene subtypes, nearly all genes were detected at > 40x, though some targets were 100% detectable from genomes with coverage ≥20x. The resistance detection tool was 97% concordant with phenotypic testing results. With isolates sequenced to > 40x coverage, the different databases accurately predicted serotype, virulence, and resistance from WGS data, providing a fast and cheaper alternative to conventional typing techniques.

Database resources of the National Center for Biotechnology Information.

PubMed

2016-01-04

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (PubMed Central (PMC), Bookshelf and PubReader), health (ClinVar, dbGaP, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen), genomes (BioProject, Assembly, Genome, BioSample, dbSNP, dbVar, Epigenomics, the Map Viewer, Nucleotide, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser and the Trace Archive), genes (Gene, Gene Expression Omnibus (GEO), HomoloGene, PopSet and UniGene), proteins (Protein, the Conserved Domain Database (CDD), COBALT, Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB) and Protein Clusters) and chemicals (Biosystems and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for most of these databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. Published by Oxford University Press on behalf of Nucleic Acids Research 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Database resources of the National Center for Biotechnology Information.

PubMed

2015-01-01

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (Bookshelf, PubMed Central (PMC) and PubReader); medical genetics (ClinVar, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen); genes and genomics (BioProject, BioSample, dbSNP, dbVar, Epigenomics, Gene, Gene Expression Omnibus (GEO), Genome, HomoloGene, the Map Viewer, Nucleotide, PopSet, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser, Trace Archive and UniGene); and proteins and chemicals (Biosystems, COBALT, the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB), Protein Clusters, Protein and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for many of these databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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.

Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

PubMed

Liang, Chen; Sun, Jingchun; Tao, Cui

2015-01-01

There remain significant difficulties selecting probable candidate drugs from existing databases. We describe an ontology-oriented approach to represent the nexus between genes, drugs, phenotypes, symptoms, and diseases from multiple information sources. We also report a case study in which we attempted to explore candidate drugs effective for bipolar disorder and epilepsy. We constructed an ontology incorporating knowledge between the two diseases and performed semantic reasoning tasks with the ontology. The results suggested 48 candidate drugs that hold promise for further breakthrough. The evaluation demonstrated the validity our approach. Our approach prioritizes the candidate drugs that have potential associations among genes, phenotypes and symptoms, and thus facilitates the data integration and drug repurposing in psychiatric disorders.

The Pathway Coexpression Network: Revealing pathway relationships

PubMed Central

Tanzi, Rudolph E.

2018-01-01

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/. PMID:29554099

Distinct profiles of expressed sequence tags during intestinal regeneration in the sea cucumber Holothuria glaberrima

PubMed Central

Rojas-Cartagena, Carmencita; Ortíz-Pineda, Pablo; Ramírez-Gómez, Francisco; Suárez-Castillo, Edna C.; Matos-Cruz, Vanessa; Rodríguez, Carlos; Ortíz-Zuazaga, Humberto; García-Arrarás, José E.

2010-01-01

Repair and regeneration are key processes for tissue maintenance, and their disruption may lead to disease states. Little is known about the molecular mechanisms that underline the repair and regeneration of the digestive tract. The sea cucumber Holothuria glaberrima represents an excellent model to dissect and characterize the molecular events during intestinal regeneration. To study the gene expression profile, cDNA libraries were constructed from normal, 3-day, and 7-day regenerating intestines of H. glaberrima. Clones were randomly sequenced and queried against the nonredundant protein database at the National Center for Biotechnology Information. RT-PCR analyses were made of several genes to determine their expression profile during intestinal regeneration. A total of 5,173 sequences from three cDNA libraries were obtained. About 46.2, 35.6, and 26.2% of the sequences for the normal, 3-days, and 7-days cDNA libraries, respectively, shared significant similarity with known sequences in the protein database of GenBank but only present 10% of similarity among them. Analysis of the libraries in terms of functional processes, protein domains, and most common sequences suggests that a differential expression profile is taking place during the regeneration process. Further examination of the expressed sequence tag dataset revealed that 12 putative genes are differentially expressed at significant level (R > 6). Experimental validation by RT-PCR analysis reveals that at least three genes (unknown C-4677-1, melanotransferrin, and centaurin) present a differential expression during regeneration. These findings strongly suggest that the gene expression profile varies among regeneration stages and provide evidence for the existence of differential gene expression. PMID:17579180

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

PubMed Central

Liu, Zhi-Ping; Wu, Canglin; Miao, Hongyu; Wu, Hulin

2015-01-01

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org PMID:26424082

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Relating genes to function: identifying enriched transcription factors using the ENCODE ChIP-Seq significance tool.

PubMed

Auerbach, Raymond K; Chen, Bin; Butte, Atul J

2013-08-01

Biological analysis has shifted from identifying genes and transcripts to mapping these genes and transcripts to biological functions. The ENCODE Project has generated hundreds of ChIP-Seq experiments spanning multiple transcription factors and cell lines for public use, but tools for a biomedical scientist to analyze these data are either non-existent or tailored to narrow biological questions. We present the ENCODE ChIP-Seq Significance Tool, a flexible web application leveraging public ENCODE data to identify enriched transcription factors in a gene or transcript list for comparative analyses. The ENCODE ChIP-Seq Significance Tool is written in JavaScript on the client side and has been tested on Google Chrome, Apple Safari and Mozilla Firefox browsers. Server-side scripts are written in PHP and leverage R and a MySQL database. The tool is available at http://encodeqt.stanford.edu. abutte@stanford.edu Supplementary material is available at Bioinformatics online.

MEPD: a Medaka gene expression pattern database

PubMed Central

Henrich, Thorsten; Ramialison, Mirana; Quiring, Rebecca; Wittbrodt, Beate; Furutani-Seiki, Makoto; Wittbrodt, Joachim; Kondoh, Hisato

2003-01-01

The Medaka Expression Pattern Database (MEPD) stores and integrates information of gene expression during embryonic development of the small freshwater fish Medaka (Oryzias latipes). Expression patterns of genes identified by ESTs are documented by images and by descriptions through parameters such as staining intensity, category and comments and through a comprehensive, hierarchically organized dictionary of anatomical terms. Sequences of the ESTs are available and searchable through BLAST. ESTs in the database are clustered upon entry and have been blasted against public data-bases. The BLAST results are updated regularly, stored within the database and searchable. The MEPD is a project within the Medaka Genome Initiative (MGI) and entries will be interconnected to integrated genomic map databases. MEPD is accessible through the WWW at http://medaka.dsp.jst.go.jp/MEPD. PMID:12519950

A statistical approach to identify, monitor, and manage incomplete curated data sets.

PubMed

Howe, Douglas G

2018-04-02

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here. In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval. This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

RPG: the Ribosomal Protein Gene database.

PubMed

Nakao, Akihiro; Yoshihama, Maki; Kenmochi, Naoya

2004-01-01

RPG (http://ribosome.miyazaki-med.ac.jp/) is a new database that provides detailed information about ribosomal protein (RP) genes. It contains data from humans and other organisms, including Drosophila melanogaster, Caenorhabditis elegans, Saccharo myces cerevisiae, Methanococcus jannaschii and Escherichia coli. Users can search the database by gene name and organism. Each record includes sequences (genomic, cDNA and amino acid sequences), intron/exon structures, genomic locations and information about orthologs. In addition, users can view and compare the gene structures of the above organisms and make multiple amino acid sequence alignments. RPG also provides information on small nucleolar RNAs (snoRNAs) that are encoded in the introns of RP genes.

RPG: the Ribosomal Protein Gene database

PubMed Central

Nakao, Akihiro; Yoshihama, Maki; Kenmochi, Naoya

2004-01-01

RPG (http://ribosome.miyazaki-med.ac.jp/) is a new database that provides detailed information about ribosomal protein (RP) genes. It contains data from humans and other organisms, including Drosophila melanogaster, Caenorhabditis elegans, Saccharo myces cerevisiae, Methanococcus jannaschii and Escherichia coli. Users can search the database by gene name and organism. Each record includes sequences (genomic, cDNA and amino acid sequences), intron/exon structures, genomic locations and information about orthologs. In addition, users can view and compare the gene structures of the above organisms and make multiple amino acid sequence alignments. RPG also provides information on small nucleolar RNAs (snoRNAs) that are encoded in the introns of RP genes. PMID:14681386

miBLAST: scalable evaluation of a batch of nucleotide sequence queries with BLAST

PubMed Central

Kim, You Jung; Boyd, Andrew; Athey, Brian D.; Patel, Jignesh M.

2005-01-01

A common task in many modern bioinformatics applications is to match a set of nucleotide query sequences against a large sequence dataset. Exis-ting tools, such as BLAST, are designed to evaluate a single query at a time and can be unacceptably slow when the number of sequences in the query set is large. In this paper, we present a new algorithm, called miBLAST, that evaluates such batch workloads efficiently. At the core, miBLAST employs a q-gram filtering and an index join for efficiently detecting similarity between the query sequences and database sequences. This set-oriented technique, which indexes both the query and the database sets, results in substantial performance improvements over existing methods. Our results show that miBLAST is significantly faster than BLAST in many cases. For example, miBLAST aligned 247 965 oligonucleotide sequences in the Affymetrix probe set against the Human UniGene in 1.26 days, compared with 27.27 days with BLAST (an improvement by a factor of 22). The relative performance of miBLAST increases for larger word sizes; however, it decreases for longer queries. miBLAST employs the familiar BLAST statistical model and output format, guaranteeing the same accuracy as BLAST and facilitating a seamless transition for existing BLAST users. PMID:16061938

PGMapper: a web-based tool linking phenotype to genes.

PubMed

Xiong, Qing; Qiu, Yuhui; Gu, Weikuan

2008-04-01

With the availability of whole genome sequence in many species, linkage analysis, positional cloning and microarray are gradually becoming powerful tools for investigating the links between phenotype and genotype or genes. However, in these methods, causative genes underlying a quantitative trait locus, or a disease, are usually located within a large genomic region or a large set of genes. Examining the function of every gene is very time consuming and needs to retrieve and integrate the information from multiple databases or genome resources. PGMapper is a software tool for automatically matching phenotype to genes from a defined genome region or a group of given genes by combining the mapping information from the Ensembl database and gene function information from the OMIM and PubMed databases. PGMapper is currently available for candidate gene search of human, mouse, rat, zebrafish and 12 other species. Available online at http://www.genediscovery.org/pgmapper/index.jsp.

UniGene Tabulator: a full parser for the UniGene format.

PubMed

Lenzi, Luca; Frabetti, Flavia; Facchin, Federica; Casadei, Raffaella; Vitale, Lorenza; Canaider, Silvia; Carinci, Paolo; Zannotti, Maria; Strippoli, Pierluigi

2006-10-15

UniGene Tabulator 1.0 provides a solution for full parsing of UniGene flat file format; it implements a structured graphical representation of each data field present in UniGene following import into a common database managing system usable in a personal computer. This database includes related tables for sequence, protein similarity, sequence-tagged site (STS) and transcript map interval (TXMAP) data, plus a summary table where each record represents a UniGene cluster. UniGene Tabulator enables full local management of UniGene data, allowing parsing, querying, indexing, retrieving, exporting and analysis of UniGene data in a relational database form, usable on Macintosh (OS X 10.3.9 or later) and Windows (2000, with service pack 4, XP, with service pack 2 or later) operating systems-based computers. The current release, including both the FileMaker runtime applications, is freely available at http://apollo11.isto.unibo.it/software/

acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

DOE PAGES

Lux, Markus; Kruger, Jan; Rinke, Christian; ...

2016-12-20

A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data. We present acdc, a tool specifically developed to aidmore » the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows. Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.« less

acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

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

Lux, Markus; Kruger, Jan; Rinke, Christian

A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data. We present acdc, a tool specifically developed to aidmore » the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows. Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.« less

DRUMS: a human disease related unique gene mutation search engine.

PubMed

Li, Zuofeng; Liu, Xingnan; Wen, Jingran; Xu, Ye; Zhao, Xin; Li, Xuan; Liu, Lei; Zhang, Xiaoyan

2011-10-01

With the completion of the human genome project and the development of new methods for gene variant detection, the integration of mutation data and its phenotypic consequences has become more important than ever. Among all available resources, locus-specific databases (LSDBs) curate one or more specific genes' mutation data along with high-quality phenotypes. Although some genotype-phenotype data from LSDB have been integrated into central databases little effort has been made to integrate all these data by a search engine approach. In this work, we have developed disease related unique gene mutation search engine (DRUMS), a search engine for human disease related unique gene mutation as a convenient tool for biologists or physicians to retrieve gene variant and related phenotype information. Gene variant and phenotype information were stored in a gene-centred relational database. Moreover, the relationships between mutations and diseases were indexed by the uniform resource identifier from LSDB, or another central database. By querying DRUMS, users can access the most popular mutation databases under one interface. DRUMS could be treated as a domain specific search engine. By using web crawling, indexing, and searching technologies, it provides a competitively efficient interface for searching and retrieving mutation data and their relationships to diseases. The present system is freely accessible at http://www.scbit.org/glif/new/drums/index.html. © 2011 Wiley-Liss, Inc.

The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice.

PubMed

Abbott, Kenneth L; Nyre, Erik T; Abrahante, Juan; Ho, Yen-Yi; Isaksson Vogel, Rachel; Starr, Timothy K

2015-01-01

Identification of cancer driver gene mutations is crucial for advancing cancer therapeutics. Due to the overwhelming number of passenger mutations in the human tumor genome, it is difficult to pinpoint causative driver genes. Using transposon mutagenesis in mice many laboratories have conducted forward genetic screens and identified thousands of candidate driver genes that are highly relevant to human cancer. Unfortunately, this information is difficult to access and utilize because it is scattered across multiple publications using different mouse genome builds and strength metrics. To improve access to these findings and facilitate meta-analyses, we developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/). The CCGD is a manually curated database containing a unified description of all identified candidate driver genes and the genomic location of transposon common insertion sites (CISs) from all currently published transposon-based screens. To demonstrate relevance to human cancer, we performed a modified gene set enrichment analysis using KEGG pathways and show that human cancer pathways are highly enriched in the database. We also used hierarchical clustering to identify pathways enriched in blood cancers compared to solid cancers. The CCGD is a novel resource available to scientists interested in the identification of genetic drivers of cancer. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes.

PubMed

Niu, Sheng-Yong; Yang, Jinyu; McDermaid, Adam; Zhao, Jing; Kang, Yu; Ma, Qin

2017-05-08

Metagenomic and metatranscriptomic sequencing approaches are more frequently being used to link microbiota to important diseases and ecological changes. Many analyses have been used to compare the taxonomic and functional profiles of microbiota across habitats or individuals. While a large portion of metagenomic analyses focus on species-level profiling, some studies use strain-level metagenomic analyses to investigate the relationship between specific strains and certain circumstances. Metatranscriptomic analysis provides another important insight into activities of genes by examining gene expression levels of microbiota. Hence, combining metagenomic and metatranscriptomic analyses will help understand the activity or enrichment of a given gene set, such as drug-resistant genes among microbiome samples. Here, we summarize existing bioinformatics tools of metagenomic and metatranscriptomic data analysis, the purpose of which is to assist researchers in deciding the appropriate tools for their microbiome studies. Additionally, we propose an Integrated Meta-Function mapping pipeline to incorporate various reference databases and accelerate functional gene mapping procedures for both metagenomic and metatranscriptomic analyses. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Transactional Database Transformation and Its Application in Prioritizing Human Disease Genes

PubMed Central

Xiang, Yang; Payne, Philip R.O.; Huang, Kun

2013-01-01

Binary (0,1) matrices, commonly known as transactional databases, can represent many application data, including gene-phenotype data where “1” represents a confirmed gene-phenotype relation and “0” represents an unknown relation. It is natural to ask what information is hidden behind these “0”s and “1”s. Unfortunately, recent matrix completion methods, though very effective in many cases, are less likely to infer something interesting from these (0,1)-matrices. To answer this challenge, we propose IndEvi, a very succinct and effective algorithm to perform independent-evidence-based transactional database transformation. Each entry of a (0,1)-matrix is evaluated by “independent evidence” (maximal supporting patterns) extracted from the whole matrix for this entry. The value of an entry, regardless of its value as 0 or 1, has completely no effect for its independent evidence. The experiment on a gene-phenotype database shows that our method is highly promising in ranking candidate genes and predicting unknown disease genes. PMID:21422495

Integrating text mining into the MGI biocuration workflow

PubMed Central

Dowell, K.G.; McAndrews-Hill, M.S.; Hill, D.P.; Drabkin, H.J.; Blake, J.A.

2009-01-01

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals. In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen ∼1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database. Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature. PMID:20157492

Integrating text mining into the MGI biocuration workflow.

PubMed

Dowell, K G; McAndrews-Hill, M S; Hill, D P; Drabkin, H J; Blake, J A

2009-01-01

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals.In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen approximately 1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database.Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature.

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.

PubMed

Le, Duc-Hau; Verbeke, Lieven; Son, Le Hoang; Chu, Dinh-Toi; Pham, Van-Huy

2017-11-14

MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks.

Bioinformatic analysis of the nucleotide binding site-encoding disease-resistance genes in foxtail millet (Setaria italica (L.) Beauv.).

PubMed

Zhu, Y B; Xie, X Q; Li, Z Y; Bai, H; Dong, L; Dong, Z P; Dong, J G

2014-08-28

The nucleotide-binding site (NBS) disease-resistance genes are the largest category of plant disease-resistance gene analogs. The complete set of disease-resistant candidate genes, which encode the NBS sequence, was filtered in the genomes of two varieties of foxtail millet (Yugu1 and 'Zhang gu'). This study investigated a number of characteristics of the putative NBS genes, such as structural diversity and phylogenetic relationships. A total of 269 and 281 NBS-coding sequences were identified in Yugu1 and 'Zhang gu', respectively. When the two databases were compared, 72 genes were found to be identical and 164 genes showed more than 90% similarity. Physical positioning and gene family analysis of the NBS disease-resistance genes in the genome revealed that the number of genes on each chromosome was similar in both varieties. The eighth chromosome contained the largest number of genes and the ninth chromosome contained the lowest number of genes. Exactly 34 gene clusters containing the 161 genes were found in the Yugu1 genome, with each cluster containing 4.7 genes on average. In comparison, the 'Zhang gu' genome possessed 28 gene clusters, which had 151 genes, with an average of 5.4 genes in each cluster. The largest gene cluster, located on the eighth chromosome, contained 12 genes in the Yugu1 database, whereas it contained 16 genes in the 'Zhang gu' database. The classification results showed that the CC-NBS-LRR gene made up the largest part of each chromosome in the two databases. Two TIR-NBS genes were also found in the Yugu1 genome.

SinEx DB: a database for single exon coding sequences in mammalian genomes.

PubMed

Jorquera, Roddy; Ortiz, Rodrigo; Ossandon, F; Cárdenas, Juan Pablo; Sepúlveda, Rene; González, Carolina; Holmes, David S

2016-01-01

Eukaryotic genes are typically interrupted by intragenic, noncoding sequences termed introns. However, some genes lack introns in their coding sequence (CDS) and are generally known as 'single exon genes' (SEGs). In this work, a SEG is defined as a nuclear, protein-coding gene that lacks introns in its CDS. Whereas, many public databases of Eukaryotic multi-exon genes are available, there are only two specialized databases for SEGs. The present work addresses the need for a more extensive and diverse database by creating SinEx DB, a publicly available, searchable database of predicted SEGs from 10 completely sequenced mammalian genomes including human. SinEx DB houses the DNA and protein sequence information of these SEGs and includes their functional predictions (KOG) and the relative distribution of these functions within species. The information is stored in a relational database built with My SQL Server 5.1.33 and the complete dataset of SEG sequences and their functional predictions are available for downloading. SinEx DB can be interrogated by: (i) a browsable phylogenetic schema, (ii) carrying out BLAST searches to the in-house SinEx DB of SEGs and (iii) via an advanced search mode in which the database can be searched by key words and any combination of searches by species and predicted functions. SinEx DB provides a rich source of information for advancing our understanding of the evolution and function of SEGs.Database URL: www.sinex.cl. © The Author(s) 2016. Published by Oxford University Press.

Comparative Analysis of Gene Expression for Convergent Evolution of Camera Eye Between Octopus and Human

PubMed Central

Ogura, Atsushi; Ikeo, Kazuho; Gojobori, Takashi

2004-01-01

Although the camera eye of the octopus is very similar to that of humans, phylogenetic and embryological analyses have suggested that their camera eyes have been acquired independently. It has been known as a typical example of convergent evolution. To study the molecular basis of convergent evolution of camera eyes, we conducted a comparative analysis of gene expression in octopus and human camera eyes. We sequenced 16,432 ESTs of the octopus eye, leading to 1052 nonredundant genes that have matches in the protein database. Comparing these 1052 genes with 13,303 already-known ESTs of the human eye, 729 (69.3%) genes were commonly expressed between the human and octopus eyes. On the contrary, when we compared octopus eye ESTs with human connective tissue ESTs, the expression similarity was quite low. To trace the evolutionary changes that are potentially responsible for camera eye formation, we also compared octopus-eye ESTs with the completed genome sequences of other organisms. We found that 1019 out of the 1052 genes had already existed at the common ancestor of bilateria, and 875 genes were conserved between humans and octopuses. It suggests that a larger number of conserved genes and their similar gene expression may be responsible for the convergent evolution of the camera eye. PMID:15289475

FlyAtlas: database of gene expression in the tissues of Drosophila melanogaster

PubMed Central

Robinson, Scott W.; Herzyk, Pawel; Dow, Julian A. T.; Leader, David P.

2013-01-01

The FlyAtlas resource contains data on the expression of the genes of Drosophila melanogaster in different tissues (currently 25—17 adult and 8 larval) obtained by hybridization of messenger RNA to Affymetrix Drosophila Genome 2 microarrays. The microarray probe sets cover 13 250 Drosophila genes, detecting 12 533 in an unambiguous manner. The data underlying the original web application (http://flyatlas.org) have been restructured into a relational database and a Java servlet written to provide a new web interface, FlyAtlas 2 (http://flyatlas.gla.ac.uk/), which allows several additional queries. Users can retrieve data for individual genes or for groups of genes belonging to the same or related ontological categories. Assistance in selecting valid search terms is provided by an Ajax ‘autosuggest’ facility that polls the database as the user types. Searches can also focus on particular tissues, and data can be retrieved for the most highly expressed genes, for genes of a particular category with above-average expression or for genes with the greatest difference in expression between the larval and adult stages. A novel facility allows the database to be queried with a specific gene to find other genes with a similar pattern of expression across the different tissues. PMID:23203866

FlyAtlas: database of gene expression in the tissues of Drosophila melanogaster.

PubMed

Robinson, Scott W; Herzyk, Pawel; Dow, Julian A T; Leader, David P

2013-01-01

The FlyAtlas resource contains data on the expression of the genes of Drosophila melanogaster in different tissues (currently 25-17 adult and 8 larval) obtained by hybridization of messenger RNA to Affymetrix Drosophila Genome 2 microarrays. The microarray probe sets cover 13,250 Drosophila genes, detecting 12,533 in an unambiguous manner. The data underlying the original web application (http://flyatlas.org) have been restructured into a relational database and a Java servlet written to provide a new web interface, FlyAtlas 2 (http://flyatlas.gla.ac.uk/), which allows several additional queries. Users can retrieve data for individual genes or for groups of genes belonging to the same or related ontological categories. Assistance in selecting valid search terms is provided by an Ajax 'autosuggest' facility that polls the database as the user types. Searches can also focus on particular tissues, and data can be retrieved for the most highly expressed genes, for genes of a particular category with above-average expression or for genes with the greatest difference in expression between the larval and adult stages. A novel facility allows the database to be queried with a specific gene to find other genes with a similar pattern of expression across the different tissues.

NABIC marker database: A molecular markers information network of agricultural crops.

PubMed

Kim, Chang-Kug; Seol, Young-Joo; Lee, Dong-Jun; Jeong, In-Seon; Yoon, Ung-Han; Lee, Gang-Seob; Hahn, Jang-Ho; Park, Dong-Suk

2013-01-01

In 2013, National Agricultural Biotechnology Information Center (NABIC) reconstructs a molecular marker database for useful genetic resources. The web-based marker database consists of three major functional categories: map viewer, RSN marker and gene annotation. It provides 7250 marker locations, 3301 RSN marker property, 3280 molecular marker annotation information in agricultural plants. The individual molecular marker provides information such as marker name, expressed sequence tag number, gene definition and general marker information. This updated marker-based database provides useful information through a user-friendly web interface that assisted in tracing any new structures of the chromosomes and gene positional functions using specific molecular markers. The database is available for free at http://nabic.rda.go.kr/gere/rice/molecularMarkers/

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

PubMed

Maier, Dieter; Kalus, Wenzel; Wolff, Martin; Kalko, Susana G; Roca, Josep; Marin de Mas, Igor; Turan, Nil; Cascante, Marta; Falciani, Francesco; Hernandez, Miguel; Villà-Freixa, Jordi; Losko, Sascha

2011-03-05

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

PubMed Central

2011-01-01

Background To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development. PMID:21375767

Update of the Diatom EST Database: a new tool for digital transcriptomics

PubMed Central

Maheswari, Uma; Mock, Thomas; Armbrust, E. Virginia; Bowler, Chris

2009-01-01

The Diatom Expressed Sequence Tag (EST) Database was constructed to provide integral access to ESTs from these ecologically and evolutionarily interesting microalgae. It has now been updated with 130 000 Phaeodactylum tricornutum ESTs from 16 cDNA libraries and 77 000 Thalassiosira pseudonana ESTs from seven libraries, derived from cells grown in different nutrient and stress regimes. The updated relational database incorporates results from statistical analyses such as log-likelihood ratios and hierarchical clustering, which help to identify differentially expressed genes under different conditions, and allow similarities in gene expression in different libraries to be investigated in a functional context. The database also incorporates links to the recently sequenced genomes of P. tricornutum and T. pseudonana, enabling an easy cross-talk between the expression pattern of diatom orthologs and the genome browsers. These improvements will facilitate exploration of diatom responses to conditions of ecological relevance and will aid gene function identification of diatom-specific genes and in silico gene prediction in this largely unexplored class of eukaryotes. The updated Diatom EST Database is available at http://www.biologie.ens.fr/diatomics/EST3. PMID:19029140

PlantRGDB: A Database of Plant Retrocopied Genes.

PubMed

Wang, Yi

2017-01-01

RNA-based gene duplication, known as retrocopy, plays important roles in gene origination and genome evolution. The genomes of many plants have been sequenced, offering an opportunity to annotate and mine the retrocopies in plant genomes. However, comprehensive and unified annotation of retrocopies in these plants is still lacking. In this study I constructed the PlantRGDB (Plant Retrocopied Gene DataBase), the first database of plant retrocopies, to provide a putatively complete centralized list of retrocopies in plant genomes. The database is freely accessible at http://probes.pw.usda.gov/plantrgdb or http://aegilops.wheat.ucdavis.edu/plantrgdb. It currently integrates 49 plant species and 38,997 retrocopies along with characterization information. PlantRGDB provides a user-friendly web interface for searching, browsing and downloading the retrocopies in the database. PlantRGDB also offers graphical viewer-integrated sequence information for displaying the structure of each retrocopy. The attributes of the retrocopies of each species are reported using a browse function. In addition, useful tools, such as an advanced search and BLAST, are available to search the database more conveniently. In conclusion, the database will provide a web platform for obtaining valuable insight into the generation of retrocopies and will supplement research on gene duplication and genome evolution in plants. © The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

The Finnish disease heritage database (FinDis) update-a database for the genes mutated in the Finnish disease heritage brought to the next-generation sequencing era.

PubMed

Polvi, Anne; Linturi, Henna; Varilo, Teppo; Anttonen, Anna-Kaisa; Byrne, Myles; Fokkema, Ivo F A C; Almusa, Henrikki; Metzidis, Anthony; Avela, Kristiina; Aula, Pertti; Kestilä, Marjo; Muilu, Juha

2013-11-01

The Finnish Disease Heritage Database (FinDis) (http://findis.org) was originally published in 2004 as a centralized information resource for rare monogenic diseases enriched in the Finnish population. The FinDis database originally contained 405 causative variants for 30 diseases. At the time, the FinDis database was a comprehensive collection of data, but since 1994, a large amount of new information has emerged, making the necessity to update the database evident. We collected information and updated the database to contain genes and causative variants for 35 diseases, including six more genes and more than 1,400 additional disease-causing variants. Information for causative variants for each gene is collected under the LOVD 3.0 platform, enabling easy updating. The FinDis portal provides a centralized resource and user interface to link information on each disease and gene with variant data in the LOVD 3.0 platform. The software written to achieve this has been open-sourced and made available on GitHub (http://github.com/findis-db), allowing biomedical institutions in other countries to present their national data in a similar way, and to both contribute to, and benefit from, standardized variation data. The updated FinDis portal provides a unique resource to assist patient diagnosis, research, and the development of new cures. © 2013 WILEY PERIODICALS, INC.

Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana.

PubMed

Hansen, Bjoern Oest; Meyer, Etienne H; Ferrari, Camilla; Vaid, Neha; Movahedi, Sara; Vandepoele, Klaas; Nikoloski, Zoran; Mutwil, Marek

2018-03-01

Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

MGDB: crossing the marker genes of a user microarray with a database of public-microarrays marker genes.

PubMed

Huerta, Mario; Munyi, Marc; Expósito, David; Querol, Enric; Cedano, Juan

2014-06-15

The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different microarrays comparable with tools easy to use for scientists. We have developed these two solutions in our server, implementing a database of microarray marker genes (Marker Genes Data Base). This database contains the marker genes of all GEO microarray datasets and it is updated monthly with the new microarrays from GEO. Thus, researchers can see whether the marker genes of their microarray are marker genes in other microarrays in the database, expanding the analysis of their microarray to the rest of the public microarrays. This solution helps not only to corroborate the conclusions regarding a researcher's microarray but also to identify the phenotype of different subsets of individuals under investigation, to frame the results with microarray experiments from other species, pathologies or tissues, to search for drugs that promote the transition between the studied phenotypes, to detect undesirable side effects of the treatment applied, etc. Thus, the researcher can quickly add relevant information to his/her studies from all of the previous analyses performed in other studies as long as they have been deposited in public repositories. Marker-gene database tool: http://ibb.uab.es/mgdb © The Author 2014. Published by Oxford University Press.

GeneSigDB—a curated database of gene expression signatures

PubMed Central

Culhane, Aedín C.; Schwarzl, Thomas; Sultana, Razvan; Picard, Kermshlise C.; Picard, Shaita C.; Lu, Tim H.; Franklin, Katherine R.; French, Simon J.; Papenhausen, Gerald; Correll, Mick; Quackenbush, John

2010-01-01

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats. PMID:19934259

New mutations and an updated database for the patched-1 (PTCH1) gene.

PubMed

Reinders, Marie G; van Hout, Antonius F; Cosgun, Betûl; Paulussen, Aimée D; Leter, Edward M; Steijlen, Peter M; Mosterd, Klara; van Geel, Michel; Gille, Johan J

2018-05-01

Basal cell nevus syndrome (BCNS) is an autosomal dominant disorder characterized by multiple basal cell carcinomas (BCCs), maxillary keratocysts, and cerebral calcifications. BCNS most commonly is caused by a germline mutation in the patched-1 (PTCH1) gene. PTCH1 mutations are also described in patients with holoprosencephaly. We have established a locus-specific database for the PTCH1 gene using the Leiden Open Variation Database (LOVD). We included 117 new PTCH1 variations, in addition to 331 previously published unique PTCH1 mutations. These new mutations were found in 141 patients who had a positive PTCH1 mutation analysis in either the VU University Medical Centre (VUMC) or Maastricht University Medical Centre (MUMC) between 1995 and 2015. The database contains 331 previously published unique PTCH1 mutations and 117 new PTCH1 variations. We have established a locus-specific database for the PTCH1 gene using the Leiden Open Variation Database (LOVD). The database provides an open collection for both clinicians and researchers and is accessible online at http://www.lovd.nl/PTCH1. © 2018 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.

Database resources of the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Barrett, Tanya; Benson, Dennis A.; Bryant, Stephen H.; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M.; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Feolo, Michael; Geer, Lewis Y.; Helmberg, Wolfgang; Kapustin, Yuri; Khovayko, Oleg; Landsman, David; Lipman, David J.; Madden, Thomas L.; Maglott, Donna R.; Miller, Vadim; Ostell, James; Pruitt, Kim D.; Schuler, Gregory D.; Shumway, Martin; Sequeira, Edwin; Sherry, Steven T.; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Tatusov, Roman L.; Tatusova, Tatiana A.; Wagner, Lukas; Yaschenko, Eugene

2008-01-01

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data available through NCBI's web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link, Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace, Assembly, and Short Read Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Entrez Probe, GENSAT, Database of Genotype and Phenotype, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool and the PubChem suite of small molecule databases. Augmenting the web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. PMID:18045790

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

PubMed

Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg .

MELOGEN: an EST database for melon functional genomics

PubMed Central

Gonzalez-Ibeas, Daniel; Blanca, José; Roig, Cristina; González-To, Mireia; Picó, Belén; Truniger, Verónica; Gómez, Pedro; Deleu, Wim; Caño-Delgado, Ana; Arús, Pere; Nuez, Fernando; Garcia-Mas, Jordi; Puigdomènech, Pere; Aranda, Miguel A

2007-01-01

Background Melon (Cucumis melo L.) is one of the most important fleshy fruits for fresh consumption. Despite this, few genomic resources exist for this species. To facilitate the discovery of genes involved in essential traits, such as fruit development, fruit maturation and disease resistance, and to speed up the process of breeding new and better adapted melon varieties, we have produced a large collection of expressed sequence tags (ESTs) from eight normalized cDNA libraries from different tissues in different physiological conditions. Results We determined over 30,000 ESTs that were clustered into 16,637 non-redundant sequences or unigenes, comprising 6,023 tentative consensus sequences (contigs) and 10,614 unclustered sequences (singletons). Many potential molecular markers were identified in the melon dataset: 1,052 potential simple sequence repeats (SSRs) and 356 single nucleotide polymorphisms (SNPs) were found. Sixty-nine percent of the melon unigenes showed a significant similarity with proteins in databases. Functional classification of the unigenes was carried out following the Gene Ontology scheme. In total, 9,402 unigenes were mapped to one or more ontology. Remarkably, the distributions of melon and Arabidopsis unigenes followed similar tendencies, suggesting that the melon dataset is representative of the whole melon transcriptome. Bioinformatic analyses primarily focused on potential precursors of melon micro RNAs (miRNAs) in the melon dataset, but many other genes potentially controlling disease resistance and fruit quality traits were also identified. Patterns of transcript accumulation were characterised by Real-Time-qPCR for 20 of these genes. Conclusion The collection of ESTs characterised here represents a substantial increase on the genetic information available for melon. A database (MELOGEN) which contains all EST sequences, contig images and several tools for analysis and data mining has been created. This set of sequences constitutes also the basis for an oligo-based microarray for melon that is being used in experiments to further analyse the melon transcriptome. PMID:17767721

Finding approximate gene clusters with Gecko 3.

PubMed

Winter, Sascha; Jahn, Katharina; Wehner, Stefanie; Kuchenbecker, Leon; Marz, Manja; Stoye, Jens; Böcker, Sebastian

2016-11-16

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in <40 min. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

MECP2 variation in Rett syndrome-An overview of current coverage of genetic and phenotype data within existing databases.

PubMed

Townend, Gillian S; Ehrhart, Friederike; van Kranen, Henk J; Wilkinson, Mark; Jacobsen, Annika; Roos, Marco; Willighagen, Egon L; van Enckevort, David; Evelo, Chris T; Curfs, Leopold M G

2018-04-27

Rett syndrome (RTT) is a monogenic rare disorder that causes severe neurological problems. In most cases, it results from a loss-of-function mutation in the gene encoding methyl-CPG-binding protein 2 (MECP2). Currently, about 900 unique MECP2 variations (benign and pathogenic) have been identified and it is suspected that the different mutations contribute to different levels of disease severity. For researchers and clinicians, it is important that genotype-phenotype information is available to identify disease-causing mutations for diagnosis, to aid in clinical management of the disorder, and to provide counseling for parents. In this study, 13 genotype-phenotype databases were surveyed for their general functionality and availability of RTT-specific MECP2 variation data. For each database, we investigated findability and interoperability alongside practical user functionality, and type and amount of genetic and phenotype data. The main conclusions are that, as well as being challenging to find these databases and specific MECP2 variants held within, interoperability is as yet poorly developed and requires effort to search across databases. Nevertheless, we found several thousand online database entries for MECP2 variations and their associated phenotypes, diagnosis, or predicted variant effects, which is a good starting point for researchers and clinicians who want to provide, annotate, and use the data. © 2018 The Authors. Human Mutation published by Wiley Periodicals, Inc.

GrainGenes: Changing Times, Changing Databases, Digital Evolution.

USDA-ARS?s Scientific Manuscript database

The GrainGenes database is one of few agricultural databases that had an early start on the Internet and that has changed with the times. Initial goals were to collect a wide range of data relating to the developing maps and attributes of small grains crops, and to make them easily accessible. The ...

Database resources of the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Barrett, Tanya; Benson, Dennis A.; Bryant, Stephen H.; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M.; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Geer, Lewis Y.; Helmberg, Wolfgang; Kapustin, Yuri; Kenton, David L.; Khovayko, Oleg; Lipman, David J.; Madden, Thomas L.; Maglott, Donna R.; Ostell, James; Pruitt, Kim D.; Schuler, Gregory D.; Schriml, Lynn M.; Sequeira, Edwin; Sherry, Stephen T.; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Suzek, Tugba O.; Tatusov, Roman; Tatusova, Tatiana A.; Wagner, Lukas; Yaschenko, Eugene

2006-01-01

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups, Retroviral Genotyping Tools, HIV-1, Human Protein Interaction Database, SAGEmap, Gene Expression Omnibus, Entrez Probe, GENSAT, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of the resources can be accessed through the NCBI home page at: . PMID:16381840

Database resources of the National Center for Biotechnology Information.

PubMed

Sayers, Eric W; Barrett, Tanya; Benson, Dennis A; Bolton, Evan; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; Dicuccio, Michael; Federhen, Scott; Feolo, Michael; Fingerman, Ian M; Geer, Lewis Y; Helmberg, Wolfgang; Kapustin, Yuri; Krasnov, Sergey; Landsman, David; Lipman, David J; Lu, Zhiyong; Madden, Thomas L; Madej, Tom; Maglott, Donna R; Marchler-Bauer, Aron; Miller, Vadim; Karsch-Mizrachi, Ilene; Ostell, James; Panchenko, Anna; Phan, Lon; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Stephen T; Shumway, Martin; Sirotkin, Karl; Slotta, Douglas; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A; Wagner, Lukas; Wang, Yanli; Wilbur, W John; Yaschenko, Eugene; Ye, Jian

2012-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Website. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Probe, Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Database resources of the National Center for Biotechnology Information

PubMed Central

Acland, Abigail; Agarwala, Richa; Barrett, Tanya; Beck, Jeff; Benson, Dennis A.; Bollin, Colleen; Bolton, Evan; Bryant, Stephen H.; Canese, Kathi; Church, Deanna M.; Clark, Karen; DiCuccio, Michael; Dondoshansky, Ilya; Federhen, Scott; Feolo, Michael; Geer, Lewis Y.; Gorelenkov, Viatcheslav; Hoeppner, Marilu; Johnson, Mark; Kelly, Christopher; Khotomlianski, Viatcheslav; Kimchi, Avi; Kimelman, Michael; Kitts, Paul; Krasnov, Sergey; Kuznetsov, Anatoliy; Landsman, David; Lipman, David J.; Lu, Zhiyong; Madden, Thomas L.; Madej, Tom; Maglott, Donna R.; Marchler-Bauer, Aron; Karsch-Mizrachi, Ilene; Murphy, Terence; Ostell, James; O'Sullivan, Christopher; Panchenko, Anna; Phan, Lon; Pruitt, Don Preussm Kim D.; Rubinstein, Wendy; Sayers, Eric W.; Schneider, Valerie; Schuler, Gregory D.; Sequeira, Edwin; Sherry, Stephen T.; Shumway, Martin; Sirotkin, Karl; Siyan, Karanjit; Slotta, Douglas; Soboleva, Alexandra; Soussov, Vladimir; Starchenko, Grigory; Tatusova, Tatiana A.; Trawick, Bart W.; Vakatov, Denis; Wang, Yanli; Ward, Minghong; John Wilbur, W.; Yaschenko, Eugene; Zbicz, Kerry

2014-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, PubReader, Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link, Primer-BLAST, COBALT, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, the Genetic Testing Registry, Genome and related tools, the Map Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, ClinVar, MedGen, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Probe, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool, Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All these resources can be accessed through the NCBI home page. PMID:24259429

Database resources of the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Church, Deanna M.; Lash, Alex E.; Leipe, Detlef D.; Madden, Thomas L.; Pontius, Joan U.; Schuler, Gregory D.; Schriml, Lynn M.; Tatusova, Tatiana A.; Wagner, Lukas; Rapp, Barbara A.

2001-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI’s Web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, GeneMap’99, Human–Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, Cancer Genome Anatomy Project (CGAP), SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheri­tance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov. PMID:11125038

A Novel Approach: Chemical Relational Databases, and the Role of the ISSCAN Database on Assessing Chemical Carcinogenity

EPA Science Inventory

Mutagenicity and carcinogenicity databases are crucial resources for toxicologists and regulators involved in chemicals risk assessment. Until recently, existing public toxicity databases have been constructed primarily as "look-up-tables" of existing data, and most often did no...

Follicle Online: an integrated database of follicle assembly, development and ovulation.

PubMed

Hua, Juan; Xu, Bo; Yang, Yifan; Ban, Rongjun; Iqbal, Furhan; Cooke, Howard J; Zhang, Yuanwei; Shi, Qinghua

2015-01-01

Folliculogenesis is an important part of ovarian function as it provides the oocytes for female reproductive life. Characterizing genes/proteins involved in folliculogenesis is fundamental for understanding the mechanisms associated with this biological function and to cure the diseases associated with folliculogenesis. A large number of genes/proteins associated with folliculogenesis have been identified from different species. However, no dedicated public resource is currently available for folliculogenesis-related genes/proteins that are validated by experiments. Here, we are reporting a database 'Follicle Online' that provides the experimentally validated gene/protein map of the folliculogenesis in a number of species. Follicle Online is a web-based database system for storing and retrieving folliculogenesis-related experimental data. It provides detailed information for 580 genes/proteins (from 23 model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Mesocricetus auratus, Bos Taurus, Drosophila and Xenopus laevis) that have been reported to be involved in folliculogenesis, POF (premature ovarian failure) and PCOS (polycystic ovary syndrome). The literature was manually curated from more than 43,000 published articles (till 1 March 2014). The Follicle Online database is implemented in PHP + MySQL + JavaScript and this user-friendly web application provides access to the stored data. In summary, we have developed a centralized database that provides users with comprehensive information about genes/proteins involved in folliculogenesis. This database can be accessed freely and all the stored data can be viewed without any registration. Database URL: http://mcg.ustc.edu.cn/sdap1/follicle/index.php © The Author(s) 2015. Published by Oxford University Press.

Follicle Online: an integrated database of follicle assembly, development and ovulation

PubMed Central

Hua, Juan; Xu, Bo; Yang, Yifan; Ban, Rongjun; Iqbal, Furhan; Zhang, Yuanwei; Shi, Qinghua

2015-01-01

Folliculogenesis is an important part of ovarian function as it provides the oocytes for female reproductive life. Characterizing genes/proteins involved in folliculogenesis is fundamental for understanding the mechanisms associated with this biological function and to cure the diseases associated with folliculogenesis. A large number of genes/proteins associated with folliculogenesis have been identified from different species. However, no dedicated public resource is currently available for folliculogenesis-related genes/proteins that are validated by experiments. Here, we are reporting a database ‘Follicle Online’ that provides the experimentally validated gene/protein map of the folliculogenesis in a number of species. Follicle Online is a web-based database system for storing and retrieving folliculogenesis-related experimental data. It provides detailed information for 580 genes/proteins (from 23 model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Mesocricetus auratus, Bos Taurus, Drosophila and Xenopus laevis) that have been reported to be involved in folliculogenesis, POF (premature ovarian failure) and PCOS (polycystic ovary syndrome). The literature was manually curated from more than 43 000 published articles (till 1 March 2014). The Follicle Online database is implemented in PHP + MySQL + JavaScript and this user-friendly web application provides access to the stored data. In summary, we have developed a centralized database that provides users with comprehensive information about genes/proteins involved in folliculogenesis. This database can be accessed freely and all the stored data can be viewed without any registration. Database URL: http://mcg.ustc.edu.cn/sdap1/follicle/index.php PMID:25931457

Gene discovery in EST sequences from the wheat leaf rust fungus Puccinia triticina sexual spores, asexual spores and haustoria, compared to other rust and corn smut fungi

PubMed Central

2011-01-01

Background Rust fungi are biotrophic basidiomycete plant pathogens that cause major diseases on plants and trees world-wide, affecting agriculture and forestry. Their biotrophic nature precludes many established molecular genetic manipulations and lines of research. The generation of genomic resources for these microbes is leading to novel insights into biology such as interactions with the hosts and guiding directions for breakthrough research in plant pathology. Results To support gene discovery and gene model verification in the genome of the wheat leaf rust fungus, Puccinia triticina (Pt), we have generated Expressed Sequence Tags (ESTs) by sampling several life cycle stages. We focused on several spore stages and isolated haustorial structures from infected wheat, generating 17,684 ESTs. We produced sequences from both the sexual (pycniospores, aeciospores and teliospores) and asexual (germinated urediniospores) stages of the life cycle. From pycniospores and aeciospores, produced by infecting the alternate host, meadow rue (Thalictrum speciosissimum), 4,869 and 1,292 reads were generated, respectively. We generated 3,703 ESTs from teliospores produced on the senescent primary wheat host. Finally, we generated 6,817 reads from haustoria isolated from infected wheat as well as 1,003 sequences from germinated urediniospores. Along with 25,558 previously generated ESTs, we compiled a database of 13,328 non-redundant sequences (4,506 singlets and 8,822 contigs). Fungal genes were predicted using the EST version of the self-training GeneMarkS algorithm. To refine the EST database, we compared EST sequences by BLASTN to a set of 454 pyrosequencing-generated contigs and Sanger BAC-end sequences derived both from the Pt genome, and to ESTs and genome reads from wheat. A collection of 6,308 fungal genes was identified and compared to sequences of the cereal rusts, Puccinia graminis f. sp. tritici (Pgt) and stripe rust, P. striiformis f. sp. tritici (Pst), and poplar leaf rust Melampsora species, and the corn smut fungus, Ustilago maydis (Um). While extensive homologies were found, many genes appeared novel and species-specific; over 40% of genes did not match any known sequence in existing databases. Focusing on spore stages, direct comparison to Um identified potential functional homologs, possibly allowing heterologous functional analysis in that model fungus. Many potentially secreted protein genes were identified by similarity searches against genes and proteins of Pgt and Melampsora spp., revealing apparent orthologs. Conclusions The current set of Pt unigenes contributes to gene discovery in this major cereal pathogen and will be invaluable for gene model verification in the genome sequence. PMID:21435244

PIECE 2.0: an update for the plant gene structure comparison and evolution database

USDA-ARS?s Scientific Manuscript database

PIECE (Plant Intron Exon Comparision and Evolution) is a web-accessible database that houses intron and exon information of plant genes. PIECE serves as a resource for biologists interested in comparing intron-exon organization and provides valuable insights into the evolution of gene structure in ...

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

A dehydration-inducible gene in the truffle Tuber borchii identifies a novel group of dehydrins

PubMed Central

Abba', Simona; Ghignone, Stefano; Bonfante, Paola

2006-01-01

Background The expressed sequence tag M6G10 was originally isolated from a screening for differentially expressed transcripts during the reproductive stage of the white truffle Tuber borchii. mRNA levels for M6G10 increased dramatically during fruiting body maturation compared to the vegetative mycelial stage. Results Bioinformatics tools, phylogenetic analysis and expression studies were used to support the hypothesis that this sequence, named TbDHN1, is the first dehydrin (DHN)-like coding gene isolated in fungi. Homologs of this gene, all defined as "coding for hypothetical proteins" in public databases, were exclusively found in ascomycetous fungi and in plants. Although complete (or almost complete) fungal genomes and EST collections of some Basidiomycota and Glomeromycota are already available, DHN-like proteins appear to be represented only in Ascomycota. A new and previously uncharacterized conserved signature pattern was identified and proposed to Uniprot database as the main distinguishing feature of this new group of DHNs. Expression studies provide experimental evidence of a transcript induction of TbDHN1 during cellular dehydration. Conclusion Expression pattern and sequence similarities to known plant DHNs indicate that TbDHN1 is the first characterized DHN-like protein in fungi. The high similarity of TbDHN1 with homolog coding sequences implies the existence of a novel fungal/plant group of LEA Class II proteins characterized by a previously undescribed signature pattern. PMID:16512918

BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis

PubMed Central

Bagger, Frederik Otzen; Sasivarevic, Damir; Sohi, Sina Hadi; Laursen, Linea Gøricke; Pundhir, Sachin; Sønderby, Casper Kaae; Winther, Ole; Rapin, Nicolas; Porse, Bo T.

2016-01-01

Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely inaccessible. Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival. To address these shortcomings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles from FACS sorted healthy and malignant haematopoietic cells. A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan–Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database. The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool. Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia. Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well-defined haematopoietic cellular space. PMID:26507857

MultitaskProtDB: a database of multitasking proteins

PubMed Central

Hernández, Sergio; Ferragut, Gabriela; Amela, Isaac; Perez-Pons, JosepAntoni; Piñol, Jaume; Mozo-Villarias, Angel; Cedano, Juan; Querol, Enrique

2014-01-01

We have compiled MultitaskProtDB, available online at http://wallace.uab.es/multitask, to provide a repository where the many multitasking proteins found in the literature can be stored. Multitasking or moonlighting is the capability of some proteins to execute two or more biological functions. Usually, multitasking proteins are experimentally revealed by serendipity. This ability of proteins to perform multitasking functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Even so, the study of this phenomenon is complex because, among other things, there is no database of moonlighting proteins. The existence of such a tool facilitates the collection and dissemination of these important data. This work reports the database, MultitaskProtDB, which is designed as a friendly user web page containing >288 multitasking proteins with their NCBI and UniProt accession numbers, canonical and additional biological functions, monomeric/oligomeric states, PDB codes when available and bibliographic references. This database also serves to gain insight into some characteristics of multitasking proteins such as frequencies of the different pairs of functions, phylogenetic conservation and so forth. PMID:24253302

Short communication: Evaluation of MALDI-TOF mass spectrometry and a custom reference spectra expanded database for the identification of bovine-associated coagulase-negative staphylococci.

PubMed

Cameron, M; Perry, J; Middleton, J R; Chaffer, M; Lewis, J; Keefe, G P

2018-01-01

This study evaluated MALDI-TOF mass spectrometry and a custom reference spectra expanded database for the identification of bovine-associated coagulase-negative staphylococci (CNS). A total of 861 CNS isolates were used in the study, covering 21 different CNS species. The majority of the isolates were previously identified by rpoB gene sequencing (n = 804) and the remainder were identified by sequencing of hsp60 (n = 56) and tuf (n = 1). The genotypic identification was considered the gold standard identification. Using a direct transfer protocol and the existing commercial database, MALDI-TOF mass spectrometry showed a typeability of 96.5% (831/861) and an accuracy of 99.2% (824/831). Using a custom reference spectra expanded database, which included an additional 13 in-house created reference spectra, isolates were identified by MALDI-TOF mass spectrometry with 99.2% (854/861) typeability and 99.4% (849/854) accuracy. Overall, MALDI-TOF mass spectrometry using the direct transfer method was shown to be a highly reliable tool for the identification of bovine-associated CNS. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

pGenN, a gene normalization tool for plant genes and proteins in scientific literature.

PubMed

Ding, Ruoyao; Arighi, Cecilia N; Lee, Jung-Youn; Wu, Cathy H; Vijay-Shanker, K

2015-01-01

Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/).

Molecular Characterization, Gene Evolution, and Expression Analysis of the Fructose-1, 6-bisphosphate Aldolase (FBA) Gene Family in Wheat (Triticum aestivum L.)

PubMed Central

Lv, Geng-Yin; Guo, Xiao-Guang; Xie, Li-Ping; Xie, Chang-Gen; Zhang, Xiao-Hong; Yang, Yuan; Xiao, Lei; Tang, Yu-Ying; Pan, Xing-Lai; Guo, Ai-Guang; Xu, Hong

2017-01-01

Fructose-1, 6-bisphosphate aldolase (FBA) is a key plant enzyme that is involved in glycolysis, gluconeogenesis, and the Calvin cycle. It plays significant roles in biotic and abiotic stress responses, as well as in regulating growth and development processes. In the present paper, 21 genes encoding TaFBA isoenzymes were identified, characterized, and categorized into three groups: class I chloroplast/plastid FBA (CpFBA), class I cytosol FBA (cFBA), and class II chloroplast/plastid FBA. By using a prediction online database and genomic PCR analysis of Chinese Spring nulli-tetrasomic lines, we have confirmed the chromosomal location of these genes in 12 chromosomes of four homologous groups. Sequence and genomic structure analysis revealed the high identity of the allelic TaFBA genes and the origin of different TaFBA genes. Numerous putative environment stimulus-responsive cis-elements have been identified in 1,500-bp regions of TaFBA gene promoters, of which the most abundant are the light-regulated elements (LREs). Phylogenetic reconstruction using the deduced protein sequence of 245 FBA genes indicated an independent evolutionary pathway for the class I and class II groups. Although, earlier studies have indicated that class II FBA only occurs in prokaryote and fungi, our results have demonstrated that a few class II CpFBAs exist in wheat and other closely related species. Class I TaFBA was predicted to be tetramers and class II to be dimers. Gene expression analysis based on microarray and transcriptome databases suggested the distinct role of TaFBAs in different tissues and developmental stages. The TaFBA 4–9 genes were highly expressed in leaves and might play important roles in wheat development. The differential expression patterns of the TaFBA genes in light/dark and a few abiotic stress conditions were also analyzed. The results suggested that LRE cis-elements of TaFBA gene promoters were not directly related to light responses. Most TaFBA genes had higher expression levels in the roots than in the shoots when under various stresses. Class I cytosol TaFBA genes, particularly TaFBA10/12/18 and TaFBA13/16, and three class II TaFBA genes are involved in responses to various abiotic stresses. Class I CpFBA genes in wheat are apparently sensitive to different stress conditions. PMID:28659962

dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.

PubMed

Zhang, Shuyun; Deng, Libin; Jia, Qiyue; Huang, Shaoting; Gu, Junwang; Zhou, Fankun; Gao, Meng; Sun, Xinyi; Feng, Chang; Fan, Guangqin

2017-11-16

Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed. Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain. This database, dbMDEGA ( https://dbmdega.shinyapps.io/dbMDEGA/ ), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder. This new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders.

The Androgen Receptor Gene Mutations Database.

PubMed

Gottlieb, B; Lehvaslaiho, H; Beitel, L K; Lumbroso, R; Pinsky, L; Trifiro, M

1998-01-01

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 272 to 309 in the past year. We have expanded the database: (i) by giving each entry an accession number; (ii) by adding information on the length of polymorphic polyglutamine (polyGln) and polyglycine (polyGly) tracts in exon 1; (iii) by adding information on large gene deletions; (iv) by providing a direct link with a completely searchable database (courtesy EMBL-European Bioinformatics Institute). The addition of the exon 1 polymorphisms is discussed in light of their possible relevance as markers for predisposition to prostate or breast cancer. The database is also available on the internet (http://www.mcgill. ca/androgendb/ ), from EMBL-European Bioinformatics Institute (ftp. ebi.ac.uk/pub/databases/androgen ), or as a Macintosh FilemakerPro or Word file (MC33@musica.mcgill.ca).

The Androgen Receptor Gene Mutations Database.

PubMed Central

Gottlieb, B; Lehvaslaiho, H; Beitel, L K; Lumbroso, R; Pinsky, L; Trifiro, M

1998-01-01

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 272 to 309 in the past year. We have expanded the database: (i) by giving each entry an accession number; (ii) by adding information on the length of polymorphic polyglutamine (polyGln) and polyglycine (polyGly) tracts in exon 1; (iii) by adding information on large gene deletions; (iv) by providing a direct link with a completely searchable database (courtesy EMBL-European Bioinformatics Institute). The addition of the exon 1 polymorphisms is discussed in light of their possible relevance as markers for predisposition to prostate or breast cancer. The database is also available on the internet (http://www.mcgill. ca/androgendb/ ), from EMBL-European Bioinformatics Institute (ftp. ebi.ac.uk/pub/databases/androgen ), or as a Macintosh FilemakerPro or Word file (MC33@musica.mcgill.ca). PMID:9399843

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE.

PubMed

Névéol, Aurélie; Wilbur, W John; Lu, Zhiyong

2012-01-01

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE

PubMed Central

Névéol, Aurélie; Wilbur, W. John; Lu, Zhiyong

2012-01-01

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/ PMID:22685160

VitisExpDB: a database resource for grape functional genomics.

PubMed

Doddapaneni, Harshavardhan; Lin, Hong; Walker, M Andrew; Yao, Jiqiang; Civerolo, Edwin L

2008-02-28

The family Vitaceae consists of many different grape species that grow in a range of climatic conditions. In the past few years, several studies have generated functional genomic information on different Vitis species and cultivars, including the European grape vine, Vitis vinifera. Our goal is to develop a comprehensive web data source for Vitaceae. VitisExpDB is an online MySQL-PHP driven relational database that houses annotated EST and gene expression data for V. vinifera and non-vinifera grape species and varieties. Currently, the database stores approximately 320,000 EST sequences derived from 8 species/hybrids, their annotation (BLAST top match) details and Gene Ontology based structured vocabulary. Putative homologs for each EST in other species and varieties along with information on their percent nucleotide identities, phylogenetic relationship and common primers can be retrieved. The database also includes information on probe sequence and annotation features of the high density 60-mer gene expression chip consisting of approximately 20,000 non-redundant set of ESTs. Finally, the database includes 14 processed global microarray expression profile sets. Data from 12 of these expression profile sets have been mapped onto metabolic pathways. A user-friendly web interface with multiple search indices and extensively hyperlinked result features that permit efficient data retrieval has been developed. Several online bioinformatics tools that interact with the database along with other sequence analysis tools have been added. In addition, users can submit their ESTs to the database. The developed database provides genomic resource to grape community for functional analysis of genes in the collection and for the grape genome annotation and gene function identification. The VitisExpDB database is available through our website http://cropdisease.ars.usda.gov/vitis_at/main-page.htm.

VitisExpDB: A database resource for grape functional genomics

PubMed Central

Doddapaneni, Harshavardhan; Lin, Hong; Walker, M Andrew; Yao, Jiqiang; Civerolo, Edwin L

2008-01-01

Background The family Vitaceae consists of many different grape species that grow in a range of climatic conditions. In the past few years, several studies have generated functional genomic information on different Vitis species and cultivars, including the European grape vine, Vitis vinifera. Our goal is to develop a comprehensive web data source for Vitaceae. Description VitisExpDB is an online MySQL-PHP driven relational database that houses annotated EST and gene expression data for V. vinifera and non-vinifera grape species and varieties. Currently, the database stores ~320,000 EST sequences derived from 8 species/hybrids, their annotation (BLAST top match) details and Gene Ontology based structured vocabulary. Putative homologs for each EST in other species and varieties along with information on their percent nucleotide identities, phylogenetic relationship and common primers can be retrieved. The database also includes information on probe sequence and annotation features of the high density 60-mer gene expression chip consisting of ~20,000 non-redundant set of ESTs. Finally, the database includes 14 processed global microarray expression profile sets. Data from 12 of these expression profile sets have been mapped onto metabolic pathways. A user-friendly web interface with multiple search indices and extensively hyperlinked result features that permit efficient data retrieval has been developed. Several online bioinformatics tools that interact with the database along with other sequence analysis tools have been added. In addition, users can submit their ESTs to the database. Conclusion The developed database provides genomic resource to grape community for functional analysis of genes in the collection and for the grape genome annotation and gene function identification. The VitisExpDB database is available through our website . PMID:18307813

Discovering gene annotations in biomedical text databases

PubMed Central

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-01-01

Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values. PMID:18325104

Discovering gene annotations in biomedical text databases.

PubMed

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-03-06

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

HNdb: an integrated database of gene and protein information on head and neck squamous cell carcinoma

PubMed Central

Henrique, Tiago; José Freitas da Silveira, Nelson; Henrique Cunha Volpato, Arthur; Mioto, Mayra Mataruco; Carolina Buzzo Stefanini, Ana; Bachir Fares, Adil; Gustavo da Silva Castro Andrade, João; Masson, Carolina; Verónica Mendoza López, Rossana; Daumas Nunes, Fabio; Paulo Kowalski, Luis; Severino, Patricia; Tajara, Eloiza Helena

2016-01-01

The total amount of scientific literature has grown rapidly in recent years. Specifically, there are several million citations in the field of cancer. This makes it difficult, if not impossible, to manually retrieve relevant information on the mechanisms that govern tumor behavior or the neoplastic process. Furthermore, cancer is a complex disease or, more accurately, a set of diseases. The heterogeneity that permeates many tumors is particularly evident in head and neck (HN) cancer, one of the most common types of cancer worldwide. In this study, we present HNdb, a free database that aims to provide a unified and comprehensive resource of information on genes and proteins involved in HN squamous cell carcinoma, covering data on genomics, transcriptomics, proteomics, literature citations and also cross-references of external databases. Different literature searches of MEDLINE abstracts were performed using specific Medical Subject Headings (MeSH terms) for oral, oropharyngeal, hypopharyngeal and laryngeal squamous cell carcinomas. A curated gene-to-publication assignment yielded a total of 1370 genes related to HN cancer. The diversity of results allowed identifying novel and mostly unexplored gene associations, revealing, for example, that processes linked to response to steroid hormone stimulus are significantly enriched in genes related to HN carcinomas. Thus, our database expands the possibilities for gene networks investigation, providing potential hypothesis to be tested. Database URL: http://www.gencapo.famerp.br/hndb PMID:27013077

Androgen-responsive gene database: integrated knowledge on androgen-responsive genes.

PubMed

Jiang, Mei; Ma, Yunsheng; Chen, Congcong; Fu, Xuping; Yang, Shu; Li, Xia; Yu, Guohua; Mao, Yumin; Xie, Yi; Li, Yao

2009-11-01

Androgen signaling plays an important role in many biological processes. Androgen Responsive Gene Database (ARGDB) is devoted to providing integrated knowledge on androgen-controlled genes. Gene records were collected on the basis of PubMed literature collections. More than 6000 abstracts and 950 original publications were manually screened, leading to 1785 human genes, 993 mouse genes, and 583 rat genes finally included in the database. All the collected genes were experimentally proved to be regulated by androgen at the expression level or to contain androgen-responsive regions. For each gene important details of the androgen regulation experiments were collected from references, such as expression change, androgen-responsive sequence, response time, tissue/cell type, experimental method, ligand identity, and androgen amount, which will facilitate further evaluation by researchers. Furthermore, the database was integrated with multiple annotation resources, including National Center for Biotechnology Information, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway, to reveal the biological characteristics and significance of androgen-regulated genes. The ARGDB web site is mainly composed of the Browse, Search, Element Scan, and Submission modules. It is user friendly and freely accessible at http://argdb.fudan.edu.cn. Preliminary analysis of the collected data was performed. Many disease pathways, such as prostate carcinogenesis, were found to be enriched in androgen-regulated genes. The discovered androgen-response motifs were similar to those in previous reports. The analysis results are displayed in the web site. In conclusion, ARGDB provides a unified gateway to storage, retrieval, and update of information on androgen-regulated genes.

Breeding and Genetics Symposium: networks and pathways to guide genomic selection.

PubMed

Snelling, W M; Cushman, R A; Keele, J W; Maltecca, C; Thomas, M G; Fortes, M R S; Reverter, A

2013-02-01

Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits.

Kinase Pathway Database: An Integrated Protein-Kinase and NLP-Based Protein-Interaction Resource

PubMed Central

Koike, Asako; Kobayashi, Yoshiyuki; Takagi, Toshihisa

2003-01-01

Protein kinases play a crucial role in the regulation of cellular functions. Various kinds of information about these molecules are important for understanding signaling pathways and organism characteristics. We have developed the Kinase Pathway Database, an integrated database involving major completely sequenced eukaryotes. It contains the classification of protein kinases and their functional conservation, ortholog tables among species, protein–protein, protein–gene, and protein–compound interaction data, domain information, and structural information. It also provides an automatic pathway graphic image interface. The protein, gene, and compound interactions are automatically extracted from abstracts for all genes and proteins by natural-language processing (NLP).The method of automatic extraction uses phrase patterns and the GENA protein, gene, and compound name dictionary, which was developed by our group. With this database, pathways are easily compared among species using data with more than 47,000 protein interactions and protein kinase ortholog tables. The database is available for querying and browsing at http://kinasedb.ontology.ims.u-tokyo.ac.jp/. PMID:12799355

LGscore: A method to identify disease-related genes using biological literature and Google data.

PubMed

Kim, Jeongwoo; Kim, Hyunjin; Yoon, Youngmi; Park, Sanghyun

2015-04-01

Since the genome project in 1990s, a number of studies associated with genes have been conducted and researchers have confirmed that genes are involved in disease. For this reason, the identification of the relationships between diseases and genes is important in biology. We propose a method called LGscore, which identifies disease-related genes using Google data and literature data. To implement this method, first, we construct a disease-related gene network using text-mining results. We then extract gene-gene interactions based on co-occurrences in abstract data obtained from PubMed, and calculate the weights of edges in the gene network by means of Z-scoring. The weights contain two values: the frequency and the Google search results. The frequency value is extracted from literature data, and the Google search result is obtained using Google. We assign a score to each gene through a network analysis. We assume that genes with a large number of links and numerous Google search results and frequency values are more likely to be involved in disease. For validation, we investigated the top 20 inferred genes for five different diseases using answer sets. The answer sets comprised six databases that contain information on disease-gene relationships. We identified a significant number of disease-related genes as well as candidate genes for Alzheimer's disease, diabetes, colon cancer, lung cancer, and prostate cancer. Our method was up to 40% more accurate than existing methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Universal and idiosyncratic characteristic lengths in bacterial genomes

NASA Astrophysics Data System (ADS)

Junier, Ivan; Frémont, Paul; Rivoire, Olivier

2018-05-01

In condensed matter physics, simplified descriptions are obtained by coarse-graining the features of a system at a certain characteristic length, defined as the typical length beyond which some properties are no longer correlated. From a physics standpoint, in vitro DNA has thus a characteristic length of 300 base pairs (bp), the Kuhn length of the molecule beyond which correlations in its orientations are typically lost. From a biology standpoint, in vivo DNA has a characteristic length of 1000 bp, the typical length of genes. Since bacteria live in very different physico-chemical conditions and since their genomes lack translational invariance, whether larger, universal characteristic lengths exist is a non-trivial question. Here, we examine this problem by leveraging the large number of fully sequenced genomes available in public databases. By analyzing GC content correlations and the evolutionary conservation of gene contexts (synteny) in hundreds of bacterial chromosomes, we conclude that a fundamental characteristic length around 10–20 kb can be defined. This characteristic length reflects elementary structures involved in the coordination of gene expression, which are present all along the genome of nearly all bacteria. Technically, reaching this conclusion required us to implement methods that are insensitive to the presence of large idiosyncratic genomic features, which may co-exist along these fundamental universal structures.

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

PubMed Central

Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A.; Sanz, Ferran; Furlong, Laura I.

2011-01-01

Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. Availability The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download. PMID:21695124

Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases.

PubMed

Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A; Sanz, Ferran; Furlong, Laura I

2011-01-01

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

Piece2.0: an update for the pant gene structure comparison and evolution database

USDA-ARS?s Scientific Manuscript database

PIECE (Plant Intron Exon Comparison and Evolution) is a web-accessible database that houses intron and exon information of plant genes. PIECE serves as a resource for biologists interested in comparing intron–exon organization and provides valuable insights into the evolution of gene structure in pl...

rrndb: the Ribosomal RNA Operon Copy Number Database

PubMed Central

Klappenbach, Joel A.; Saxman, Paul R.; Cole, James R.; Schmidt, Thomas M.

2001-01-01

The Ribosomal RNA Operon Copy Number Database (rrndb) is an Internet-accessible database containing annotated information on rRNA operon copy number among prokaryotes. Gene redundancy is uncommon in prokaryotic genomes, yet the rRNA genes can vary from one to as many as 15 copies. Despite the widespread use of 16S rRNA gene sequences for identification of prokaryotes, information on the number and sequence of individual rRNA genes in a genome is not readily accessible. In an attempt to understand the evolutionary implications of rRNA operon redundancy, we have created a phylogenetically arranged report on rRNA gene copy number for a diverse collection of prokaryotic microorganisms. Each entry (organism) in the rrndb contains detailed information linked directly to external websites including the Ribosomal Database Project, GenBank, PubMed and several culture collections. Data contained in the rrndb will be valuable to researchers investigating microbial ecology and evolution using 16S rRNA gene sequences. The rrndb web site is directly accessible on the WWW at http://rrndb.cme.msu.edu. PMID:11125085

Down-weighting overlapping genes improves gene set analysis

PubMed Central

2012-01-01

Background The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. Results In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method Pathway Analysis with Down-weighting of Overlapping Genes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. Conclusions PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org. PMID:22713124

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.

MAGIC database and interfaces: an integrated package for gene discovery and expression.

PubMed

Cordonnier-Pratt, Marie-Michèle; Liang, Chun; Wang, Haiming; Kolychev, Dmitri S; Sun, Feng; Freeman, Robert; Sullivan, Robert; Pratt, Lee H

2004-01-01

The rapidly increasing rate at which biological data is being produced requires a corresponding growth in relational databases and associated tools that can help laboratories contend with that data. With this need in mind, we describe here a Modular Approach to a Genomic, Integrated and Comprehensive (MAGIC) Database. This Oracle 9i database derives from an initial focus in our laboratory on gene discovery via production and analysis of expressed sequence tags (ESTs), and subsequently on gene expression as assessed by both EST clustering and microarrays. The MAGIC Gene Discovery portion of the database focuses on information derived from DNA sequences and on its biological relevance. In addition to MAGIC SEQ-LIMS, which is designed to support activities in the laboratory, it contains several additional subschemas. The latter include MAGIC Admin for database administration, MAGIC Sequence for sequence processing as well as sequence and clone attributes, MAGIC Cluster for the results of EST clustering, MAGIC Polymorphism in support of microsatellite and single-nucleotide-polymorphism discovery, and MAGIC Annotation for electronic annotation by BLAST and BLAT. The MAGIC Microarray portion is a MIAME-compliant database with two components at present. These are MAGIC Array-LIMS, which makes possible remote entry of all information into the database, and MAGIC Array Analysis, which provides data mining and visualization. Because all aspects of interaction with the MAGIC Database are via a web browser, it is ideally suited not only for individual research laboratories but also for core facilities that serve clients at any distance.

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.

Cloning of a gene (RIG-G) associated with retinoic acid-induced differentiation of acute promyelocytic leukemia cells and representing a new member of a family of interferon-stimulated genes

PubMed Central

Yu, Man; Tong, Jian-Hua; Mao, Mao; Kan, Li-Xin; Liu, Meng-Min; Sun, Yi-Wu; Fu, Gang; Jing, Yong-Kui; Yu, Long; Lepaslier, Denis; Lanotte, Michel; Wang, Zhen-Yi; Chen, Zhu; Waxman, Samuel; Wang, Ya-Xin; Tan, Jia-Zhen; Chen, Sai-Juan

1997-01-01

In a cell line (NB4) derived from a patient with acute promyelocytic leukemia, all-trans-retinoic acid (ATRA) and interferon (IFN) induce the expression of a novel gene we call RIG-G (for retinoic acid-induced gene G). This gene codes for a 58-kDa protein containing 490 amino acids with several potential sites for post-translational modification. In untreated NB4 cells, the expression of RIG-G is undetectable. ATRA treatment induces the transcriptional expression of RIG-G relatively late (12–24 hr) in a protein synthesis-dependent manner, whereas IFN-α induces its expression early (30 min to 3 hr). Database search has revealed a high-level homology between RIG-G and several IFN-stimulated genes in human (ISG54K, ISG56K, and IFN-inducible and retinoic acid-inducible 58K gene) and some other species, defining a well conserved gene family. The gene is composed of two exons and has been mapped by fluorescence in situ hybridization to chromosome 10q24, where two other human IFN-stimulated gene members are localized. A synergistic induction of RIG-G expression in NB4 cells by combined treatment with ATRA and IFNs suggests that a collaboration exists between their respective signaling pathways. PMID:9207104

An annotated database of Arabidopsis mutants of acyl lipid metabolism

DOE PAGES

McGlew, Kathleen; Shaw, Vincent; Zhang, Meng; ...

2014-12-10

Mutants have played a fundamental role in gene discovery and in understanding the function of genes involved in plant acyl lipid metabolism. The first mutant in Arabidopsis lipid metabolism ( fad4) was described in 1985. Since that time, characterization of mutants in more than 280 genes associated with acyl lipid metabolism has been reported. This review provides a brief background and history on identification of mutants in acyl lipid metabolism, an analysis of the distribution of mutants in different areas of acyl lipid metabolism and presents an annotated database (ARALIPmutantDB) of these mutants. The database provides information on the phenotypesmore » of mutants, pathways and enzymes/proteins associated with the mutants, and allows rapid access via hyperlinks to summaries of information about each mutant and to literature that provides information on the lipid composition of the mutants. Mutants for at least 30 % of the genes in the database have multiple names, which have been compiled here to reduce ambiguities in searches for information. Furthermore, the database should also provide a tool for exploring the relationships between mutants in acyl lipid-related genes and their lipid phenotypes and point to opportunities for further research.« less

Biosynthesis of the active compounds of Isatis indigotica based on transcriptome sequencing and metabolites profiling

PubMed Central

2013-01-01

Backgroud Isatis indigotica is a widely used herb for the clinical treatment of colds, fever, and influenza in Traditional Chinese Medicine (TCM). Various structural classes of compounds have been identified as effective ingredients. However, little is known at genetics level about these active metabolites. In the present study, we performed de novo transcriptome sequencing for the first time to produce a comprehensive dataset of I. indigotica. Results A database of 36,367 unigenes (average length = 1,115.67 bases) was generated by performing transcriptome sequencing. Based on the gene annotation of the transcriptome, 104 unigenes were identified covering most of the catalytic steps in the general biosynthetic pathways of indole, terpenoid, and phenylpropanoid. Subsequently, the organ-specific expression patterns of the genes involved in these pathways, and their responses to methyl jasmonate (MeJA) induction, were investigated. Metabolites profile of effective phenylpropanoid showed accumulation pattern of secondary metabolites were mostly correlated with the transcription of their biosynthetic genes. According to the analysis of UDP-dependent glycosyltransferases (UGT) family, several flavonoids were indicated to exist in I. indigotica and further identified by metabolic profile using UPLC/Q-TOF. Moreover, applying transcriptome co-expression analysis, nine new, putative UGTs were suggested as flavonol glycosyltransferases and lignan glycosyltransferases. Conclusions This database provides a pool of candidate genes involved in biosynthesis of effective metabolites in I. indigotica. Furthermore, the comprehensive analysis and characterization of the significant pathways are expected to give a better insight regarding the diversity of chemical composition, synthetic characteristics, and the regulatory mechanism which operate in this medical herb. PMID:24308360

MADGE: scalable distributed data management software for cDNA microarrays.

PubMed

McIndoe, Richard A; Lanzen, Aaron; Hurtz, Kimberly

2003-01-01

The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

PubMed

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

Clique-based data mining for related genes in a biomedical database.

PubMed

Matsunaga, Tsutomu; Yonemori, Chikara; Tomita, Etsuji; Muramatsu, Masaaki

2009-07-01

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.

DGIdb 3.0: a redesign and expansion of the drug-gene interaction database.

PubMed

Cotto, Kelsy C; Wagner, Alex H; Feng, Yang-Yang; Kiwala, Susanna; Coffman, Adam C; Spies, Gregory; Wollam, Alex; Spies, Nicholas C; Griffith, Obi L; Griffith, Malachi

2018-01-04

The drug-gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug-gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug-gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.

Phylogenomics databases for facilitating functional genomics in rice.

PubMed

Jung, Ki-Hong; Cao, Peijian; Sharma, Rita; Jain, Rashmi; Ronald, Pamela C

2015-12-01

The completion of whole genome sequence of rice (Oryza sativa) has significantly accelerated functional genomics studies. Prior to the release of the sequence, only a few genes were assigned a function each year. Since sequencing was completed in 2005, the rate has exponentially increased. As of 2014, 1,021 genes have been described and added to the collection at The Overview of functionally characterized Genes in Rice online database (OGRO). Despite this progress, that number is still very low compared with the total number of genes estimated in the rice genome. One limitation to progress is the presence of functional redundancy among members of the same rice gene family, which covers 51.6 % of all non-transposable element-encoding genes. There remain a significant portion or rice genes that are not functionally redundant, as reflected in the recovery of loss-of-function mutants. To more accurately analyze functional redundancy in the rice genome, we have developed a phylogenomics databases for six large gene families in rice, including those for glycosyltransferases, glycoside hydrolases, kinases, transcription factors, transporters, and cytochrome P450 monooxygenases. In this review, we introduce key features and applications of these databases. We expect that they will serve as a very useful guide in the post-genomics era of research.

GEneSTATION 1.0: a synthetic resource of diverse evolutionary and functional genomic data for studying the evolution of pregnancy-associated tissues and phenotypes

PubMed Central

Kim, Mara; Cooper, Brian A.; Venkat, Rohit; Phillips, Julie B.; Eidem, Haley R.; Hirbo, Jibril; Nutakki, Sashank; Williams, Scott M.; Muglia, Louis J.; Capra, J. Anthony; Petren, Kenneth; Abbot, Patrick; Rokas, Antonis; McGary, Kriston L.

2016-01-01

Mammalian gestation and pregnancy are fast evolving processes that involve the interaction of the fetal, maternal and paternal genomes. Version 1.0 of the GEneSTATION database (http://genestation.org) integrates diverse types of omics data across mammals to advance understanding of the genetic basis of gestation and pregnancy-associated phenotypes and to accelerate the translation of discoveries from model organisms to humans. GEneSTATION is built using tools from the Generic Model Organism Database project, including the biology-aware database CHADO, new tools for rapid data integration, and algorithms that streamline synthesis and user access. GEneSTATION contains curated life history information on pregnancy and reproduction from 23 high-quality mammalian genomes. For every human gene, GEneSTATION contains diverse evolutionary (e.g. gene age, population genetic and molecular evolutionary statistics), organismal (e.g. tissue-specific gene and protein expression, differential gene expression, disease phenotype), and molecular data types (e.g. Gene Ontology Annotation, protein interactions), as well as links to many general (e.g. Entrez, PubMed) and pregnancy disease-specific (e.g. PTBgene, dbPTB) databases. By facilitating the synthesis of diverse functional and evolutionary data in pregnancy-associated tissues and phenotypes and enabling their quick, intuitive, accurate and customized meta-analysis, GEneSTATION provides a novel platform for comprehensive investigation of the function and evolution of mammalian pregnancy. PMID:26567549

How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database

PubMed Central

Mashiach, R.; Cohen, S.; Kedem, A.; Baron, A.; Zajicek, M.; Feldman, I.; Seidman, D.; Soriano, D.

2018-01-01

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis. PMID:29750165

How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database.

PubMed

Bouaziz, J; Mashiach, R; Cohen, S; Kedem, A; Baron, A; Zajicek, M; Feldman, I; Seidman, D; Soriano, D

2018-01-01

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis.

The National NeuroAIDS Tissue Consortium (NNTC) Database: an integrated database for HIV-related studies

PubMed Central

Cserhati, Matyas F.; Pandey, Sanjit; Beaudoin, James J.; Baccaglini, Lorena; Guda, Chittibabu; Fox, Howard S.

2015-01-01

We herein present the National NeuroAIDS Tissue Consortium-Data Coordinating Center (NNTC-DCC) database, which is the only available database for neuroAIDS studies that contains data in an integrated, standardized form. This database has been created in conjunction with the NNTC, which provides human tissue and biofluid samples to individual researchers to conduct studies focused on neuroAIDS. The database contains experimental datasets from 1206 subjects for the following categories (which are further broken down into subcategories): gene expression, genotype, proteins, endo-exo-chemicals, morphometrics and other (miscellaneous) data. The database also contains a wide variety of downloadable data and metadata for 95 HIV-related studies covering 170 assays from 61 principal investigators. The data represent 76 tissue types, 25 measurement types, and 38 technology types, and reaches a total of 33 017 407 data points. We used the ISA platform to create the database and develop a searchable web interface for querying the data. A gene search tool is also available, which searches for NCBI GEO datasets associated with selected genes. The database is manually curated with many user-friendly features, and is cross-linked to the NCBI, HUGO and PubMed databases. A free registration is required for qualified users to access the database. Database URL: http://nntc-dcc.unmc.edu PMID:26228431

CardioTF, a database of deconstructing transcriptional circuits in the heart system

PubMed Central

2016-01-01

Background: Information on cardiovascular gene transcription is fragmented and far behind the present requirements of the systems biology field. To create a comprehensive source of data for cardiovascular gene regulation and to facilitate a deeper understanding of genomic data, the CardioTF database was constructed. The purpose of this database is to collate information on cardiovascular transcription factors (TFs), position weight matrices (PWMs), and enhancer sequences discovered using the ChIP-seq method. Methods: The Naïve-Bayes algorithm was used to classify literature and identify all PubMed abstracts on cardiovascular development. The natural language learning tool GNAT was then used to identify corresponding gene names embedded within these abstracts. Local Perl scripts were used to integrate and dump data from public databases into the MariaDB management system (MySQL). In-house R scripts were written to analyze and visualize the results. Results: Known cardiovascular TFs from humans and human homologs from fly, Ciona, zebrafish, frog, chicken, and mouse were identified and deposited in the database. PWMs from Jaspar, hPDI, and UniPROBE databases were deposited in the database and can be retrieved using their corresponding TF names. Gene enhancer regions from various sources of ChIP-seq data were deposited into the database and were able to be visualized by graphical output. Besides biocuration, mouse homologs of the 81 core cardiac TFs were selected using a Naïve-Bayes approach and then by intersecting four independent data sources: RNA profiling, expert annotation, PubMed abstracts and phenotype. Discussion: The CardioTF database can be used as a portal to construct transcriptional network of cardiac development. Availability and Implementation: Database URL: http://www.cardiosignal.org/database/cardiotf.html. PMID:27635320

CardioTF, a database of deconstructing transcriptional circuits in the heart system.

PubMed

Zhen, Yisong

2016-01-01

Information on cardiovascular gene transcription is fragmented and far behind the present requirements of the systems biology field. To create a comprehensive source of data for cardiovascular gene regulation and to facilitate a deeper understanding of genomic data, the CardioTF database was constructed. The purpose of this database is to collate information on cardiovascular transcription factors (TFs), position weight matrices (PWMs), and enhancer sequences discovered using the ChIP-seq method. The Naïve-Bayes algorithm was used to classify literature and identify all PubMed abstracts on cardiovascular development. The natural language learning tool GNAT was then used to identify corresponding gene names embedded within these abstracts. Local Perl scripts were used to integrate and dump data from public databases into the MariaDB management system (MySQL). In-house R scripts were written to analyze and visualize the results. Known cardiovascular TFs from humans and human homologs from fly, Ciona, zebrafish, frog, chicken, and mouse were identified and deposited in the database. PWMs from Jaspar, hPDI, and UniPROBE databases were deposited in the database and can be retrieved using their corresponding TF names. Gene enhancer regions from various sources of ChIP-seq data were deposited into the database and were able to be visualized by graphical output. Besides biocuration, mouse homologs of the 81 core cardiac TFs were selected using a Naïve-Bayes approach and then by intersecting four independent data sources: RNA profiling, expert annotation, PubMed abstracts and phenotype. The CardioTF database can be used as a portal to construct transcriptional network of cardiac development. Database URL: http://www.cardiosignal.org/database/cardiotf.html.

Development of Cymbidium ensifolium genic-SSR markers and their utility in genetic diversity and population structure analysis in cymbidiums.

PubMed

Li, Xiaobai; Jin, Feng; Jin, Liang; Jackson, Aaron; Huang, Cheng; Li, Kehu; Shu, Xiaoli

2014-12-05

Cymbidium is a genus of 68 species in the orchid family, with extremely high ornamental value. Marker-assisted selection has proven to be an effective strategy in accelerating plant breeding for many plant species. Analysis of cymbidiums genetic background by molecular markers can be of great value in assisting parental selection and breeding strategy design, however, in plants such as cymbidiums limited genomic resources exist. In order to obtain efficient markers, we deep sequenced the C. ensifolium transcriptome to identify simple sequence repeats derived from gene regions (genic-SSR). The 7,936 genic-SSR markers were identified. A total of 80 genic-SSRs were selected, and primers were designed according to their flanking sequences. Of the 80 genic-SSR primer sets, 62 were amplified in C. ensifolium successfully, and 55 showed polymorphism when cross-tested among 9 Cymbidium species comprising 59 accessions. Unigenes containing the 62 genic-SSRs were searched against Non-redundant (Nr), Gene Ontology database (GO), eukaryotic orthologous groups (KOGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The search resulted in 53 matching Nr sequences, of which 39 had GO terms, 18 were assigned to KOGs, and 15 were annotated with KEGG. Genetic diversity and population structure were analyzed based on 55 polymorphic genic-SSR data among 59 accessions. The genetic distance averaged 0.3911, ranging from 0.016 to 0.618. The polymorphic index content (PIC) of 55 polymorphic markers averaged 0.407, ranging from 0.033 to 0.863. A model-based clustering analysis revealed that five genetic groups existed in the collection. Accessions from the same species were typically grouped together; however, C. goeringii accessions did not always form a separate cluster, suggesting that C. goeringii accessions were polyphyletic. The genic-SSR identified in this study constitute a set of markers that can be applied across multiple Cymbidium species and used for the evaluation of genetic relationships as well as qualitative and quantitative trait mapping studies. Genic-SSR's coupled with the functional annotations provided by the unigenes will aid in mapping candidate genes of specific function.

The database on transgenic luminescent microorganisms as an instrument of studying a microbial component of closed ecosystems

NASA Astrophysics Data System (ADS)

Boyandin, A. N.; Lankin, Y. P.; Kargatova, T. V.; Popova, L. Y.; Pechurkin, N. S.

Luminescent transgenic microorganisms are widely used for study of microbial communities' functioning including closed ones. Bioluminescence is of high sensitive to effects of different environmental factors. Integration of lux-genes into different metabolic ways allows studying many aspects of microorganisms' life permitting to carry out measurements in situ. There is much information about applications of bioluminescent bacteria in different researches. But for effective using these data their summarizing and accumulation in common source is required. Therefore an information system on characteristics of transgenic microorganisms with cloned lux-genes was created. The database and client software related were developed. A database structure includes information on common characteristics of cloned lux-genes, their sources and properties, on regulation of gene expression in bacterial cells, on dependence of bioluminescence manifestation on biotic, abiotic and anthropogenic environmental factors. The database also can store description of changes in bacterial populations depending on environmental changes. The database created allows storing and using bibliographic information and also links to web sites of world collections of microorganisms. Internet publishing software permitting to open access to the database through the Internet is developed.

Database resources of the National Center for Biotechnology Information

PubMed Central

Sayers, Eric W.; Barrett, Tanya; Benson, Dennis A.; Bolton, Evan; Bryant, Stephen H.; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M.; DiCuccio, Michael; Federhen, Scott; Feolo, Michael; Fingerman, Ian M.; Geer, Lewis Y.; Helmberg, Wolfgang; Kapustin, Yuri; Krasnov, Sergey; Landsman, David; Lipman, David J.; Lu, Zhiyong; Madden, Thomas L.; Madej, Tom; Maglott, Donna R.; Marchler-Bauer, Aron; Miller, Vadim; Karsch-Mizrachi, Ilene; Ostell, James; Panchenko, Anna; Phan, Lon; Pruitt, Kim D.; Schuler, Gregory D.; Sequeira, Edwin; Sherry, Stephen T.; Shumway, Martin; Sirotkin, Karl; Slotta, Douglas; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A.; Wagner, Lukas; Wang, Yanli; Wilbur, W. John; Yaschenko, Eugene; Ye, Jian

2012-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Website. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Probe, Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov. PMID:22140104

Database resources of the National Center for Biotechnology Information

PubMed Central

2013-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, the Genetic Testing Registry, Genome and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, BioProject, BioSample, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Probe, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool, Biosystems, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page. PMID:23193264

Database resources of the National Center for Biotechnology Information.

PubMed

Wheeler, David L; Barrett, Tanya; Benson, Dennis A; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Geer, Lewis Y; Kapustin, Yuri; Khovayko, Oleg; Landsman, David; Lipman, David J; Madden, Thomas L; Maglott, Donna R; Ostell, James; Miller, Vadim; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Steven T; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Tatusov, Roman L; Tatusova, Tatiana A; Wagner, Lukas; Yaschenko, Eugene

2007-01-01

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link(BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace and Assembly Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Viral Genotyping Tools, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Database resources of the National Center for Biotechnology Information.

PubMed

Sayers, Eric W; Barrett, Tanya; Benson, Dennis A; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; DiCuccio, Michael; Edgar, Ron; Federhen, Scott; Feolo, Michael; Geer, Lewis Y; Helmberg, Wolfgang; Kapustin, Yuri; Landsman, David; Lipman, David J; Madden, Thomas L; Maglott, Donna R; Miller, Vadim; Mizrachi, Ilene; Ostell, James; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Stephen T; Shumway, Martin; Sirotkin, Karl; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A; Wagner, Lukas; Yaschenko, Eugene; Ye, Jian

2009-01-01

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the web applications is custom implementation of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Conversion of KEGG metabolic pathways to SBGN maps including automatic layout

PubMed Central

2013-01-01

Background Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non‐trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused. Results Here we describe a methodology for automatic translation of KEGG metabolic pathways into the SBGN format. We infer important properties of the KEGG layout and treat these as layout constraints that are maintained during the conversion to SBGN maps. Conclusions This allows for the drawing and layout conventions of SBGN to be followed while creating maps that are still recognizably the original KEGG pathways. This article details the steps in this process and provides examples of the final result. PMID:23953132

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

PubMed Central

Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

2006-01-01

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. PMID:16685651

A-WINGS: an integrated genome database for Pleurocybella porrigens (Angel's wing oyster mushroom, Sugihiratake).

PubMed

Yamamoto, Naoki; Suzuki, Tomohiro; Kobayashi, Masaaki; Dohra, Hideo; Sasaki, Yohei; Hirai, Hirofumi; Yokoyama, Koji; Kawagishi, Hirokazu; Yano, Kentaro

2014-12-03

The angel's wing oyster mushroom (Pleurocybella porrigens, Sugihiratake) is a well-known delicacy. However, its potential risk in acute encephalopathy was recently revealed by a food poisoning incident. To disclose the genes underlying the accident and provide mechanistic insight, we seek to develop an information infrastructure containing omics data. In our previous work, we sequenced the genome and transcriptome using next-generation sequencing techniques. The next step in achieving our goal is to develop a web database to facilitate the efficient mining of large-scale omics data and identification of genes specifically expressed in the mushroom. This paper introduces a web database A-WINGS (http://bioinf.mind.meiji.ac.jp/a-wings/) that provides integrated genomic and transcriptomic information for the angel's wing oyster mushroom. The database contains structure and functional annotations of transcripts and gene expressions. Functional annotations contain information on homologous sequences from NCBI nr and UniProt, Gene Ontology, and KEGG Orthology. Digital gene expression profiles were derived from RNA sequencing (RNA-seq) analysis in the fruiting bodies and mycelia. The omics information stored in the database is freely accessible through interactive and graphical interfaces by search functions that include 'GO TREE VIEW' browsing, keyword searches, and BLAST searches. The A-WINGS database will accelerate omics studies on specific aspects of the angel's wing oyster mushroom and the family Tricholomataceae.

BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis.

PubMed

Bagger, Frederik Otzen; Sasivarevic, Damir; Sohi, Sina Hadi; Laursen, Linea Gøricke; Pundhir, Sachin; Sønderby, Casper Kaae; Winther, Ole; Rapin, Nicolas; Porse, Bo T

2016-01-04

Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely inaccessible. Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival. To address these shortcomings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles from FACS sorted healthy and malignant haematopoietic cells. A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan-Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database. The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool. Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia. Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well-defined haematopoietic cellular space. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Integrated database for identifying candidate genes for Aspergillus flavus resistance in maize

PubMed Central

2010-01-01

Background Aspergillus flavus Link:Fr, an opportunistic fungus that produces aflatoxin, is pathogenic to maize and other oilseed crops. Aflatoxin is a potent carcinogen, and its presence markedly reduces the value of grain. Understanding and enhancing host resistance to A. flavus infection and/or subsequent aflatoxin accumulation is generally considered an efficient means of reducing grain losses to aflatoxin. Different proteomic, genomic and genetic studies of maize (Zea mays L.) have generated large data sets with the goal of identifying genes responsible for conferring resistance to A. flavus, or aflatoxin. Results In order to maximize the usage of different data sets in new studies, including association mapping, we have constructed a relational database with web interface integrating the results of gene expression, proteomic (both gel-based and shotgun), Quantitative Trait Loci (QTL) genetic mapping studies, and sequence data from the literature to facilitate selection of candidate genes for continued investigation. The Corn Fungal Resistance Associated Sequences Database (CFRAS-DB) (http://agbase.msstate.edu/) was created with the main goal of identifying genes important to aflatoxin resistance. CFRAS-DB is implemented using MySQL as the relational database management system running on a Linux server, using an Apache web server, and Perl CGI scripts as the web interface. The database and the associated web-based interface allow researchers to examine many lines of evidence (e.g. microarray, proteomics, QTL studies, SNP data) to assess the potential role of a gene or group of genes in the response of different maize lines to A. flavus infection and subsequent production of aflatoxin by the fungus. Conclusions CFRAS-DB provides the first opportunity to integrate data pertaining to the problem of A. flavus and aflatoxin resistance in maize in one resource and to support queries across different datasets. The web-based interface gives researchers different query options for mining the database across different types of experiments. The database is publically available at http://agbase.msstate.edu. PMID:20946609

Integrated database for identifying candidate genes for Aspergillus flavus resistance in maize.

PubMed

Kelley, Rowena Y; Gresham, Cathy; Harper, Jonathan; Bridges, Susan M; Warburton, Marilyn L; Hawkins, Leigh K; Pechanova, Olga; Peethambaran, Bela; Pechan, Tibor; Luthe, Dawn S; Mylroie, J E; Ankala, Arunkanth; Ozkan, Seval; Henry, W B; Williams, W P

2010-10-07

Aspergillus flavus Link:Fr, an opportunistic fungus that produces aflatoxin, is pathogenic to maize and other oilseed crops. Aflatoxin is a potent carcinogen, and its presence markedly reduces the value of grain. Understanding and enhancing host resistance to A. flavus infection and/or subsequent aflatoxin accumulation is generally considered an efficient means of reducing grain losses to aflatoxin. Different proteomic, genomic and genetic studies of maize (Zea mays L.) have generated large data sets with the goal of identifying genes responsible for conferring resistance to A. flavus, or aflatoxin. In order to maximize the usage of different data sets in new studies, including association mapping, we have constructed a relational database with web interface integrating the results of gene expression, proteomic (both gel-based and shotgun), Quantitative Trait Loci (QTL) genetic mapping studies, and sequence data from the literature to facilitate selection of candidate genes for continued investigation. The Corn Fungal Resistance Associated Sequences Database (CFRAS-DB) (http://agbase.msstate.edu/) was created with the main goal of identifying genes important to aflatoxin resistance. CFRAS-DB is implemented using MySQL as the relational database management system running on a Linux server, using an Apache web server, and Perl CGI scripts as the web interface. The database and the associated web-based interface allow researchers to examine many lines of evidence (e.g. microarray, proteomics, QTL studies, SNP data) to assess the potential role of a gene or group of genes in the response of different maize lines to A. flavus infection and subsequent production of aflatoxin by the fungus. CFRAS-DB provides the first opportunity to integrate data pertaining to the problem of A. flavus and aflatoxin resistance in maize in one resource and to support queries across different datasets. The web-based interface gives researchers different query options for mining the database across different types of experiments. The database is publically available at http://agbase.msstate.edu.

Evolutionarily conserved ELOVL4 gene expression in the vertebrate retina.

PubMed

Lagali, Pamela S; Liu, Jiafan; Ambasudhan, Rajesh; Kakuk, Laura E; Bernstein, Steven L; Seigel, Gail M; Wong, Paul W; Ayyagari, Radha

2003-07-01

The gene elongation of very long chain fatty acids-4 (ELOVL4) has been shown to underlie phenotypically heterogeneous forms of autosomal dominant macular degeneration. In this study, the extent of evolutionary conservation and the existence and localization of retinal expression of this gene was investigated across a wide variety of species. Southern blot analysis of genomic DNA and bioinformatic analysis using the human ELOVL4 cDNA and protein sequences, respectively, were performed to identify species in which ELOVL4 orthologues and/or homologues are present. Retinal RNA and protein extracts derived from different species were assessed by Northern hybridization and immunoblot techniques to assess evolutionary conservation of gene expression. Immunohistochemical analysis of tissue sections prepared from various mammalian retinas was performed to determine the distribution of ELOVL4 and homologous proteins within specific retinal cell layers. The existence of ELOVL4 sequence orthologues and homologues was confirmed by both Southern blot analysis and in silico searches of protein sequence databases. Phylogenetic analysis places ELOVL4 among a large family of known and putative fatty acid elongase proteins. Northern blot analysis revealed the presence of multiple transcripts corresponding to ELOVL4 homologues expressed in the retina of several different mammalian species. Conserved proteins were also detected among retinal extracts of different mammals and were found to localize predominantly to the photoreceptor cell layer within retinal tissue preparations. The ELOVL4 gene is highly conserved throughout evolution and is expressed in the photoreceptor cells of the retina in a variety of different species, which suggests that it plays a critical role in retinal cell biology.

The YeastGenome app: the Saccharomyces Genome Database at your fingertips.

PubMed

Wong, Edith D; Karra, Kalpana; Hitz, Benjamin C; Hong, Eurie L; Cherry, J Michael

2013-01-01

The Saccharomyces Genome Database (SGD) is a scientific database that provides researchers with high-quality curated data about the genes and gene products of Saccharomyces cerevisiae. To provide instant and easy access to this information on mobile devices, we have developed YeastGenome, a native application for the Apple iPhone and iPad. YeastGenome can be used to quickly find basic information about S. cerevisiae genes and chromosomal features regardless of internet connectivity. With or without network access, you can view basic information and Gene Ontology annotations about a gene of interest by searching gene names and gene descriptions or by browsing the database within the app to find the gene of interest. With internet access, the app provides more detailed information about the gene, including mutant phenotypes, references and protein and genetic interactions, as well as provides hyperlinks to retrieve detailed information by showing SGD pages and views of the genome browser. SGD provides online help describing basic ways to navigate the mobile version of SGD, highlights key features and answers frequently asked questions related to the app. The app is available from iTunes (http://itunes.com/apps/yeastgenome). The YeastGenome app is provided freely as a service to our community, as part of SGD's mission to provide free and open access to all its data and annotations.

pGenN, a Gene Normalization Tool for Plant Genes and Proteins in Scientific Literature

PubMed Central

Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.

2015-01-01

Background Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. Methods In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. Results We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/). PMID:26258475

Distribution of triclosan-resistant genes in major pathogenic microorganisms revealed by metagenome and genome-wide analysis

PubMed Central

Khan, Raees; Roy, Nazish; Choi, Kihyuck

2018-01-01

The substantial use of triclosan (TCS) has been aimed to kill pathogenic bacteria, but TCS resistance seems to be prevalent in microbial species and limited knowledge exists about TCS resistance determinants in a majority of pathogenic bacteria. We aimed to evaluate the distribution of TCS resistance determinants in major pathogenic bacteria (N = 231) and to assess the enrichment of potentially pathogenic genera in TCS contaminated environments. A TCS-resistant gene (TRG) database was constructed and experimentally validated to predict TCS resistance in major pathogenic bacteria. Genome-wide in silico analysis was performed to define the distribution of TCS-resistant determinants in major pathogens. Microbiome analysis of TCS contaminated soil samples was also performed to investigate the abundance of TCS-resistant pathogens. We experimentally confirmed that TCS resistance could be accurately predicted using genome-wide in silico analysis against TRG database. Predicted TCS resistant phenotypes were observed in all of the tested bacterial strains (N = 17), and heterologous expression of selected TCS resistant genes from those strains conferred expected levels of TCS resistance in an alternative host Escherichia coli. Moreover, genome-wide analysis revealed that potential TCS resistance determinants were abundant among the majority of human-associated pathogens (79%) and soil-borne plant pathogenic bacteria (98%). These included a variety of enoyl-acyl carrier protein reductase (ENRs) homologues, AcrB efflux pumps, and ENR substitutions. FabI ENR, which is the only known effective target for TCS, was either co-localized with other TCS resistance determinants or had TCS resistance-associated substitutions. Furthermore, microbiome analysis revealed that pathogenic genera with intrinsic TCS-resistant determinants exist in TCS contaminated environments. We conclude that TCS may not be as effective against the majority of bacterial pathogens as previously presumed. Further, the excessive use of this biocide in natural environments may selectively enrich for not only TCS-resistant bacterial pathogens, but possibly for additional resistance to multiple antibiotics. PMID:29420585

The PBII gene of the human salivary proline-rich protein P-B produces another protein, Q504X8, with an opiorphin homolog, QRGPR.

PubMed

Saitoh, Eiichi; Sega, Takuya; Imai, Akane; Isemura, Satoko; Kato, Tetsuo; Ochiai, Akihito; Taniguchi, Masayuki

2018-04-01

The NCBI gene database and human-transcriptome database for alternative splicing were used to determine the expression of mRNAs for P-B (SMR3B) and variant form of P-B. The translational product from the former mRNA was identified as the protein named P-B, whereas that from the latter has not yet been elucidated. In the present study, we investigated the expression of P-B and its variant form at the protein level. To identify the variant protein of P-B, (1) cationic proteins with a higher isoelectric point in human pooled whole saliva were purified by a two dimensional liquid chromatography; (2) the peptide fragments generated from the in-solution of all proteins digested with trypsin separated and analyzed by MALDI-TOF-MS; and (3) the presence or absence of P-B in individual saliva was examined by 15% SDS-PAGE. The peptide sequences (I 37 PPPYSCTPNMNNCSR 52 , C 53 HHHHKRHHYPCNYCFCYPK 72 , R 59 HHYPCNYCFCYPK 72 and H 60 HYPCNYCFCYPK 72 ) present in the variant protein of P-B were identified. The peptide sequence (G 6 PYPPGPLAPPQPFGPGFVPPPPPPPYGPGR 36 ) in P-B (or the variant) and sequence (I 37 PPPPPAPYGPGIFPPPPPQP 57 ) in P-B were identified. The sum of the sequences identified indicated a 91.23% sequence identity for P-B and 79.76% for the variant. There were cases in which P-B existed in individual saliva, but there were cases in which it did not exist in individual saliva. The variant protein is produced by excising a non-canonical intron (CC-AC pair) from the 3'-noncoding sequence of the PBII gene. Both P-B and the variant are subject to proteolysis in the oral cavity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Identification of Odor-Processing Genes in the Emerald Ash Borer, Agrilus planipennis

PubMed Central

Mamidala, Praveen; Wijeratne, Asela J.; Wijeratne, Saranga; Poland, Therese; Qazi, Sohail S.; Doucet, Daniel; Cusson, Michel; Beliveau, Catherine; Mittapalli, Omprakash

2013-01-01

Background Insects rely on olfaction to locate food, mates, and suitable oviposition sites for successful completion of their life cycle. Agrilus planipennis Fairmaire (emerald ash borer) is a serious invasive insect pest that has killed tens of millions of North American ash (Fraxinus spp) trees and threatens the very existence of the genus Fraxinus. Adult A. planipennis are attracted to host volatiles and conspecifics; however, to date no molecular knowledge exists on olfaction in A. planipennis. Hence, we undertook an antennae-specific transcriptomic study to identify the repertoire of odor processing genes involved in A. planipennis olfaction. Methodology and Principal Findings We acquired 139,085 Roche/454 GS FLX transcriptomic reads that were assembled into 30,615 high quality expressed sequence tags (ESTs), including 3,249 isotigs and 27,366 non-isotigs (contigs and singletons). Intriguingly, the majority of the A. planipennis antennal transcripts (59.72%) did not show similarity with sequences deposited in the non-redundant database of GenBank, potentially representing novel genes. Functional annotation and KEGG analysis revealed pathways associated with signaling and detoxification. Several odor processing genes (9 odorant binding proteins, 2 odorant receptors, 1 sensory neuron membrane protein and 134 odorant/xenobiotic degradation enzymes, including cytochrome P450s, glutathione-S-transferases; esterases, etc.) putatively involved in olfaction processes were identified. Quantitative PCR of candidate genes in male and female A. planipennis in different developmental stages revealed developmental- and sex-biased expression patterns. Conclusions and Significance The antennal ESTs derived from A. planipennis constitute a rich molecular resource for the identification of genes potentially involved in the olfaction process of A. planipennis. These findings should help in understanding the processing of antennally-active compounds (e.g. 7-epi-sesquithujene) previously identified in this serious invasive pest. PMID:23424668

Genome sequence determination and metagenomic characterization of a Dehalococcoides mixed culture grown on cis-1,2-dichloroethene.

PubMed

Yohda, Masafumi; Yagi, Osami; Takechi, Ayane; Kitajima, Mizuki; Matsuda, Hisashi; Miyamura, Naoaki; Aizawa, Tomoko; Nakajima, Mutsuyasu; Sunairi, Michio; Daiba, Akito; Miyajima, Takashi; Teruya, Morimi; Teruya, Kuniko; Shiroma, Akino; Shimoji, Makiko; Tamotsu, Hinako; Juan, Ayaka; Nakano, Kazuma; Aoyama, Misako; Terabayashi, Yasunobu; Satou, Kazuhito; Hirano, Takashi

2015-07-01

A Dehalococcoides-containing bacterial consortium that performed dechlorination of 0.20 mM cis-1,2-dichloroethene to ethene in 14 days was obtained from the sediment mud of the lotus field. To obtain detailed information of the consortium, the metagenome was analyzed using the short-read next-generation sequencer SOLiD 3. Matching the obtained sequence tags with the reference genome sequences indicated that the Dehalococcoides sp. in the consortium was highly homologous to Dehalococcoides mccartyi CBDB1 and BAV1. Sequence comparison with the reference sequence constructed from 16S rRNA gene sequences in a public database showed the presence of Sedimentibacter, Sulfurospirillum, Clostridium, Desulfovibrio, Parabacteroides, Alistipes, Eubacterium, Peptostreptococcus and Proteocatella in addition to Dehalococcoides sp. After further enrichment, the members of the consortium were narrowed down to almost three species. Finally, the full-length circular genome sequence of the Dehalococcoides sp. in the consortium, D. mccartyi IBARAKI, was determined by analyzing the metagenome with the single-molecule DNA sequencer PacBio RS. The accuracy of the sequence was confirmed by matching it to the tag sequences obtained by SOLiD 3. The genome is 1,451,062 nt and the number of CDS is 1566, which includes 3 rRNA genes and 47 tRNA genes. There exist twenty-eight RDase genes that are accompanied by the genes for anchor proteins. The genome exhibits significant sequence identity with other Dehalococcoides spp. throughout the genome, but there exists significant difference in the distribution RDase genes. The combination of a short-read next-generation DNA sequencer and a long-read single-molecule DNA sequencer gives detailed information of a bacterial consortium. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Identification of odor-processing genes in the emerald ash borer, Agrilus planipennis.

PubMed

Mamidala, Praveen; Wijeratne, Asela J; Wijeratne, Saranga; Poland, Therese; Qazi, Sohail S; Doucet, Daniel; Cusson, Michel; Beliveau, Catherine; Mittapalli, Omprakash

2013-01-01

Insects rely on olfaction to locate food, mates, and suitable oviposition sites for successful completion of their life cycle. Agrilus planipennis Fairmaire (emerald ash borer) is a serious invasive insect pest that has killed tens of millions of North American ash (Fraxinus spp) trees and threatens the very existence of the genus Fraxinus. Adult A. planipennis are attracted to host volatiles and conspecifics; however, to date no molecular knowledge exists on olfaction in A. planipennis. Hence, we undertook an antennae-specific transcriptomic study to identify the repertoire of odor processing genes involved in A. planipennis olfaction. We acquired 139,085 Roche/454 GS FLX transcriptomic reads that were assembled into 30,615 high quality expressed sequence tags (ESTs), including 3,249 isotigs and 27,366 non-isotigs (contigs and singletons). Intriguingly, the majority of the A. planipennis antennal transcripts (59.72%) did not show similarity with sequences deposited in the non-redundant database of GenBank, potentially representing novel genes. Functional annotation and KEGG analysis revealed pathways associated with signaling and detoxification. Several odor processing genes (9 odorant binding proteins, 2 odorant receptors, 1 sensory neuron membrane protein and 134 odorant/xenobiotic degradation enzymes, including cytochrome P450s, glutathione-S-transferases; esterases, etc.) putatively involved in olfaction processes were identified. Quantitative PCR of candidate genes in male and female A. planipennis in different developmental stages revealed developmental- and sex-biased expression patterns. The antennal ESTs derived from A. planipennis constitute a rich molecular resource for the identification of genes potentially involved in the olfaction process of A. planipennis. These findings should help in understanding the processing of antennally-active compounds (e.g. 7-epi-sesquithujene) previously identified in this serious invasive pest.

DISEASES: text mining and data integration of disease-gene associations.

PubMed

Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

2015-03-01

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

ContEst16S: an algorithm that identifies contaminated prokaryotic genomes using 16S RNA gene sequences.

PubMed

Lee, Imchang; Chalita, Mauricio; Ha, Sung-Min; Na, Seong-In; Yoon, Seok-Hwan; Chun, Jongsik

2017-06-01

Thanks to the recent advancement of DNA sequencing technology, the cost and time of prokaryotic genome sequencing have been dramatically decreased. It has repeatedly been reported that genome sequencing using high-throughput next-generation sequencing is prone to contaminations due to its high depth of sequencing coverage. Although a few bioinformatics tools are available to detect potential contaminations, these have inherited limitations as they only use protein-coding genes. Here we introduce a new algorithm, called ContEst16S, to detect potential contaminations using 16S rRNA genes from genome assemblies. We screened 69 745 prokaryotic genomes from the NCBI Assembly Database using ContEst16S and found that 594 were contaminated by bacteria, human and plants. Of the predicted contaminated genomes, 8 % were not predicted by the existing protein-coding gene-based tool, implying that both methods can be complementary in the detection of contaminations. A web-based service of the algorithm is available at www.ezbiocloud.net/tools/contest16s.

Identification of core pathways based on attractor and crosstalk in ischemic stroke.

PubMed

Diao, Xiufang; Liu, Aijuan

2018-02-01

Ischemic stroke is a leading cause of mortality and disability around the world. It is an important task to identify dysregulated pathways which infer molecular and functional insights existing in high-throughput experimental data. Gene expression profile of E-GEOD-16561 was collected. Pathways were obtained from the database of Kyoto Encyclopedia of Genes and Genomes and Retrieval of Interacting Genes was used to download protein-protein interaction sets. Attractor and crosstalk approaches were applied to screen dysregulated pathways. A total of 20 differentially expressed genes were identified in ischemic stroke. Thirty-nine significant differential pathways were identified according to P<0.01 and 28 pathways were identified with RP<0.01 and 17 pathways were identified with impact factor >250. On the basis of the three criteria, 11 significant dysfunctional pathways were identified. Among them, Epstein-Barr virus infection was the most significant differential pathway. In conclusion, with the method based on attractor and crosstalk, significantly dysfunctional pathways were identified. These pathways are expected to provide molecular mechanism of ischemic stroke and represents a novel potential therapeutic target for ischemic stroke treatment.

Arabidopsis Gene Family Profiler (aGFP)--user-oriented transcriptomic database with easy-to-use graphic interface.

PubMed

Dupl'áková, Nikoleta; Renák, David; Hovanec, Patrik; Honysová, Barbora; Twell, David; Honys, David

2007-07-23

Microarray technologies now belong to the standard functional genomics toolbox and have undergone massive development leading to increased genome coverage, accuracy and reliability. The number of experiments exploiting microarray technology has markedly increased in recent years. In parallel with the rapid accumulation of transcriptomic data, on-line analysis tools are being introduced to simplify their use. Global statistical data analysis methods contribute to the development of overall concepts about gene expression patterns and to query and compose working hypotheses. More recently, these applications are being supplemented with more specialized products offering visualization and specific data mining tools. We present a curated gene family-oriented gene expression database, Arabidopsis Gene Family Profiler (aGFP; http://agfp.ueb.cas.cz), which gives the user access to a large collection of normalised Affymetrix ATH1 microarray datasets. The database currently contains NASC Array and AtGenExpress transcriptomic datasets for various tissues at different developmental stages of wild type plants gathered from nearly 350 gene chips. The Arabidopsis GFP database has been designed as an easy-to-use tool for users needing an easily accessible resource for expression data of single genes, pre-defined gene families or custom gene sets, with the further possibility of keyword search. Arabidopsis Gene Family Profiler presents a user-friendly web interface using both graphic and text output. Data are stored at the MySQL server and individual queries are created in PHP script. The most distinguishable features of Arabidopsis Gene Family Profiler database are: 1) the presentation of normalized datasets (Affymetrix MAS algorithm and calculation of model-based gene-expression values based on the Perfect Match-only model); 2) the choice between two different normalization algorithms (Affymetrix MAS4 or MAS5 algorithms); 3) an intuitive interface; 4) an interactive "virtual plant" visualizing the spatial and developmental expression profiles of both gene families and individual genes. Arabidopsis GFP gives users the possibility to analyze current Arabidopsis developmental transcriptomic data starting with simple global queries that can be expanded and further refined to visualize comparative and highly selective gene expression profiles.

Transcriptome-Wide Identification of Reference Genes for Expression Analysis of Soybean Responses to Drought Stress along the Day.

PubMed

Marcolino-Gomes, Juliana; Rodrigues, Fabiana Aparecida; Fuganti-Pagliarini, Renata; Nakayama, Thiago Jonas; Ribeiro Reis, Rafaela; Bouças Farias, Jose Renato; Harmon, Frank G; Correa Molinari, Hugo Bruno; Correa Molinari, Mayla Daiane; Nepomuceno, Alexandre

2015-01-01

The soybean transcriptome displays strong variation along the day in optimal growth conditions and also in response to adverse circumstances, like drought stress. However, no study conducted to date has presented suitable reference genes, with stable expression along the day, for relative gene expression quantification in combined studies on drought stress and diurnal oscillations. Recently, water deficit responses have been associated with circadian clock oscillations at the transcription level, revealing the existence of hitherto unknown processes and increasing the demand for studies on plant responses to drought stress and its oscillation during the day. We performed data mining from a transcriptome-wide background using microarrays and RNA-seq databases to select an unpublished set of candidate reference genes, specifically chosen for the normalization of gene expression in studies on soybean under both drought stress and diurnal oscillations. Experimental validation and stability analysis in soybean plants submitted to drought stress and sampled during a 24 h timecourse showed that four of these newer reference genes (FYVE, NUDIX, Golgin-84 and CYST) indeed exhibited greater expression stability than the conventionally used housekeeping genes (ELF1-β and β-actin) under these conditions. We also demonstrated the effect of using reference candidate genes with different stability values to normalize the relative expression data from a drought-inducible soybean gene (DREB5) evaluated in different periods of the day.

The Papillomavirus Episteme: a central resource for papillomavirus sequence data and analysis.

PubMed

Van Doorslaer, Koenraad; Tan, Qina; Xirasagar, Sandhya; Bandaru, Sandya; Gopalan, Vivek; Mohamoud, Yasmin; Huyen, Yentram; McBride, Alison A

2013-01-01

The goal of the Papillomavirus Episteme (PaVE) is to provide an integrated resource for the analysis of papillomavirus (PV) genome sequences and related information. The PaVE is a freely accessible, web-based tool (http://pave.niaid.nih.gov) created around a relational database, which enables storage, analysis and exchange of sequence information. From a design perspective, the PaVE adopts an Open Source software approach and stresses the integration and reuse of existing tools. Reference PV genome sequences have been extracted from publicly available databases and reannotated using a custom-created tool. To date, the PaVE contains 241 annotated PV genomes, 2245 genes and regions, 2004 protein sequences and 47 protein structures, which users can explore, analyze or download. The PaVE provides scientists with the data and tools needed to accelerate scientific progress for the study and treatment of diseases caused by PVs.

Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs.

PubMed

Lazzari, Barbara; Caprera, Andrea; Cestaro, Alessandro; Merelli, Ivan; Del Corvo, Marcello; Fontana, Paolo; Milanesi, Luciano; Velasco, Riccardo; Stella, Alessandra

2009-06-29

Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated.

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

PubMed

Jambusaria, Ankit; Klomp, Jeff; Hong, Zhigang; Rafii, Shahin; Dai, Yang; Malik, Asrar B; Rehman, Jalees

2018-06-07

The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

Epigenomic Elements Analyses for Promoters Identify ESRRG as a New Susceptibility Gene for Obesity-related Traits

PubMed Central

Dong, Shan-Shan; Guo, Yan; Zhu, Dong-Li; Chen, Xiao-Feng; Wu, Xiao-Ming; Shen, Hui; Chen, Xiang-Ding; Tan, Li-Jun; Tian, Qing; Deng, Hong-Wen; Yang, Tie-Lin

2016-01-01

OBJECTIVES With ENCODE epigenomic data and results from published genome-wide association studies (GWASs), we aimed to find regulatory signatures of obesity genes and discover novel susceptibility genes. METHODS Obesity genes were obtained from public GWASs databases and their promoters were annotated based on the regulatory elements information. Significantly enriched or depleted epigenomic elements in the promoters of obesity genes were evaluated and all human genes were then prioritized according to the existence of the selected elements to predict new candidate genes. Top ranked genes were subsequently applied to validate their associations with obesity-related traits in three independent in-house GWASs samples. RESULTS We identified RAD21 and EZH2 as over-represented, STAT2 and IRF3 as depleted transcription factors. Histone modification of H3K9me3 and chromatin state segmentation of “poised promoter” and “repressed” were overrepresented. All genes were prioritized and we selected the top five genes for validation at population level. Combined results from the three GWASs samples, rs7522101 in ESRRG remained significantly associated with BMI after multiple testing corrections (P = 7.25 × 10−5). It was also associated with β-cell function (P = 1.99 × 10−3) and fasting glucose level (P < 0.05) in the meta-analyses of glucose and insulin-related traits consortium (MAGIC) dataset. CONCLUSIONS In summary, we identified epigenomic characteristics for obesity genes and suggested ESRRG as a novel obesity susceptibility gene. PMID:27113491

Update of the androgen receptor gene mutations database.

PubMed

Gottlieb, B; Beitel, L K; Lumbroso, R; Pinsky, L; Trifiro, M

1999-01-01

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 309 to 374 during the past year. We have expanded the database by adding information on AR-interacting proteins; and we have improved the database by identifying those mutation entries that have been updated. Mutations of unknown significance have now been reported in both the 5' and 3' untranslated regions of the AR gene, and in individuals who are somatic mosaics constitutionally. In addition, single nucleotide polymorphisms, including silent mutations, have been discovered in normal individuals and in individuals with male infertility. A mutation hotspot associated with prostatic cancer has been identified in exon 5. The database is available on the internet (http://www.mcgill.ca/androgendb/), from EMBL-European Bioinformatics Institute (ftp.ebi.ac.uk/pub/databases/androgen), or as a Macintosh FilemakerPro or Word file (MC33@musica.mcgill.ca). Copyright 1999 Wiley-Liss, Inc.

Database resources of the National Center for Biotechnology Information: 2002 update

PubMed Central

Wheeler, David L.; Church, Deanna M.; Lash, Alex E.; Leipe, Detlef D.; Madden, Thomas L.; Pontius, Joan U.; Schuler, Gregory D.; Schriml, Lynn M.; Tatusova, Tatiana A.; Wagner, Lukas; Rapp, Barbara A.

2002-01-01

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI’s web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, Human¡VMouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov. PMID:11752242

The National NeuroAIDS Tissue Consortium (NNTC) Database: an integrated database for HIV-related studies.

PubMed

Cserhati, Matyas F; Pandey, Sanjit; Beaudoin, James J; Baccaglini, Lorena; Guda, Chittibabu; Fox, Howard S

2015-01-01

We herein present the National NeuroAIDS Tissue Consortium-Data Coordinating Center (NNTC-DCC) database, which is the only available database for neuroAIDS studies that contains data in an integrated, standardized form. This database has been created in conjunction with the NNTC, which provides human tissue and biofluid samples to individual researchers to conduct studies focused on neuroAIDS. The database contains experimental datasets from 1206 subjects for the following categories (which are further broken down into subcategories): gene expression, genotype, proteins, endo-exo-chemicals, morphometrics and other (miscellaneous) data. The database also contains a wide variety of downloadable data and metadata for 95 HIV-related studies covering 170 assays from 61 principal investigators. The data represent 76 tissue types, 25 measurement types, and 38 technology types, and reaches a total of 33,017,407 data points. We used the ISA platform to create the database and develop a searchable web interface for querying the data. A gene search tool is also available, which searches for NCBI GEO datasets associated with selected genes. The database is manually curated with many user-friendly features, and is cross-linked to the NCBI, HUGO and PubMed databases. A free registration is required for qualified users to access the database. © The Author(s) 2015. Published by Oxford University Press.

DrugBank 5.0: a major update to the DrugBank database for 2018.

PubMed

Wishart, David S; Feunang, Yannick D; Guo, An C; Lo, Elvis J; Marcu, Ana; Grant, Jason R; Sajed, Tanvir; Johnson, Daniel; Li, Carin; Sayeeda, Zinat; Assempour, Nazanin; Iynkkaran, Ithayavani; Liu, Yifeng; Maciejewski, Adam; Gale, Nicola; Wilson, Alex; Chin, Lucy; Cummings, Ryan; Le, Diana; Pon, Allison; Knox, Craig; Wilson, Michael

2018-01-04

DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing

PubMed Central

2013-01-01

Background A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. Results In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Conclusions We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. PMID:23742147

HAEdb: a novel interactive, locus-specific mutation database for the C1 inhibitor gene.

PubMed

Kalmár, Lajos; Hegedüs, Tamás; Farkas, Henriette; Nagy, Melinda; Tordai, Attila

2005-01-01

Hereditary angioneurotic edema (HAE) is an autosomal dominant disorder characterized by episodic local subcutaneous and submucosal edema and is caused by the deficiency of the activated C1 esterase inhibitor protein (C1-INH or C1INH; approved gene symbol SERPING1). Published C1-INH mutations are represented in large universal databases (e.g., OMIM, HGMD), but these databases update their data rather infrequently, they are not interactive, and they do not allow searches according to different criteria. The HAEdb, a C1-INH gene mutation database (http://hae.biomembrane.hu) was created to contribute to the following expectations: 1) help the comprehensive collection of information on genetic alterations of the C1-INH gene; 2) create a database in which data can be searched and compared according to several flexible criteria; and 3) provide additional help in new mutation identification. The website uses MySQL, an open-source, multithreaded, relational database management system. The user-friendly graphical interface was written in the PHP web programming language. The website consists of two main parts, the freely browsable search function, and the password-protected data deposition function. Mutations of the C1-INH gene are divided in two parts: gross mutations involving DNA fragments >1 kb, and micro mutations encompassing all non-gross mutations. Several attributes (e.g., affected exon, molecular consequence, family history) are collected for each mutation in a standardized form. This database may facilitate future comprehensive analyses of C1-INH mutations and also provide regular help for molecular diagnostic testing of HAE patients in different centers.

The University of Minnesota Biocatalysis/Biodegradation Database: specialized metabolism for functional genomics.

PubMed Central

Ellis, L B; Hershberger, C D; Wackett, L P

1999-01-01

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://www.labmed.umn.edu/umbbd/i nde x.html) first became available on the web in 1995 to provide information on microbial biocatalytic reactions of, and biodegradation pathways for, organic chemical compounds, especially those produced by man. Its goal is to become a representative database of biodegradation, spanning the diversity of known microbial metabolic routes, organic functional groups, and environmental conditions under which biodegradation occurs. The database can be used to enhance understanding of basic biochemistry, biocatalysis leading to speciality chemical manufacture, and biodegradation of environmental pollutants. It is also a resource for functional genomics, since it contains information on enzymes and genes involved in specialized metabolism not found in intermediary metabolism databases, and thus can assist in assigning functions to genes homologous to such less common genes. With information on >400 reactions and compounds, it is poised to become a resource for prediction of microbial biodegradation pathways for compounds it does not contain, a process complementary to predicting the functions of new classes of microbial genes. PMID:9847233

The Innate Immune Database (IIDB)

PubMed Central

Korb, Martin; Rust, Aistair G; Thorsson, Vesteinn; Battail, Christophe; Li, Bin; Hwang, Daehee; Kennedy, Kathleen A; Roach, Jared C; Rosenberger, Carrie M; Gilchrist, Mark; Zak, Daniel; Johnson, Carrie; Marzolf, Bruz; Aderem, Alan; Shmulevich, Ilya; Bolouri, Hamid

2008-01-01

Background As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site . Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens. Description We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser. Conclusion We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at . PMID:18321385

Culture adaptation of malaria parasites selects for convergent loss-of-function mutants.

PubMed

Claessens, Antoine; Affara, Muna; Assefa, Samuel A; Kwiatkowski, Dominic P; Conway, David J

2017-01-24

Cultured human pathogens may differ significantly from source populations. To investigate the genetic basis of laboratory adaptation in malaria parasites, clinical Plasmodium falciparum isolates were sampled from patients and cultured in vitro for up to three months. Genome sequence analysis was performed on multiple culture time point samples from six monoclonal isolates, and single nucleotide polymorphism (SNP) variants emerging over time were detected. Out of a total of five positively selected SNPs, four represented nonsense mutations resulting in stop codons, three of these in a single ApiAP2 transcription factor gene, and one in SRPK1. To survey further for nonsense mutants associated with culture, genome sequences of eleven long-term laboratory-adapted parasite strains were examined, revealing four independently acquired nonsense mutations in two other ApiAP2 genes, and five in Epac. No mutants of these genes exist in a large database of parasite sequences from uncultured clinical samples. This implicates putative master regulator genes in which multiple independent stop codon mutations have convergently led to culture adaptation, affecting most laboratory lines of P. falciparum. Understanding the adaptive processes should guide development of experimental models, which could include targeted gene disruption to adapt fastidious malaria parasite species to culture.

PRGdb: a bioinformatics platform for plant resistance gene analysis

PubMed Central

Sanseverino, Walter; Roma, Guglielmo; De Simone, Marco; Faino, Luigi; Melito, Sara; Stupka, Elia; Frusciante, Luigi; Ercolano, Maria Raffaella

2010-01-01

PRGdb is a web accessible open-source (http://www.prgdb.org) database that represents the first bioinformatic resource providing a comprehensive overview of resistance genes (R-genes) in plants. PRGdb holds more than 16 000 known and putative R-genes belonging to 192 plant species challenged by 115 different pathogens and linked with useful biological information. The complete database includes a set of 73 manually curated reference R-genes, 6308 putative R-genes collected from NCBI and 10463 computationally predicted putative R-genes. Thanks to a user-friendly interface, data can be examined using different query tools. A home-made prediction pipeline called Disease Resistance Analysis and Gene Orthology (DRAGO), based on reference R-gene sequence data, was developed to search for plant resistance genes in public datasets such as Unigene and Genbank. New putative R-gene classes containing unknown domain combinations were discovered and characterized. The development of the PRG platform represents an important starting point to conduct various experimental tasks. The inferred cross-link between genomic and phenotypic information allows access to a large body of information to find answers to several biological questions. The database structure also permits easy integration with other data types and opens up prospects for future implementations. PMID:19906694

Fastidious Gram-Negatives: Identification by the Vitek 2 Neisseria-Haemophilus Card and by Partial 16S rRNA Gene Sequencing Analysis.

PubMed

Sönksen, Ute Wolff; Christensen, Jens Jørgen; Nielsen, Lisbeth; Hesselbjerg, Annemarie; Hansen, Dennis Schrøder; Bruun, Brita

2010-12-31

Taxonomy and identification of fastidious Gram negatives are evolving and challenging. We compared identifications achieved with the Vitek 2 Neisseria-Haemophilus (NH) card and partial 16S rRNA gene sequence (526 bp stretch) analysis with identifications obtained with extensive phenotypic characterization using 100 fastidious Gram negative bacteria. Seventy-five strains represented 21 of the 26 taxa included in the Vitek 2 NH database and 25 strains represented related species not included in the database. Of the 100 strains, 31 were the type strains of the species. Vitek 2 NH identification results: 48 of 75 database strains were correctly identified, 11 strains gave `low discrimination´, seven strains were unidentified, and nine strains were misidentified. Identification of 25 non-database strains resulted in 14 strains incorrectly identified as belonging to species in the database. Partial 16S rRNA gene sequence analysis results: For 76 strains phenotypic and sequencing identifications were identical, for 23 strains the sequencing identifications were either probable or possible, and for one strain only the genus was confirmed. Thus, the Vitek 2 NH system identifies most of the commonly occurring species included in the database. Some strains of rarely occurring species and strains of non-database species closely related to database species cause problems. Partial 16S rRNA gene sequence analysis performs well, but does not always suffice, additional phenotypical characterization being useful for final identification.

Fastidious Gram-Negatives: Identification by the Vitek 2 Neisseria-Haemophilus Card and by Partial 16S rRNA Gene Sequencing Analysis

PubMed Central

Sönksen, Ute Wolff; Christensen, Jens Jørgen; Nielsen, Lisbeth; Hesselbjerg, Annemarie; Hansen, Dennis Schrøder; Bruun, Brita

2010-01-01

Taxonomy and identification of fastidious Gram negatives are evolving and challenging. We compared identifications achieved with the Vitek 2 Neisseria-Haemophilus (NH) card and partial 16S rRNA gene sequence (526 bp stretch) analysis with identifications obtained with extensive phenotypic characterization using 100 fastidious Gram negative bacteria. Seventy-five strains represented 21 of the 26 taxa included in the Vitek 2 NH database and 25 strains represented related species not included in the database. Of the 100 strains, 31 were the type strains of the species. Vitek 2 NH identification results: 48 of 75 database strains were correctly identified, 11 strains gave `low discrimination´, seven strains were unidentified, and nine strains were misidentified. Identification of 25 non-database strains resulted in 14 strains incorrectly identified as belonging to species in the database. Partial 16S rRNA gene sequence analysis results: For 76 strains phenotypic and sequencing identifications were identical, for 23 strains the sequencing identifications were either probable or possible, and for one strain only the genus was confirmed. Thus, the Vitek 2 NH system identifies most of the commonly occurring species included in the database. Some strains of rarely occurring species and strains of non-database species closely related to database species cause problems. Partial 16S rRNA gene sequence analysis performs well, but does not always suffice, additional phenotypical characterization being useful for final identification. PMID:21347215

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database

PubMed Central

Drabkin, Harold J.; Blake, Judith A.

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as ‘GO’ or ‘homology’ or ‘phenotype’. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as ‘papers selected for GO that refer to genes with NO GO annotation’. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications. PMID:23110975

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database.

PubMed

Drabkin, Harold J; Blake, Judith A

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as 'GO' or 'homology' or 'phenotype'. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as 'papers selected for GO that refer to genes with NO GO annotation'. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications.

Workflow based framework for life science informatics.

PubMed

Tiwari, Abhishek; Sekhar, Arvind K T

2007-10-01

Workflow technology is a generic mechanism to integrate diverse types of available resources (databases, servers, software applications and different services) which facilitate knowledge exchange within traditionally divergent fields such as molecular biology, clinical research, computational science, physics, chemistry and statistics. Researchers can easily incorporate and access diverse, distributed tools and data to develop their own research protocols for scientific analysis. Application of workflow technology has been reported in areas like drug discovery, genomics, large-scale gene expression analysis, proteomics, and system biology. In this article, we have discussed the existing workflow systems and the trends in applications of workflow based systems.

[Long non-coding RNAs in plants].

PubMed

Xiaoqing, Huang; Dandan, Li; Juan, Wu

2015-04-01

Long non-coding RNAs (lncRNAs), which are longer than 200 nucleotides in length, widely exist in organisms and function in a variety of biological processes. Currently, most of lncRNAs found in plants are transcribed by RNA polymerase Ⅱ and mediate gene expression through multiple mechanisms, such as target mimicry, transcription interference, histone methylation and DNA methylation, and play important roles in flowering, male sterility, nutrition metabolism, biotic and abiotic stress and other biological processes as regulators in plants. In this review, we summarize the databases, prediction methods, and possible functions of plant lncRNAs discovered in recent years.

CRITICA: coding region identification tool invoking comparative analysis

NASA Technical Reports Server (NTRS)

Badger, J. H.; Olsen, G. J.; Woese, C. R. (Principal Investigator)

1999-01-01

Gene recognition is essential to understanding existing and future DNA sequence data. CRITICA (Coding Region Identification Tool Invoking Comparative Analysis) is a suite of programs for identifying likely protein-coding sequences in DNA by combining comparative analysis of DNA sequences with more common noncomparative methods. In the comparative component of the analysis, regions of DNA are aligned with related sequences from the DNA databases; if the translation of the aligned sequences has greater amino acid identity than expected for the observed percentage nucleotide identity, this is interpreted as evidence for coding. CRITICA also incorporates noncomparative information derived from the relative frequencies of hexanucleotides in coding frames versus other contexts (i.e., dicodon bias). The dicodon usage information is derived by iterative analysis of the data, such that CRITICA is not dependent on the existence or accuracy of coding sequence annotations in the databases. This independence makes the method particularly well suited for the analysis of novel genomes. CRITICA was tested by analyzing the available Salmonella typhimurium DNA sequences. Its predictions were compared with the DNA sequence annotations and with the predictions of GenMark. CRITICA proved to be more accurate than GenMark, and moreover, many of its predictions that would seem to be errors instead reflect problems in the sequence databases. The source code of CRITICA is freely available by anonymous FTP (rdp.life.uiuc.edu in/pub/critica) and on the World Wide Web (http:/(/)rdpwww.life.uiuc.edu).

Actinobase: Database on molecular diversity, phylogeny and biocatalytic potential of salt tolerant alkaliphilic actinomycetes.

PubMed

Sharma, Amit K; Gohel, Sangeeta; Singh, Satya P

2012-01-01

Actinobase is a relational database of molecular diversity, phylogeny and biocatalytic potential of haloalkaliphilic actinomycetes. The main objective of this data base is to provide easy access to range of information, data storage, comparison and analysis apart from reduced data redundancy, data entry, storage, retrieval costs and improve data security. Information related to habitat, cell morphology, Gram reaction, biochemical characterization and molecular features would allow researchers in understanding identification and stress adaptation of the existing and new candidates belonging to salt tolerant alkaliphilic actinomycetes. The PHP front end helps to add nucleotides and protein sequence of reported entries which directly help researchers to obtain the required details. Analysis of the genus wise status of the salt tolerant alkaliphilic actinomycetes indicated 6 different genera among the 40 classified entries of the salt tolerant alkaliphilic actinomycetes. The results represented wide spread occurrence of salt tolerant alkaliphilic actinomycetes belonging to diverse taxonomic positions. Entries and information related to actinomycetes in the database are publicly accessible at http://www.actinobase.in. On clustalW/X multiple sequence alignment of the alkaline protease gene sequences, different clusters emerged among the groups. The narrow search and limit options of the constructed database provided comparable information. The user friendly access to PHP front end facilitates would facilitate addition of sequences of reported entries. The database is available for free at http://www.actinobase.in.

Comprehensive coverage of cardiovascular disease data in the disease portals at the Rat Genome Database.

PubMed

Wang, Shur-Jen; Laulederkind, Stanley J F; Hayman, G Thomas; Petri, Victoria; Smith, Jennifer R; Tutaj, Marek; Nigam, Rajni; Dwinell, Melinda R; Shimoyama, Mary

2016-08-01

Cardiovascular diseases are complex diseases caused by a combination of genetic and environmental factors. To facilitate progress in complex disease research, the Rat Genome Database (RGD) provides the community with a disease portal where genome objects and biological data related to cardiovascular diseases are systematically organized. The purpose of this study is to present biocuration at RGD, including disease, genetic, and pathway data. The RGD curation team uses controlled vocabularies/ontologies to organize data curated from the published literature or imported from disease and pathway databases. These organized annotations are associated with genes, strains, and quantitative trait loci (QTLs), thus linking functional annotations to genome objects. Screen shots from the web pages are used to demonstrate the organization of annotations at RGD. The human cardiovascular disease genes identified by annotations were grouped according to data sources and their annotation profiles were compared by in-house tools and other enrichment tools available to the public. The analysis results show that the imported cardiovascular disease genes from ClinVar and OMIM are functionally different from the RGD manually curated genes in terms of pathway and Gene Ontology annotations. The inclusion of disease genes from other databases enriches the collection of disease genes not only in quantity but also in quality. Copyright © 2016 the American Physiological Society.

WormQTLHD--a web database for linking human disease to natural variation data in C. elegans.

PubMed

van der Velde, K Joeri; de Haan, Mark; Zych, Konrad; Arends, Danny; Snoek, L Basten; Kammenga, Jan E; Jansen, Ritsert C; Swertz, Morris A; Li, Yang

2014-01-01

Interactions between proteins are highly conserved across species. As a result, the molecular basis of multiple diseases affecting humans can be studied in model organisms that offer many alternative experimental opportunities. One such organism-Caenorhabditis elegans-has been used to produce much molecular quantitative genetics and systems biology data over the past decade. We present WormQTL(HD) (Human Disease), a database that quantitatively and systematically links expression Quantitative Trait Loci (eQTL) findings in C. elegans to gene-disease associations in man. WormQTL(HD), available online at http://www.wormqtl-hd.org, is a user-friendly set of tools to reveal functionally coherent, evolutionary conserved gene networks. These can be used to predict novel gene-to-gene associations and the functions of genes underlying the disease of interest. We created a new database that links C. elegans eQTL data sets to human diseases (34 337 gene-disease associations from OMIM, DGA, GWAS Central and NHGRI GWAS Catalogue) based on overlapping sets of orthologous genes associated to phenotypes in these two species. We utilized QTL results, high-throughput molecular phenotypes, classical phenotypes and genotype data covering different developmental stages and environments from WormQTL database. All software is available as open source, built on MOLGENIS and xQTL workbench.

Plant Genome Resources at the National Center for Biotechnology Information

PubMed Central

Wheeler, David L.; Smith-White, Brian; Chetvernin, Vyacheslav; Resenchuk, Sergei; Dombrowski, Susan M.; Pechous, Steven W.; Tatusova, Tatiana; Ostell, James

2005-01-01

The National Center for Biotechnology Information (NCBI) integrates data from more than 20 biological databases through a flexible search and retrieval system called Entrez. A core Entrez database, Entrez Nucleotide, includes GenBank and is tightly linked to the NCBI Taxonomy database, the Entrez Protein database, and the scientific literature in PubMed. A suite of more specialized databases for genomes, genes, gene families, gene expression, gene variation, and protein domains dovetails with the core databases to make Entrez a powerful system for genomic research. Linked to the full range of Entrez databases is the NCBI Map Viewer, which displays aligned genetic, physical, and sequence maps for eukaryotic genomes including those of many plants. A specialized plant query page allow maps from all plant genomes covered by the Map Viewer to be searched in tandem to produce a display of aligned maps from several species. PlantBLAST searches against the sequences shown in the Map Viewer allow BLAST alignments to be viewed within a genomic context. In addition, precomputed sequence similarities, such as those for proteins offered by BLAST Link, enable fluid navigation from unannotated to annotated sequences, quickening the pace of discovery. NCBI Web pages for plants, such as Plant Genome Central, complete the system by providing centralized access to NCBI's genomic resources as well as links to organism-specific Web pages beyond NCBI. PMID:16010002

Construction of a rice glycoside hydrolase phylogenomic database and identification of targets for biofuel research

PubMed Central

Sharma, Rita; Cao, Peijian; Jung, Ki-Hong; Sharma, Manoj K.; Ronald, Pamela C.

2013-01-01

Glycoside hydrolases (GH) catalyze the hydrolysis of glycosidic bonds in cell wall polymers and can have major effects on cell wall architecture. Taking advantage of the massive datasets available in public databases, we have constructed a rice phylogenomic database of GHs (http://ricephylogenomics.ucdavis.edu/cellwalls/gh/). This database integrates multiple data types including the structural features, orthologous relationships, mutant availability, and gene expression patterns for each GH family in a phylogenomic context. The rice genome encodes 437 GH genes classified into 34 families. Based on pairwise comparison with eight dicot and four monocot genomes, we identified 138 GH genes that are highly diverged between monocots and dicots, 57 of which have diverged further in rice as compared with four monocot genomes scanned in this study. Chromosomal localization and expression analysis suggest a role for both whole-genome and localized gene duplications in expansion and diversification of GH families in rice. We examined the meta-profiles of expression patterns of GH genes in twenty different anatomical tissues of rice. Transcripts of 51 genes exhibit tissue or developmental stage-preferential expression, whereas, seventeen other genes preferentially accumulate in actively growing tissues. When queried in RiceNet, a probabilistic functional gene network that facilitates functional gene predictions, nine out of seventeen genes form a regulatory network with the well-characterized genes involved in biosynthesis of cell wall polymers including cellulose synthase and cellulose synthase-like genes of rice. Two-thirds of the GH genes in rice are up regulated in response to biotic and abiotic stress treatments indicating a role in stress adaptation. Our analyses identify potential GH targets for cell wall modification. PMID:23986771

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs.

PubMed

Dilthey, Alexander T; Gourraud, Pierre-Antoine; Mentzer, Alexander J; Cereb, Nezih; Iqbal, Zamin; McVean, Gil

2016-10-01

Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30-250 CPU hours per sample) remain a significant challenge to practical application.

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

PubMed Central

Dilthey, Alexander T.; Gourraud, Pierre-Antoine; McVean, Gil

2016-01-01

Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30–250 CPU hours per sample) remain a significant challenge to practical application. PMID:27792722

Biochemical Validation of the Glyoxylate Cycle in the Cyanobacterium Chlorogloeopsis fritschii Strain PCC 9212.

PubMed

Zhang, Shuyi; Bryant, Donald A

2015-05-29

Cyanobacteria are important photoautotrophic bacteria with extensive but variable metabolic capacities. The existence of the glyoxylate cycle, a variant of the TCA cycle, is still poorly documented in cyanobacteria. Previous studies reported the activities of isocitrate lyase and malate synthase, the key enzymes of the glyoxylate cycle in some cyanobacteria, but other studies concluded that these enzymes are missing. In this study the genes encoding isocitrate lyase and malate synthase from Chlorogloeopsis fritschii PCC 9212 were identified, and the recombinant enzymes were biochemically characterized. Consistent with the presence of the enzymes of the glyoxylate cycle, C. fritschii could assimilate acetate under both light and dark growth conditions. Transcript abundances for isocitrate lyase and malate synthase increased, and C. fritschii grew faster, when the growth medium was supplemented with acetate. Adding acetate to the growth medium also increased the yield of poly-3-hydroxybutyrate. When the genes encoding isocitrate lyase and malate synthase were expressed in Synechococcus sp. PCC 7002, the acetate assimilation capacity of the resulting strain was greater than that of wild type. Database searches showed that the genes for the glyoxylate cycle exist in only a few other cyanobacteria, all of which are able to fix nitrogen. This study demonstrates that the glyoxylate cycle exists in a few cyanobacteria, and that this pathway plays an important role in the assimilation of acetate for growth in one of those organisms. The glyoxylate cycle might play a role in coordinating carbon and nitrogen metabolism under conditions of nitrogen fixation. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

HPIDB 2.0: a curated database for host–pathogen interactions

PubMed Central

Ammari, Mais G.; Gresham, Cathy R.; McCarthy, Fiona M.; Nanduri, Bindu

2016-01-01

Identification and analysis of host–pathogen interactions (HPI) is essential to study infectious diseases. However, HPI data are sparse in existing molecular interaction databases, especially for agricultural host–pathogen systems. Therefore, resources that annotate, predict and display the HPI that underpin infectious diseases are critical for developing novel intervention strategies. HPIDB 2.0 (http://www.agbase.msstate.edu/hpi/main.html) is a resource for HPI data, and contains 45, 238 manually curated entries in the current release. Since the first description of the database in 2010, multiple enhancements to HPIDB data and interface services were made that are described here. Notably, HPIDB 2.0 now provides targeted biocuration of molecular interaction data. As a member of the International Molecular Exchange consortium, annotations provided by HPIDB 2.0 curators meet community standards to provide detailed contextual experimental information and facilitate data sharing. Moreover, HPIDB 2.0 provides access to rapidly available community annotations that capture minimum molecular interaction information to address immediate researcher needs for HPI network analysis. In addition to curation, HPIDB 2.0 integrates HPI from existing external sources and contains tools to infer additional HPI where annotated data are scarce. Compared to other interaction databases, our data collection approach ensures HPIDB 2.0 users access the most comprehensive HPI data from a wide range of pathogens and their hosts (594 pathogen and 70 host species, as of February 2016). Improvements also include enhanced search capacity, addition of Gene Ontology functional information, and implementation of network visualization. The changes made to HPIDB 2.0 content and interface ensure that users, especially agricultural researchers, are able to easily access and analyse high quality, comprehensive HPI data. All HPIDB 2.0 data are updated regularly, are publically available for direct download, and are disseminated to other molecular interaction resources. Database URL: http://www.agbase.msstate.edu/hpi/main.html PMID:27374121

BIOSPIDA: A Relational Database Translator for NCBI.

PubMed

Hagen, Matthew S; Lee, Eva K

2010-11-13

As the volume and availability of biological databases continue widespread growth, it has become increasingly difficult for research scientists to identify all relevant information for biological entities of interest. Details of nucleotide sequences, gene expression, molecular interactions, and three-dimensional structures are maintained across many different databases. To retrieve all necessary information requires an integrated system that can query multiple databases with minimized overhead. This paper introduces a universal parser and relational schema translator that can be utilized for all NCBI databases in Abstract Syntax Notation (ASN.1). The data models for OMIM, Entrez-Gene, Pubmed, MMDB and GenBank have been successfully converted into relational databases and all are easily linkable helping to answer complex biological questions. These tools facilitate research scientists to locally integrate databases from NCBI without significant workload or development time.

MALDI-TOF MS Distinctly Differentiates Nontypable Haemophilus influenzae from Haemophilus haemolyticus

PubMed Central

Zhang, Huifang; Zhang, Yongchan; Gao, Yuan; Xu, Li; Lv, Jing; Wang, Yingtong; Zhang, Jianzhong; Shao, Zhujun

2013-01-01

Nontypable Haemophilus influenzae (NTHi) and Haemophilus haemolyticus exhibit different pathogenicities, but to date, there remains no definitive and reliable strategy for differentiating these strains. In this study, we evaluated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) as a potential method for differentiating NTHi and H. haemolyticus. The phylogenetic analysis of concatenated 16S rRNA and recombinase A (recA) gene sequences, outer membrane protein P6 gene sequencing and single-gene PCR were used as reference methods. The original reference database (ORD, provided with the Biotyper software) and new reference database (NRD, extended with Chinese strains) were compared for the evaluation of MALDI-TOF MS. Through a search of the ORD, 76.9% of the NTHi (40/52) and none of the H. haemolyticus (0/20) strains were identified at the species level. However, all NTHi and H. haemolyticus strains used for identification were accurately recognized at the species level when searching the NRD. From the dendrogram clustering of the main spectra projections, the Chinese and foreign H. influenzae reference strains were categorized into two distinct groups, and H. influenzae and H. haemolyticus were also separated into two categories. Compared to the existing methods, MALDI-TOF MS has the advantage of integrating high throughput, accuracy and speed. In conclusion, MALDI-TOF MS is an excellent method for differentiating NTHi and H. haemolyticus. This method can be recommended for use in appropriately equipped laboratories. PMID:23457514

Metabolic genes in cancer: their roles in tumor progression and clinical implications

PubMed Central

Furuta, Eiji; Okuda, Hiroshi; Kobayashi, Aya; Watabe, Kounosuke

2010-01-01

Re-programming of metabolic pathways is a hallmark of physiological changes in cancer cells. The expression of certain genes that directly control the rate of key metabolic pathways including glycolysis, lipogenesis and nucleotide synthesis are drastically altered at different stages of tumor progression. These alterations are generally considered as an adaptation of tumor cells; however, they also contribute to the progression of tumor cells to become more aggressive phenotypes. This review summarizes the recent information about the mechanistic link of these genes to oncogenesis and their potential utility as diagnostic markers as well as for therapeutic targets. We particularly focus on three groups of genes; GLUT1, G6PD, TKTL1 and PGI/AMF in glycolytic pathway, ACLY, ACC1 and FAS in lipogenesis and RRM1, RRM2 and TYMS for nucleotide synthesis. All these genes are highly up-regulated in a variety of tumor cells in cancer patients, and they play active roles in tumor progression rather than expressing merely as a consequence of phenotypic change of the cancer cells. Molecular dissection of their orchestrated networks and understanding the exact mechanism of their expression will provide a window of opportunity to target these genes for specific cancer therapy. We also reviewed existing database of gene microarray to validate the utility of these genes for cancer diagnosis. PMID:20122995

dbCPG: A web resource for cancer predisposition genes.

PubMed

Wei, Ran; Yao, Yao; Yang, Wu; Zheng, Chun-Hou; Zhao, Min; Xia, Junfeng

2016-06-21

Cancer predisposition genes (CPGs) are genes in which inherited mutations confer highly or moderately increased risks of developing cancer. Identification of these genes and understanding the biological mechanisms that underlie them is crucial for the prevention, early diagnosis, and optimized management of cancer. Over the past decades, great efforts have been made to identify CPGs through multiple strategies. However, information on these CPGs and their molecular functions is scattered. To address this issue and provide a comprehensive resource for researchers, we developed the Cancer Predisposition Gene Database (dbCPG, Database URL: http://bioinfo.ahu.edu.cn:8080/dbCPG/index.jsp), the first literature-based gene resource for exploring human CPGs. It contains 827 human (724 protein-coding, 23 non-coding, and 80 unknown type genes), 637 rats, and 658 mouse CPGs. Furthermore, data mining was performed to gain insights into the understanding of the CPGs data, including functional annotation, gene prioritization, network analysis of prioritized genes and overlap analysis across multiple cancer types. A user-friendly web interface with multiple browse, search, and upload functions was also developed to facilitate access to the latest information on CPGs. Taken together, the dbCPG database provides a comprehensive data resource for further studies of cancer predisposition genes.

A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network

PubMed Central

RUAN, XIYUN; LI, HONGYUN; LIU, BO; CHEN, JIE; ZHANG, SHIBAO; SUN, ZEQIANG; LIU, SHUANGQING; SUN, FAHAI; LIU, QINGYONG

2015-01-01

The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson’s correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson’s correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

Histoplasma capsulatum proteome response to decreased iron availability

PubMed Central

Winters, Michael S; Spellman, Daniel S; Chan, Qilin; Gomez, Francisco J; Hernandez, Margarita; Catron, Brittany; Smulian, Alan G; Neubert, Thomas A; Deepe, George S

2008-01-01

Background A fundamental pathogenic feature of the fungus Histoplasma capsulatum is its ability to evade innate and adaptive immune defenses. Once ingested by macrophages the organism is faced with several hostile environmental conditions including iron limitation. H. capsulatum can establish a persistent state within the macrophage. A gap in knowledge exists because the identities and number of proteins regulated by the organism under host conditions has yet to be defined. Lack of such knowledge is an important problem because until these proteins are identified it is unlikely that they can be targeted as new and innovative treatment for histoplasmosis. Results To investigate the proteomic response by H. capsulatum to decreasing iron availability we have created H. capsulatum protein/genomic databases compatible with current mass spectrometric (MS) search engines. Databases were assembled from the H. capsulatum G217B strain genome using gene prediction programs and expressed sequence tag (EST) libraries. Searching these databases with MS data generated from two dimensional (2D) in-gel digestions of proteins resulted in over 50% more proteins identified compared to searching the publicly available fungal databases alone. Using 2D gel electrophoresis combined with statistical analysis we discovered 42 H. capsulatum proteins whose abundance was significantly modulated when iron concentrations were lowered. Altered proteins were identified by mass spectrometry and database searching to be involved in glycolysis, the tricarboxylic acid cycle, lysine metabolism, protein synthesis, and one protein sequence whose function was unknown. Conclusion We have created a bioinformatics platform for H. capsulatum and demonstrated the utility of a proteomic approach by identifying a shift in metabolism the organism utilizes to cope with the hostile conditions provided by the host. We have shown that enzyme transcripts regulated by other fungal pathogens in response to lowering iron availability are also regulated in H. capsulatum at the protein level. We also identified H. capsulatum proteins sensitive to iron level reductions which have yet to be connected to iron availability in other pathogens. These data also indicate the complexity of the response by H. capsulatum to nutritional deprivation. Finally, we demonstrate the importance of a strain specific gene/protein database for H. capsulatum proteomic analysis. PMID:19108728

Database resources of the National Center for Biotechnology Information.

PubMed

Sayers, Eric W; Barrett, Tanya; Benson, Dennis A; Bolton, Evan; Bryant, Stephen H; Canese, Kathi; Chetvernin, Vyacheslav; Church, Deanna M; DiCuccio, Michael; Federhen, Scott; Feolo, Michael; Fingerman, Ian M; Geer, Lewis Y; Helmberg, Wolfgang; Kapustin, Yuri; Landsman, David; Lipman, David J; Lu, Zhiyong; Madden, Thomas L; Madej, Tom; Maglott, Donna R; Marchler-Bauer, Aron; Miller, Vadim; Mizrachi, Ilene; Ostell, James; Panchenko, Anna; Phan, Lon; Pruitt, Kim D; Schuler, Gregory D; Sequeira, Edwin; Sherry, Stephen T; Shumway, Martin; Sirotkin, Karl; Slotta, Douglas; Souvorov, Alexandre; Starchenko, Grigory; Tatusova, Tatiana A; Wagner, Lukas; Wang, Yanli; Wilbur, W John; Yaschenko, Eugene; Ye, Jian

2011-01-01

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Electronic PCR, OrfFinder, Splign, ProSplign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), IBIS, Biosystems, Peptidome, OMSSA, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Challenges of the information age: the impact of false discovery on pathway identification.

PubMed

Rog, Colin J; Chekuri, Srinivasa C; Edgerton, Mary E

2012-11-21

Pathways with members that have known relevance to a disease are used to support hypotheses generated from analyses of gene expression and proteomic studies. Using cancer as an example, the pitfalls of searching pathways databases as support for genes and proteins that could represent false discoveries are explored. The frequency with which networks could be generated from 100 instances each of randomly selected five and ten genes sets as input to MetaCore, a commercial pathways database, was measured. A PubMed search enumerated cancer-related literature published for any gene in the networks. Using three, two, and one maximum intervening step between input genes to populate the network, networks were generated with frequencies of 97%, 77%, and 7% using ten gene sets and 73%, 27%, and 1% using five gene sets. PubMed reported an average of 4225 cancer-related articles per network gene. This can be attributed to the richly populated pathways databases and the interest in the molecular basis of cancer. As information sources become enriched, they are more likely to generate plausible mechanisms for false discoveries.

PLEXdb: Gene expression resources for plants and plant pathogens

USDA-ARS?s Scientific Manuscript database

PLEXdb (Plant Expression Database), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facili...

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

PubMed Central

Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565

Putative type II toxin-antitoxin systems in Listeria monocytogenes isolated from clinical, food, and animal samples in Iran.

PubMed

Kalani, Behrooz Sadeghi; Irajian, Gholamreza; Lotfollahi, Lida; Abdollahzadeh, Esmail; Razavi, Shabnam

2018-06-04

Listeria monocytogenes is known as a major food-borne pathogen causing a severe life-threatening disease, listeriosis, in susceptible patients. This bacterium has special features that facilitate its survival in different conditions and cause resistance to antibacterial agents and biocides. Toxin-antitoxin (TA) system has a potential to be introduced as an antibacterial target because of its participation in cell physiology, including stress response, antiphage activity, biofilm formation, and resistance to antibiotics. In this study, after the identification of 6 genes of 3 TA pairs (lM/E-lM/F, lM/S-lM/B and ydc/D-ydc/E) via existing databases, the presence and expression level of these genes were investigated by PCR and q-PCR techniques, respectively. The result of RT-qPCR revealed that identified genes were expressed in different strains and ydc (maz) increased under thermal stress. It seems that the products of these genes play an important role in the physiology and survival of the bacterium especially in heat stress. Presence of 6 detected TA genes in all of the tested isolates demonstrated that TA system could be an antibacterial target in L. monocytogenes; however, more research is needed to explain the actual role of these genes. Copyright © 2018. Published by Elsevier Ltd.

CROPPER: a metagene creator resource for cross-platform and cross-species compendium studies.

PubMed

Paananen, Jussi; Storvik, Markus; Wong, Garry

2006-09-22

Current genomic research methods provide researchers with enormous amounts of data. Combining data from different high-throughput research technologies commonly available in biological databases can lead to novel findings and increase research efficiency. However, combining data from different heterogeneous sources is often a very arduous task. These sources can be different microarray technology platforms, genomic databases, or experiments performed on various species. Our aim was to develop a software program that could facilitate the combining of data from heterogeneous sources, and thus allow researchers to perform genomic cross-platform/cross-species studies and to use existing experimental data for compendium studies. We have developed a web-based software resource, called CROPPER that uses the latest genomic information concerning different data identifiers and orthologous genes from the Ensembl database. CROPPER can be used to combine genomic data from different heterogeneous sources, allowing researchers to perform cross-platform/cross-species compendium studies without the need for complex computational tools or the requirement of setting up one's own in-house database. We also present an example of a simple cross-platform/cross-species compendium study based on publicly available Parkinson's disease data derived from different sources. CROPPER is a user-friendly and freely available web-based software resource that can be successfully used for cross-species/cross-platform compendium studies.

Massive NGS Data Analysis Reveals Hundreds Of Potential Novel Gene Fusions in Human Cell Lines.

PubMed

Gioiosa, Silvia; Bolis, Marco; Flati, Tiziano; Massini, Annalisa; Garattini, Enrico; Chillemi, Giovanni; Fratelli, Maddalena; Castrignanò, Tiziana

2018-06-01

Gene fusions derive from chromosomal rearrangements and the resulting chimeric transcripts are often endowed with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with different prognosis and, in some cases, they can provide specific drug targets. So far, many efforts have been carried out to study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive Next Generation Sequencing dataset for all the existing human tumor cell lines has provided the opportunity to further investigate these data in order to identify novel and still uncharacterized gene fusion events. In our work, we have extensively reanalyzed 935 paired-end RNA-seq experiments downloaded from "The Cancer Cell Line Encyclopedia" repository, aiming at addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has been performed by the execution of four different gene fusion detection algorithms. The results have been further prioritized by running a bayesian classifier which makes an in silico validation. The collection of fusion events supported by all of the predictive softwares results in a robust set of ∼ 1,700 in-silico predicted novel candidates suitable for downstream analyses. Given the huge amount of data and information produced, computational results have been systematized in a database named LiGeA. The database can be browsed through a dynamical and interactive web portal, further integrated with validated data from other well known repositories. Taking advantage of the intuitive query forms, the users can easily access, navigate, filter and select the putative gene fusions for further validations and studies. They can also find suitable experimental models for a given fusion of interest. We believe that the LiGeA resource can represent not only the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant cell lines, but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer biomarkers and specific drug targets.

Biomine: predicting links between biological entities using network models of heterogeneous databases.

PubMed

Eronen, Lauri; Toivonen, Hannu

2012-06-06

Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.

The androgen receptor gene mutations database.

PubMed

Gottlieb, B; Trifiro, M; Lumbroso, R; Pinsky, L

1997-01-01

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 212 to 272. We have expanded the database: (i) by adding a large amount of new data on somatic mutations in prostatic cancer tissue; (ii) by defining a new constitutional phenotype, mild androgen insensitivity (MAI); (iii) by placing additional relevant information on an internet site (http://www.mcgill.ca/androgendb/ ). The database has allowed us to examine the contribution of CpG sites to the multiplicity of reports of the same mutation in different families. The database is also available from EMBL (ftp.ebi.ac.uk/pub/databases/androgen) or as a Macintosh Filemaker Pro or Word file (MC33@musica,mcgill.ca)

The androgen receptor gene mutations database.

PubMed Central

Gottlieb, B; Trifiro, M; Lumbroso, R; Pinsky, L

1997-01-01

The current version of the androgen receptor (AR) gene mutations database is described. The total number of reported mutations has risen from 212 to 272. We have expanded the database: (i) by adding a large amount of new data on somatic mutations in prostatic cancer tissue; (ii) by defining a new constitutional phenotype, mild androgen insensitivity (MAI); (iii) by placing additional relevant information on an internet site (http://www.mcgill.ca/androgendb/ ). The database has allowed us to examine the contribution of CpG sites to the multiplicity of reports of the same mutation in different families. The database is also available from EMBL (ftp.ebi.ac.uk/pub/databases/androgen) or as a Macintosh Filemaker Pro or Word file (MC33@musica,mcgill.ca) PMID:9016528

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.

Toward unification of taxonomy databases in a distributed computer environment

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

Kitakami, Hajime; Tateno, Yoshio; Gojobori, Takashi

1994-12-31

All the taxonomy databases constructed with the DNA databases of the international DNA data banks are powerful electronic dictionaries which aid in biological research by computer. The taxonomy databases are, however not consistently unified with a relational format. If we can achieve consistent unification of the taxonomy databases, it will be useful in comparing many research results, and investigating future research directions from existent research results. In particular, it will be useful in comparing relationships between phylogenetic trees inferred from molecular data and those constructed from morphological data. The goal of the present study is to unify the existent taxonomymore » databases and eliminate inconsistencies (errors) that are present in them. Inconsistencies occur particularly in the restructuring of the existent taxonomy databases, since classification rules for constructing the taxonomy have rapidly changed with biological advancements. A repair system is needed to remove inconsistencies in each data bank and mismatches among data banks. This paper describes a new methodology for removing both inconsistencies and mismatches from the databases on a distributed computer environment. The methodology is implemented in a relational database management system, SYBASE.« less

TabSQL: a MySQL tool to facilitate mapping user data to public databases.

PubMed

Xia, Xiao-Qin; McClelland, Michael; Wang, Yipeng

2010-06-23

With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases. We developed TabSQL, a MySQL-based application tool, for viewing, filtering and querying data files with large numbers of rows. TabSQL provides functions for downloading and installing table files from public databases including the Gene Ontology database (GO), the Ensembl databases, and genome databases from the UCSC genome bioinformatics site. Any other database that provides tab-delimited flat files can also be imported. The downloaded gene annotation tables can be queried together with users' data in TabSQL using either a graphic interface or command line. TabSQL allows queries across the user's data and public databases without programming. It is a convenient tool for biologists to annotate and enrich their data.

TabSQL: a MySQL tool to facilitate mapping user data to public databases

PubMed Central

2010-01-01

Background With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases. Results We developed TabSQL, a MySQL-based application tool, for viewing, filtering and querying data files with large numbers of rows. TabSQL provides functions for downloading and installing table files from public databases including the Gene Ontology database (GO), the Ensembl databases, and genome databases from the UCSC genome bioinformatics site. Any other database that provides tab-delimited flat files can also be imported. The downloaded gene annotation tables can be queried together with users' data in TabSQL using either a graphic interface or command line. Conclusions TabSQL allows queries across the user's data and public databases without programming. It is a convenient tool for biologists to annotate and enrich their data. PMID:20573251

NCBI GEO: mining millions of expression profiles--database and tools.

PubMed

Barrett, Tanya; Suzek, Tugba O; Troup, Dennis B; Wilhite, Stephen E; Ngau, Wing-Chi; Ledoux, Pierre; Rudnev, Dmitry; Lash, Alex E; Fujibuchi, Wataru; Edgar, Ron

2005-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

The Multinational Arabidopsis Steering Subcommittee for Proteomics Assembles the Largest Proteome Database Resource for Plant Systems Biology

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

Weckwerth, Wolfram; Baginsky, Sacha; Van Wijk, Klass

2009-12-01

In the past 10 years, we have witnessed remarkable advances in the field of plant molecular biology. The rapid development of proteomic technologies and the speed with which these techniques have been applied to the field have altered our perception of how we can analyze proteins in complex systems. At nearly the same time, the availability of the complete genome for the model plant Arabidopsis thaliana was released; this effort provides an unsurpassed resource for the identification of proteins when researchers use MS to analyze plant samples. Recognizing the growth in this area, the Multinational Arabidopsis Steering Committee (MASC) establishedmore » a subcommittee for A. thaliana proteomics in 2006 with the objective of consolidating databases, technique standards, and experimentally validated candidate genes and functions. Since the establishment of the Multinational Arabidopsis Steering Subcommittee for Proteomics (MASCP), many new approaches and resources have become available. Recently, the subcommittee established a webpage to consolidate this information (www.masc-proteomics.org). It includes links to plant proteomic databases, general information about proteomic techniques, meeting information, a summary of proteomic standards, and other relevant resources. Altogether, this website provides a useful resource for the Arabidopsis proteomics community. In the future, the website will host discussions and investigate the cross-linking of databases. The subcommittee members have extensive experience in arabidopsis proteomics and collectively have produced some of the most extensive proteomics data sets for this model plant (Table S1 in the Supporting Information has a list of resources). The largest collection of proteomics data from a single study in A. thaliana was assembled into an accessible database (AtProteome; http://fgcz-atproteome.unizh.ch/index.php) and was recently published by the Baginsky lab.1 The database provides links to major Arabidopsis online resources, and raw data have been deposited in PRIDE and PRIDE BioMart. Included in this database is an Arabidopsis proteome map that provides evidence for the expression of {approx}50% of all predicted gene models, including several alternative gene models that are not represented in The Arabidopsis Information Resource (TAIR) protein database. A set of organ-specific biomarkers is provided, as well as organ-specific proteotypic peptides for 4105 proteins that can be used to facilitate targeted quantitative proteomic surveys. In the future, the AtProteome database will be linked to additional existing resources developed by MASCP members, such as PPDB, ProMEX, and SUBA. The most comprehensive study on the Arabidopsis chloroplast proteome, which includes information on chloroplast sorting signals, posttranslational modifications (PTMs), and protein abundances (analyzed by high-accuracy MS [Orbitrap]), was recently published by the van Wijk lab.2 These and previous data are available via the plant proteome database (PPDB; http://ppdb.tc.cornell.edu) for A. thaliana and maize. PPDB provides genome-wide experimental and functional characterization of the A. thaliana and maize proteomes, including PTMs and subcellular localization information, with an emphasis on leaf and plastid proteins. Maize and Arabidopsis proteome entries are directly linked via internal BLAST alignments within PPDB. Direct links for each protein to TAIR, SUBA, ProMEX, and other resources are also provided.« less

PvTFDB: a Phaseolus vulgaris transcription factors database for expediting functional genomics in legumes

PubMed Central

Bhawna; Bonthala, V.S.; Gajula, MNV Prasad

2016-01-01

The common bean [Phaseolus vulgaris (L.)] is one of the essential proteinaceous vegetables grown in developing countries. However, its production is challenged by low yields caused by numerous biotic and abiotic stress conditions. Regulatory transcription factors (TFs) symbolize a key component of the genome and are the most significant targets for producing stress tolerant crop and hence functional genomic studies of these TFs are important. Therefore, here we have constructed a web-accessible TFs database for P. vulgaris, called PvTFDB, which contains 2370 putative TF gene models in 49 TF families. This database provides a comprehensive information for each of the identified TF that includes sequence data, functional annotation, SSRs with their primer sets, protein physical properties, chromosomal location, phylogeny, tissue-specific gene expression data, orthologues, cis-regulatory elements and gene ontology (GO) assignment. Altogether, this information would be used in expediting the functional genomic studies of a specific TF(s) of interest. The objectives of this database are to understand functional genomics study of common bean TFs and recognize the regulatory mechanisms underlying various stress responses to ease breeding strategy for variety production through a couple of search interfaces including gene ID, functional annotation and browsing interfaces including by family and by chromosome. This database will also serve as a promising central repository for researchers as well as breeders who are working towards crop improvement of legume crops. In addition, this database provide the user unrestricted public access and the user can download entire data present in the database freely. Database URL: http://www.multiomics.in/PvTFDB/ PMID:27465131

Comparative genomics of metabolic capacities of regulons controlled by cis-regulatory RNA motifs in bacteria.

PubMed

Sun, Eric I; Leyn, Semen A; Kazanov, Marat D; Saier, Milton H; Novichkov, Pavel S; Rodionov, Dmitry A

2013-09-02

In silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels.An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis. A comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations. The reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/).

RatMap--rat genome tools and data.

PubMed

Petersen, Greta; Johnson, Per; Andersson, Lars; Klinga-Levan, Karin; Gómez-Fabre, Pedro M; Ståhl, Fredrik

2005-01-01

The rat genome database RatMap (http://ratmap.org or http://ratmap.gen.gu.se) has been one of the main resources for rat genome information since 1994. The database is maintained by CMB-Genetics at Goteborg University in Sweden and provides information on rat genes, polymorphic rat DNA-markers and rat quantitative trait loci (QTLs), all curated at RatMap. The database is under the supervision of the Rat Gene and Nomenclature Committee (RGNC); thus much attention is paid to rat gene nomenclature. RatMap presents information on rat idiograms, karyotypes and provides a unified presentation of the rat genome sequence and integrated rat linkage maps. A set of tools is also available to facilitate the identification and characterization of rat QTLs, as well as the estimation of exon/intron number and sizes in individual rat genes. Furthermore, comparative gene maps of rat in regard to mouse and human are provided.

RatMap—rat genome tools and data

PubMed Central

Petersen, Greta; Johnson, Per; Andersson, Lars; Klinga-Levan, Karin; Gómez-Fabre, Pedro M.; Ståhl, Fredrik

2005-01-01

The rat genome database RatMap (http://ratmap.org or http://ratmap.gen.gu.se) has been one of the main resources for rat genome information since 1994. The database is maintained by CMB–Genetics at Göteborg University in Sweden and provides information on rat genes, polymorphic rat DNA-markers and rat quantitative trait loci (QTLs), all curated at RatMap. The database is under the supervision of the Rat Gene and Nomenclature Committee (RGNC); thus much attention is paid to rat gene nomenclature. RatMap presents information on rat idiograms, karyotypes and provides a unified presentation of the rat genome sequence and integrated rat linkage maps. A set of tools is also available to facilitate the identification and characterization of rat QTLs, as well as the estimation of exon/intron number and sizes in individual rat genes. Furthermore, comparative gene maps of rat in regard to mouse and human are provided. PMID:15608244

DroSpeGe: rapid access database for new Drosophila species genomes.

PubMed

Gilbert, Donald G

2007-01-01

The Drosophila species comparative genome database DroSpeGe (http://insects.eugenes.org/DroSpeGe/) provides genome researchers with rapid, usable access to 12 new and old Drosophila genomes, since its inception in 2004. Scientists can use, with minimal computing expertise, the wealth of new genome information for developing new insights into insect evolution. New genome assemblies provided by several sequencing centers have been annotated with known model organism gene homologies and gene predictions to provided basic comparative data. TeraGrid supplies the shared cyberinfrastructure for the primary computations. This genome database includes homologies to Drosophila melanogaster and eight other eukaryote model genomes, and gene predictions from several groups. BLAST searches of the newest assemblies are integrated with genome maps. GBrowse maps provide detailed views of cross-species aligned genomes. BioMart provides for data mining of annotations and sequences. Common chromosome maps identify major synteny among species. Potential gain and loss of genes is suggested by Gene Ontology groupings for genes of the new species. Summaries of essential genome statistics include sizes, genes found and predicted, homology among genomes, phylogenetic trees of species and comparisons of several gene predictions for sensitivity and specificity in finding new and known genes.

YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface

PubMed Central

Abdulrehman, Dário; Monteiro, Pedro Tiago; Teixeira, Miguel Cacho; Mira, Nuno Pereira; Lourenço, Artur Bastos; dos Santos, Sandra Costa; Cabrito, Tânia Rodrigues; Francisco, Alexandre Paulo; Madeira, Sara Cordeiro; Aires, Ricardo Santos; Oliveira, Arlindo Limede; Sá-Correia, Isabel; Freitas, Ana Teresa

2011-01-01

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT) information system (http://www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2010, this database contains over 48 200 regulatory associations between transcription factors (TFs) and target genes, including 298 specific DNA-binding sites for 110 characterized TFs. All regulatory associations stored in the database were revisited and detailed information on the experimental evidences that sustain those associations was added and classified as direct or indirect evidences. The inclusion of this new data, gathered in response to the requests of YEASTRACT users, allows the user to restrict its queries to subsets of the data based on the existence or not of experimental evidences for the direct action of the TFs in the promoter region of their target genes. Another new feature of this release is the availability of all data through a machine readable web-service interface. Users are no longer restricted to the set of available queries made available through the existing web interface, and can use the web service interface to query, retrieve and exploit the YEASTRACT data using their own implementation of additional functionalities. The YEASTRACT information system is further complemented with several computational tools that facilitate the use of the curated data when answering a number of important biological questions. Since its first release in 2006, YEASTRACT has been extensively used by hundreds of researchers from all over the world. We expect that by making the new data and services available, the system will continue to be instrumental for yeast biologists and systems biology researchers. PMID:20972212

TrSDB: a proteome database of transcription factors

PubMed Central

Hermoso, Antoni; Aguilar, Daniel; Aviles, Francesc X.; Querol, Enrique

2004-01-01

TrSDB—TranScout Database—(http://ibb.uab.es/trsdb) is a proteome database of eukaryotic transcription factors based upon predicted motifs by TranScout and data sources such as InterPro and Gene Ontology Annotation. Nine eukaryotic proteomes are included in the current version. Extensive and diverse information for each database entry, different analyses considering TranScout classification and similarity relationships are offered for research on transcription factors or gene expression. PMID:14681387

The Clinical Next-Generation Sequencing Database: A Tool for the Unified Management of Clinical Information and Genetic Variants to Accelerate Variant Pathogenicity Classification.

PubMed

Nishio, Shin-Ya; Usami, Shin-Ichi

2017-03-01

Recent advances in next-generation sequencing (NGS) have given rise to new challenges due to the difficulties in variant pathogenicity interpretation and large dataset management, including many kinds of public population databases as well as public or commercial disease-specific databases. Here, we report a new database development tool, named the "Clinical NGS Database," for improving clinical NGS workflow through the unified management of variant information and clinical information. This database software offers a two-feature approach to variant pathogenicity classification. The first of these approaches is a phenotype similarity-based approach. This database allows the easy comparison of the detailed phenotype of each patient with the average phenotype of the same gene mutation at the variant or gene level. It is also possible to browse patients with the same gene mutation quickly. The other approach is a statistical approach to variant pathogenicity classification based on the use of the odds ratio for comparisons between the case and the control for each inheritance mode (families with apparently autosomal dominant inheritance vs. control, and families with apparently autosomal recessive inheritance vs. control). A number of case studies are also presented to illustrate the utility of this database. © 2016 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Analysis of the Genome and Chromium Metabolism-Related Genes of Serratia sp. S2.

PubMed

Dong, Lanlan; Zhou, Simin; He, Yuan; Jia, Yan; Bai, Qunhua; Deng, Peng; Gao, Jieying; Li, Yingli; Xiao, Hong

2018-05-01

This study is to investigate the genome sequence of Serratia sp. S2. The genomic DNA of Serratia sp. S2 was extracted and the sequencing library was constructed. The sequencing was carried out by Illumina 2000 and complete genomic sequences were obtained. Gene function annotation and bioinformatics analysis were performed by comparing with the known databases. The genome size of Serratia sp. S2 was 5,604,115 bp and the G+C content was 57.61%. There were 5373 protein coding genes, and 3732, 3614, and 3942 genes were respectively annotated into the GO, KEGG, and COG databases. There were 12 genes related to chromium metabolism in the Serratia sp. S2 genome. The whole genome sequence of Serratia sp. S2 is submitted to the GenBank database with gene accession number of LNRP00000000. Our findings may provide theoretical basis for the subsequent development of new biotechnology to repair environmental chromium pollution.

HuMiChip: Development of a Functional Gene Array for the Study of Human Microbiomes

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

Tu, Q.; Deng, Ye; Lin, Lu

Microbiomes play very important roles in terms of nutrition, health and disease by interacting with their hosts. Based on sequence data currently available in public domains, we have developed a functional gene array to monitor both organismal and functional gene profiles of normal microbiota in human and mouse hosts, and such an array is called human and mouse microbiota array, HMM-Chip. First, seed sequences were identified from KEGG databases, and used to construct a seed database (seedDB) containing 136 gene families in 19 metabolic pathways closely related to human and mouse microbiomes. Second, a mother database (motherDB) was constructed withmore » 81 genomes of bacterial strains with 54 from gut and 27 from oral environments, and 16 metagenomes, and used for selection of genes and probe design. Gene prediction was performed by Glimmer3 for bacterial genomes, and by the Metagene program for metagenomes. In total, 228,240 and 801,599 genes were identified for bacterial genomes and metagenomes, respectively. Then the motherDB was searched against the seedDB using the HMMer program, and gene sequences in the motherDB that were highly homologous with seed sequences in the seedDB were used for probe design by the CommOligo software. Different degrees of specific probes, including gene-specific, inclusive and exclusive group-specific probes were selected. All candidate probes were checked against the motherDB and NCBI databases for specificity. Finally, 7,763 probes covering 91.2percent (12,601 out of 13,814) HMMer confirmed sequences from 75 bacterial genomes and 16 metagenomes were selected. This developed HMM-Chip is able to detect the diversity and abundance of functional genes, the gene expression of microbial communities, and potentially, the interactions of microorganisms and their hosts.« less

Gene Discovery in the Apicomplexa as Revealed by EST Sequencing and Assembly of a Comparative Gene Database

PubMed Central

Li, Li; Brunk, Brian P.; Kissinger, Jessica C.; Pape, Deana; Tang, Keliang; Cole, Robert H.; Martin, John; Wylie, Todd; Dante, Mike; Fogarty, Steven J.; Howe, Daniel K.; Liberator, Paul; Diaz, Carmen; Anderson, Jennifer; White, Michael; Jerome, Maria E.; Johnson, Emily A.; Radke, Jay A.; Stoeckert, Christian J.; Waterston, Robert H.; Clifton, Sandra W.; Roos, David S.; Sibley, L. David

2003-01-01

Large-scale EST sequencing projects for several important parasites within the phylum Apicomplexa were undertaken for the purpose of gene discovery. Included were several parasites of medical importance (Plasmodium falciparum, Toxoplasma gondii) and others of veterinary importance (Eimeria tenella, Sarcocystis neurona, and Neospora caninum). A total of 55,192 ESTs, deposited into dbEST/GenBank, were included in the analyses. The resulting sequences have been clustered into nonredundant gene assemblies and deposited into a relational database that supports a variety of sequence and text searches. This database has been used to compare the gene assemblies using BLAST similarity comparisons to the public protein databases to identify putative genes. Of these new entries, ∼15%–20% represent putative homologs with a conservative cutoff of p < 10−9, thus identifying many conserved genes that are likely to share common functions with other well-studied organisms. Gene assemblies were also used to identify strain polymorphisms, examine stage-specific expression, and identify gene families. An interesting class of genes that are confined to members of this phylum and not shared by plants, animals, or fungi, was identified. These genes likely mediate the novel biological features of members of the Apicomplexa and hence offer great potential for biological investigation and as possible therapeutic targets. [The sequence data from this study have been submitted to dbEST division of GenBank under accession nos.: Toxoplasma gondii: –, –, –, –, – , –, –, –, –. Plasmodium falciparum: –, –, –, –. Sarcocystis neurona: , , , , , , , , , , , , , –, –, –, –, –. Eimeria tenella: –, –, –, –, –, –, –, –, – , –, –, –, –, –, –, –, –, –, –, –. Neospora caninum: –, –, , – , –, –.] PMID:12618375

BIOSPIDA: A Relational Database Translator for NCBI

PubMed Central

Hagen, Matthew S.; Lee, Eva K.

2010-01-01

As the volume and availability of biological databases continue widespread growth, it has become increasingly difficult for research scientists to identify all relevant information for biological entities of interest. Details of nucleotide sequences, gene expression, molecular interactions, and three-dimensional structures are maintained across many different databases. To retrieve all necessary information requires an integrated system that can query multiple databases with minimized overhead. This paper introduces a universal parser and relational schema translator that can be utilized for all NCBI databases in Abstract Syntax Notation (ASN.1). The data models for OMIM, Entrez-Gene, Pubmed, MMDB and GenBank have been successfully converted into relational databases and all are easily linkable helping to answer complex biological questions. These tools facilitate research scientists to locally integrate databases from NCBI without significant workload or development time. PMID:21347013

A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data.

PubMed

Mo, Fan; Hong, Xu; Gao, Feng; Du, Lin; Wang, Jun; Omenn, Gilbert S; Lin, Biaoyang

2008-12-16

Alternative splicing is an important gene regulation mechanism. It is estimated that about 74% of multi-exon human genes have alternative splicing. High throughput tandem (MS/MS) mass spectrometry provides valuable information for rapidly identifying potentially novel alternatively-spliced protein products from experimental datasets. However, the ability to identify alternative splicing events through tandem mass spectrometry depends on the database against which the spectra are searched. We wrote scripts in perl, Bioperl, mysql and Ensembl API and built a theoretical exon-exon junction protein database to account for all possible combinations of exons for a gene while keeping the frame of translation (i.e., keeping only in-phase exon-exon combinations) from the Ensembl Core Database. Using our liver cancer MS/MS dataset, we identified a total of 488 non-redundant peptides that represent putative exon skipping events. Our exon-exon junction database provides the scientific community with an efficient means to identify novel alternatively spliced (exon skipping) protein isoforms using mass spectrometry data. This database will be useful in annotating genome structures using rapidly accumulating proteomics data.

Database resources of the National Center for Biotechnology

PubMed Central

Wheeler, David L.; Church, Deanna M.; Federhen, Scott; Lash, Alex E.; Madden, Thomas L.; Pontius, Joan U.; Schuler, Gregory D.; Schriml, Lynn M.; Sequeira, Edwin; Tatusova, Tatiana A.; Wagner, Lukas

2003-01-01

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, PubMed, PubMed Central (PMC), LocusLink, the NCBITaxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR (e-PCR), Open Reading Frame (ORF) Finder, References Sequence (RefSeq), UniGene, HomoloGene, ProtEST, Database of Single Nucleotide Polymorphisms (dbSNP), Human/Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker (MM), Evidence Viewer (EV), Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov. PMID:12519941

Binding Sites Analyser (BiSA): Software for Genomic Binding Sites Archiving and Overlap Analysis

PubMed Central

Khushi, Matloob; Liddle, Christopher; Clarke, Christine L.; Graham, J. Dinny

2014-01-01

Genome-wide mapping of transcription factor binding and histone modification reveals complex patterns of interactions. Identifying overlaps in binding patterns by different factors is a major objective of genomic studies, but existing methods to archive large numbers of datasets in a personalised database lack sophistication and utility. Therefore we have developed transcription factor DNA binding site analyser software (BiSA), for archiving of binding regions and easy identification of overlap with or proximity to other regions of interest. Analysis results can be restricted by chromosome or base pair overlap between regions or maximum distance between binding peaks. BiSA is capable of reporting overlapping regions that share common base pairs; regions that are nearby; regions that are not overlapping; and average region sizes. BiSA can identify genes located near binding regions of interest, genomic features near a gene or locus of interest and statistical significance of overlapping regions can also be reported. Overlapping results can be visualized as Venn diagrams. A major strength of BiSA is that it is supported by a comprehensive database of publicly available transcription factor binding sites and histone modifications, which can be directly compared to user data. The documentation and source code are available on http://bisa.sourceforge.net PMID:24533055

Isolation and Characterization of the PKAr Gene From a Plant Pathogen, Curvularia lunata.

PubMed

Liu, T; Ma, B C; Hou, J M; Zuo, Y H

2014-09-01

By using EST database from a full-length cDNA library of Curvularia lunata, we have isolated a 2.9 kb cDNA, termed PKAr. An ORF of 1,383 bp encoding a polypeptide of 460 amino acids with molecular weight 50.1 kDa, (GeneBank Acc. No. KF675744) was cloned. The deduced amino acid sequence of the PKAr shows 90 and 88 % identity with cAMP-dependent protein kinase A regulatory subunit from Alternaria alternate and Pyrenophora tritici-repentis Pt-1C-BFP, respectively. Database analysis revealed that the deduced amino acid sequence of PKAr shares considerable similarity with that of PKA regulatory subunits in other organisms, particularly in the conserved regions. No introns were identified within the 1,383 bp of ORF compared with PKAr genomic DNA sequence. Southern blot indicated that PKAr existed as a single copy per genome. The mRNA expression level of PKAr in different development stages were demonstrated using real-time quantitative PCR. The results showed that the level of PKAr expression was highest in vegetative growth mycelium, which indicated it might play an important role in the vegetative growth of C. lunata. These results provided a fundamental supporting research on the function of PKAr in plant pathogen, C. lunata.

Eukaryotic and prokaryotic promoter databases as valuable tools in exploring the regulation of gene transcription: a comprehensive overview.

PubMed

Majewska, Małgorzata; Wysokińska, Halina; Kuźma, Łukasz; Szymczyk, Piotr

2018-02-20

The complete exploration of the regulation of gene expression remains one of the top-priority goals for researchers. As the regulation is mainly controlled at the level of transcription by promoters, study on promoters and findings are of great importance. This review summarizes forty selected databases that centralize experimental and theoretical knowledge regarding the organization of promoters, interacting transcription factors (TFs) and microRNAs (miRNAs) in many eukaryotic and prokaryotic species. The presented databases offer researchers valuable support in elucidating the regulation of gene transcription. Copyright © 2017 Elsevier B.V. All rights reserved.

ImmunemiR - A Database of Prioritized Immune miRNA Disease Associations and its Interactome.

PubMed

Prabahar, Archana; Natarajan, Jeyakumar

2017-01-01

MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of miRNA disease association through the interactome will deepen the understanding of its disease mechanisms. A specialized database for immune miRNAs is highly desirable to demonstrate the immune miRNA disease associations in the interactome. miRNAs specific to immune related diseases were retrieved from curated databases such as HMDD, miR2disease and PubMed literature based on MeSH classification of immune system diseases. The additional data such as miRNA target genes, genes coding protein-protein interaction information were compiled from related resources. Further, miRNAs were prioritized to specific immune diseases using random walk ranking algorithm. In total 245 immune miRNAs associated with 92 OMIM disease categories were identified from external databases. The resultant data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome. To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI) and its disease associations. It is freely available at http://www.biominingbu.org/immunemir/. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Suitability of partial 16S ribosomal RNA gene sequence analysis for the identification of dangerous bacterial pathogens.

PubMed

Ruppitsch, W; Stöger, A; Indra, A; Grif, K; Schabereiter-Gurtner, C; Hirschl, A; Allerberger, F

2007-03-01

In a bioterrorism event a rapid tool is needed to identify relevant dangerous bacteria. The aim of the study was to assess the usefulness of partial 16S rRNA gene sequence analysis and the suitability of diverse databases for identifying dangerous bacterial pathogens. For rapid identification purposes a 500-bp fragment of the 16S rRNA gene of 28 isolates comprising Bacillus anthracis, Brucella melitensis, Burkholderia mallei, Burkholderia pseudomallei, Francisella tularensis, Yersinia pestis, and eight genus-related and unrelated control strains was amplified and sequenced. The obtained sequence data were submitted to three public and two commercial sequence databases for species identification. The most frequent reason for incorrect identification was the lack of the respective 16S rRNA gene sequences in the database. Sequence analysis of a 500-bp 16S rDNA fragment allows the rapid identification of dangerous bacterial species. However, for discrimination of closely related species sequencing of the entire 16S rRNA gene, additional sequencing of the 23S rRNA gene or sequencing of the 16S-23S rRNA intergenic spacer is essential. This work provides comprehensive information on the suitability of partial 16S rDNA analysis and diverse databases for rapid and accurate identification of dangerous bacterial pathogens.

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

Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology.

PubMed

Vivar, Juan C; Pemu, Priscilla; McPherson, Ruth; Ghosh, Sujoy

2013-08-01

Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association to obesity compared to pathways identified from the original databases.

RefPrimeCouch—a reference gene primer CouchApp

PubMed Central

Silbermann, Jascha; Wernicke, Catrin; Pospisil, Heike; Frohme, Marcus

2013-01-01

To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License. Database URL: http://hpclife.th-wildau.de:5984/rpc/_design/rpc/view.html PMID:24368831

RefPrimeCouch--a reference gene primer CouchApp.

PubMed

Silbermann, Jascha; Wernicke, Catrin; Pospisil, Heike; Frohme, Marcus

2013-01-01

To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License. Database URL: http://hpclife.th-wildau.de:5984/rpc/_design/rpc/view.html.

'RetinoGenetics': a comprehensive mutation database for genes related to inherited retinal degeneration.

PubMed

Ran, Xia; Cai, Wei-Jun; Huang, Xiu-Feng; Liu, Qi; Lu, Fan; Qu, Jia; Wu, Jinyu; Jin, Zi-Bing

2014-01-01

Inherited retinal degeneration (IRD), a leading cause of human blindness worldwide, is exceptionally heterogeneous with clinical heterogeneity and genetic variety. During the past decades, tremendous efforts have been made to explore the complex heterogeneity, and massive mutations have been identified in different genes underlying IRD with the significant advancement of sequencing technology. In this study, we developed a comprehensive database, 'RetinoGenetics', which contains informative knowledge about all known IRD-related genes and mutations for IRD. 'RetinoGenetics' currently contains 4270 mutations in 186 genes, with detailed information associated with 164 phenotypes from 934 publications and various types of functional annotations. Then extensive annotations were performed to each gene using various resources, including Gene Ontology, KEGG pathways, protein-protein interaction, mutational annotations and gene-disease network. Furthermore, by using the search functions, convenient browsing ways and intuitive graphical displays, 'RetinoGenetics' could serve as a valuable resource for unveiling the genetic basis of IRD. Taken together, 'RetinoGenetics' is an integrative, informative and updatable resource for IRD-related genetic predispositions. Database URL: http://www.retinogenetics.org/. © The Author(s) 2014. Published by Oxford University Press.

Uridine monophosphate kinase as potential target for tuberculosis: from target to lead identification.

PubMed

Arvind, Akanksha; Jain, Vaibhav; Saravanan, Parameswaran; Mohan, C Gopi

2013-12-01

Mycobacterium tuberculosis (Mtb) is a causative agent of tuberculosis (TB) disease, which has affected approximately 2 billion people worldwide. Due to the emergence of resistance towards the existing drugs, discovery of new anti-TB drugs is an important global healthcare challenge. To address this problem, there is an urgent need to identify new drug targets in Mtb. In the present study, the subtractive genomics approach has been employed for the identification of new drug targets against TB. Screening the Mtb proteome using the Database of Essential Genes (DEG) and human proteome resulted in the identification of 60 key proteins which have no eukaryotic counterparts. Critical analysis of these proteins using Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways database revealed uridine monophosphate kinase (UMPK) enzyme as a potential drug target for developing novel anti-TB drugs. Homology model of Mtb-UMPK was constructed for the first time on the basis of the crystal structure of E. coli-UMPK, in order to understand its structure-function relationships, and which would in turn facilitate to perform structure-based inhibitor design. Furthermore, the structural similarity search was carried out using physiological inhibitor UTP of Mtb-UMPK to virtually screen ZINC database. Retrieved hits were further screened by implementing several filters like ADME and toxicity followed by molecular docking. Finally, on the basis of the Glide docking score and the mode of binding, 6 putative leads were identified as inhibitors of this enzyme which can potentially emerge as future drugs for the treatment of TB.

MALDI-TOF MS for identification of Tsukamurella species: Tsukamurella tyrosinosolvens as the predominant species associated with ocular infections.

PubMed

Teng, Jade L L; Tang, Ying; Wong, Samson S Y; Fong, Jordan Y H; Zhao, Zhe; Wong, Chun-Pong; Chen, Jonathan H K; Ngan, Antonio H Y; Wu, Alan K L; Fung, Kitty S C; Que, Tak-Lun; Lau, Susanna K P; Woo, Patrick C Y

2018-05-09

Although Tsukamurella infections have been increasingly reported in Europe, Asia, America, and Africa, indicating that diseases caused by this group of bacteria are emerging in a global scale, species identification within this genus is difficult in most clinical microbiology laboratories. Recently, we showed that groEL gene sequencing is useful for identification of all existing Tsukamurella species. Nevertheless, PCR sequencing is still considered expensive, time-consuming, and technically demanding, and therefore is yet to be incorporated as a routine identification method in clinical laboratories. Using groEL gene sequencing as the reference method, 60 Tsukamurella isolates were identified as five different Tsukamurella species [T. tyrosinosolvens (n = 31), T. pulmonis (n = 25), T. hongkongensis (n = 2), T. strandjordii (n = 1), and T. sinensis (n = 1)]. The most common source of the patient isolates were the eye (n = 18), sputum (n = 6), and blood (n = 6). None of the 60 isolates were identified correctly to species level by MALDI-TOF MS with the original Bruker database V.6.0.0.0. Using the Bruker database extended with 15 type and reference strains which covered all the currently recognized 11 Tsukamurella species, 59 of the 60 isolates were correctly identified to the species level with score ≥2.0. MALDI-TOF MS should be useful for routine species identification of Tsukamurella in clinical microbiology laboratories after optimization of the database. T. tyrosinosolvens was the most common species observed in patients with Tsukamurella infections and the predominant species associated with ocular infections.

[Genome-wide identification and expression analysis of auxin-related gene families in grape].

PubMed

Yuan, Hua-zhao; Zhao, Mi-zhen; Wu, Wei-min; Yu, Hong-Mei; Qian, Ya-ming; Wang, Zhuang-wei; Wang, Xi-cheng

2015-07-01

The auxin response gene family adjusts the auxin balance and the growth hormone signaling pathways in plants. Using bioinformatics methods, the auxin-response genes from the grape genome database are identified and their chromosomal location, gene collinearity and phylogenetic analysis are performed. Probable genes include 25 AUX_IAA, 19 ARF, 9 GH3 and 42 LBD genes, which are unevenly distributed on all 19 chromosomes and some of them formed distinct tandem duplicate gene clusters. The available grape microarray databases show that all of the auxin-response genes are expressed in fruit and leaf buds, and significant overexpressed during fruit color-changing, bud break and bud dormancy periods. This paper provides a resource for functional studies of auxin-response genes in grape leaf and fruit development.

Insights into the Evolutionary Relationships of LytA Autolysin and Ply Pneumolysin-Like Genes in Streptococcus pneumoniae and Related Streptococci

PubMed Central

Morales, María; Martín-Galiano, Antonio J.; Domenech, Mirian; García, Ernesto

2015-01-01

Streptococcus pneumoniae (pneumococcus) is a major human pathogen. The main pneumococcal autolysin LytA and the pneumolysin Ply are two of the bacterium’s most important virulence factors. The lytA- and ply-related genes are also found in other streptococci of the Mitis group (SMG). The precise characteristics of the lytA-related—but not the ply-related—genes of SMG and their prophages have been previously described. A search of the more than 400 SMG genomic sequences available in public databases (ca. 300 for S. pneumoniae), showed Streptococcus pseudopneumoniae IS7493 to harbor four ply-related genes, two of which (plyA and plyB) have 98% identical nucleotides. The plyA homolog of S. pseudopneumoniae is conserved in all S. pneumoniae strains, and seems to be included in a pathogenicity island together with the lytA gene. However, only nonencapsulated S. pneumoniae strains possess a plyB gene, which is part of an integrative and conjugative element. Notably, the existence of a bacterial lytA-related gene in a genome is linked to the presence of plyA and vice versa. The present analysis also shows there are eight main types of plyA−lytA genomic islands. A possible stepwise scenario for the evolution of the plyA−lytA island in S. pneumoniae is proposed. PMID:26349755

The androgen receptor gene mutations database.

PubMed

Patterson, M N; Hughes, I A; Gottlieb, B; Pinsky, L

1994-09-01

The androgen receptor gene mutations database is a comprehensive listing of mutations published in journals and meetings proceedings. The majority of mutations are point mutations identified in patients with androgen insensitivity syndrome. Information is included regarding the phenotype, the nature and location of the mutations, as well as the effects of the mutations on the androgen binding activity of the receptor. The current version of the database contains 149 entries, of which 114 are unique mutations. The database is available from EMBL (NetServ@EMBL-Heidelberg.DE) or as a Macintosh Filemaker file (mc33001@musica.mcgill.ca).

Epigenomic elements analyses for promoters identify ESRRG as a new susceptibility gene for obesity-related traits.

PubMed

Dong, S-S; Guo, Y; Zhu, D-L; Chen, X-F; Wu, X-M; Shen, H; Chen, X-D; Tan, L-J; Tian, Q; Deng, H-W; Yang, T-L

2016-07-01

With ENCODE epigenomic data and results from published genome-wide association studies (GWASs), we aimed to find regulatory signatures of obesity genes and discover novel susceptibility genes. Obesity genes were obtained from public GWAS databases and their promoters were annotated based on the regulatory element information. Significantly enriched or depleted epigenomic elements in the promoters of obesity genes were evaluated and all human genes were then prioritized according to the existence of the selected elements to predict new candidate genes. Top-ranked genes were subsequently applied to validate their associations with obesity-related traits in three independent in-house GWAS samples. We identified RAD21 and EZH2 as over-represented, and STAT2 (signal transducer and activator of transcription 2) and IRF3 (interferon regulatory transcription factor 3) as depleted transcription factors. Histone modification of H3K9me3 and chromatin state segmentation of 'poised promoter' and 'repressed' were over-represented. All genes were prioritized and we selected the top five genes for validation at the population level. Combining results from the three GWAS samples, rs7522101 in ESRRG (estrogen-related receptor-γ) remained significantly associated with body mass index after multiple testing corrections (P=7.25 × 10(-5)). It was also associated with β-cell function (P=1.99 × 10(-3)) and fasting glucose level (P<0.05) in the meta-analyses of glucose and insulin-related traits consortium (MAGIC) data set.Cnoclusions:In summary, we identified epigenomic characteristics for obesity genes and suggested ESRRG as a novel obesity-susceptibility gene.

CicerTransDB 1.0: a resource for expression and functional study of chickpea transcription factors.

PubMed

Gayali, Saurabh; Acharya, Shankar; Lande, Nilesh Vikram; Pandey, Aarti; Chakraborty, Subhra; Chakraborty, Niranjan

2016-07-29

Transcription factor (TF) databases are major resource for systematic studies of TFs in specific species as well as related family members. Even though there are several publicly available multi-species databases, the information on the amount and diversity of TFs within individual species is fragmented, especially for newly sequenced genomes of non-model species of agricultural significance. We constructed CicerTransDB (Cicer Transcription Factor Database), the first database of its kind, which would provide a centralized putatively complete list of TFs in a food legume, chickpea. CicerTransDB, available at www.cicertransdb.esy.es , is based on chickpea (Cicer arietinum L.) annotation v 1.0. The database is an outcome of genome-wide domain study and manual classification of TF families. This database not only provides information of the gene, but also gene ontology, domain and motif architecture. CicerTransDB v 1.0 comprises information of 1124 genes of chickpea and enables the user to not only search, browse and download sequences but also retrieve sequence features. CicerTransDB also provides several single click interfaces, transconnecting to various other databases to ease further analysis. Several webAPI(s) integrated in the database allow end-users direct access of data. A critical comparison of CicerTransDB with PlantTFDB (Plant Transcription Factor Database) revealed 68 novel TFs in the chickpea genome, hitherto unexplored. Database URL: http://www.cicertransdb.esy.es.

UMD-USHbases: a comprehensive set of databases to record and analyse pathogenic mutations and unclassified variants in seven Usher syndrome causing genes.

PubMed

Baux, David; Faugère, Valérie; Larrieu, Lise; Le Guédard-Méreuze, Sandie; Hamroun, Dalil; Béroud, Christophe; Malcolm, Sue; Claustres, Mireille; Roux, Anne-Françoise

2008-08-01

Using the Universal Mutation Database (UMD) software, we have constructed "UMD-USHbases", a set of relational databases of nucleotide variations for seven genes involved in Usher syndrome (MYO7A, CDH23, PCDH15, USH1C, USH1G, USH3A and USH2A). Mutations in the Usher syndrome type I causing genes are also recorded in non-syndromic hearing loss cases and mutations in USH2A in non-syndromic retinitis pigmentosa. Usher syndrome provides a particular challenge for molecular diagnostics because of the clinical and molecular heterogeneity. As many mutations are missense changes, and all the genes also contain apparently non-pathogenic polymorphisms, well-curated databases are crucial for accurate interpretation of pathogenicity. Tools are provided to assess the pathogenicity of mutations, including conservation of amino acids and analysis of splice-sites. Reference amino acid alignments are provided. Apparently non-pathogenic variants in patients with Usher syndrome, at both the nucleotide and amino acid level, are included. The UMD-USHbases currently contain more than 2,830 entries including disease causing mutations, unclassified variants or non-pathogenic polymorphisms identified in over 938 patients. In addition to data collected from 89 publications, 15 novel mutations identified in our laboratory are recorded in MYO7A (6), CDH23 (8), or PCDH15 (1) genes. Information is given on the relative involvement of the seven genes, the number and distribution of variants in each gene. UMD-USHbases give access to a software package that provides specific routines and optimized multicriteria research and sorting tools. These databases should assist clinicians and geneticists seeking information about mutations responsible for Usher syndrome.

ClusterMine360: a database of microbial PKS/NRPS biosynthesis

PubMed Central

Conway, Kyle R.; Boddy, Christopher N.

2013-01-01

ClusterMine360 (http://www.clustermine360.ca/) is a database of microbial polyketide and non-ribosomal peptide gene clusters. It takes advantage of crowd-sourcing by allowing members of the community to make contributions while automation is used to help achieve high data consistency and quality. The database currently has >200 gene clusters from >185 compound families. It also features a unique sequence repository containing >10 000 polyketide synthase/non-ribosomal peptide synthetase domains. The sequences are filterable and downloadable as individual or multiple sequence FASTA files. We are confident that this database will be a useful resource for members of the polyketide synthases/non-ribosomal peptide synthetases research community, enabling them to keep up with the growing number of sequenced gene clusters and rapidly mine these clusters for functional information. PMID:23104377

Genomic resources for songbird research and their use in characterizing gene expression during brain development

PubMed Central

Li, XiaoChing; Wang, Xiu-Jie; Tannenhauser, Jonathan; Podell, Sheila; Mukherjee, Piali; Hertel, Moritz; Biane, Jeremy; Masuda, Shoko; Nottebohm, Fernando; Gaasterland, Terry

2007-01-01

Vocal learning and neuronal replacement have been studied extensively in songbirds, but until recently, few molecular and genomic tools for songbird research existed. Here we describe new molecular/genomic resources developed in our laboratory. We made cDNA libraries from zebra finch (Taeniopygia guttata) brains at different developmental stages. A total of 11,000 cDNA clones from these libraries, representing 5,866 unique gene transcripts, were randomly picked and sequenced from the 3′ ends. A web-based database was established for clone tracking, sequence analysis, and functional annotations. Our cDNA libraries were not normalized. Sequencing ESTs without normalization produced many developmental stage-specific sequences, yielding insights into patterns of gene expression at different stages of brain development. In particular, the cDNA library made from brains at posthatching day 30–50, corresponding to the period of rapid song system development and song learning, has the most diverse and richest set of genes expressed. We also identified five microRNAs whose sequences are highly conserved between zebra finch and other species. We printed cDNA microarrays and profiled gene expression in the high vocal center of both adult male zebra finches and canaries (Serinus canaria). Genes differentially expressed in the high vocal center were identified from the microarray hybridization results. Selected genes were validated by in situ hybridization. Networks among the regulated genes were also identified. These resources provide songbird biologists with tools for genome annotation, comparative genomics, and microarray gene expression analysis. PMID:17426146

Tcof1-Related Molecular Networks in Treacher Collins Syndrome.

PubMed

Dai, Jiewen; Si, Jiawen; Wang, Minjiao; Huang, Li; Fang, Bing; Shi, Jun; Wang, Xudong; Shen, Guofang

2016-09-01

Treacher Collins syndrome (TCS) is a rare, autosomal-dominant disorder characterized by craniofacial deformities, and is primarily caused by mutations in the Tcof1 gene. This article was aimed to perform a comprehensive literature review and systematic bioinformatic analysis of Tcof1-related molecular networks in TCS. First, the up- and down-regulated genes in Tcof1 heterozygous haploinsufficient mutant mice embryos and Tcof1 knockdown and Tcof1 over-expressed neuroblastoma N1E-115 cells were obtained from the Gene Expression Omnibus database. The GeneDecks database was used to calculate the 500 genes most closely related to Tcof1. Then, the relationships between 4 gene sets (a predicted set and sets comparing the wildtype with the 3 Gene Expression Omnibus datasets) were analyzed using the DAVID, GeneMANIA and STRING databases. The analysis results showed that the Tcof1-related genes were enriched in various biological processes, including cell proliferation, apoptosis, cell cycle, differentiation, and migration. They were also enriched in several signaling pathways, such as the ribosome, p53, cell cycle, and WNT signaling pathways. Additionally, these genes clearly had direct or indirect interactions with Tcof1 and between each other. Literature review and bioinformatic analysis finds imply that special attention should be given to these pathways, as they may offer target points for TCS therapies.

dbCPG: A web resource for cancer predisposition genes

PubMed Central

Wei, Ran; Yao, Yao; Yang, Wu; Zheng, Chun-Hou; Zhao, Min; Xia, Junfeng

2016-01-01

Cancer predisposition genes (CPGs) are genes in which inherited mutations confer highly or moderately increased risks of developing cancer. Identification of these genes and understanding the biological mechanisms that underlie them is crucial for the prevention, early diagnosis, and optimized management of cancer. Over the past decades, great efforts have been made to identify CPGs through multiple strategies. However, information on these CPGs and their molecular functions is scattered. To address this issue and provide a comprehensive resource for researchers, we developed the Cancer Predisposition Gene Database (dbCPG, Database URL: http://bioinfo.ahu.edu.cn:8080/dbCPG/index.jsp), the first literature-based gene resource for exploring human CPGs. It contains 827 human (724 protein-coding, 23 non-coding, and 80 unknown type genes), 637 rats, and 658 mouse CPGs. Furthermore, data mining was performed to gain insights into the understanding of the CPGs data, including functional annotation, gene prioritization, network analysis of prioritized genes and overlap analysis across multiple cancer types. A user-friendly web interface with multiple browse, search, and upload functions was also developed to facilitate access to the latest information on CPGs. Taken together, the dbCPG database provides a comprehensive data resource for further studies of cancer predisposition genes. PMID:27192119

Arabidopsis Hormone Database: a comprehensive genetic and phenotypic information database for plant hormone research in Arabidopsis

PubMed Central

Peng, Zhi-yu; Zhou, Xin; Li, Linchuan; Yu, Xiangchun; Li, Hongjiang; Jiang, Zhiqiang; Cao, Guangyu; Bai, Mingyi; Wang, Xingchun; Jiang, Caifu; Lu, Haibin; Hou, Xianhui; Qu, Lijia; Wang, Zhiyong; Zuo, Jianru; Fu, Xiangdong; Su, Zhen; Li, Songgang; Guo, Hongwei

2009-01-01

Plant hormones are small organic molecules that influence almost every aspect of plant growth and development. Genetic and molecular studies have revealed a large number of genes that are involved in responses to numerous plant hormones, including auxin, gibberellin, cytokinin, abscisic acid, ethylene, jasmonic acid, salicylic acid, and brassinosteroid. Here, we develop an Arabidopsis hormone database, which aims to provide a systematic and comprehensive view of genes participating in plant hormonal regulation, as well as morphological phenotypes controlled by plant hormones. Based on data from mutant studies, transgenic analysis and gene ontology (GO) annotation, we have identified a total of 1026 genes in the Arabidopsis genome that participate in plant hormone functions. Meanwhile, a phenotype ontology is developed to precisely describe myriad hormone-regulated morphological processes with standardized vocabularies. A web interface (http://ahd.cbi.pku.edu.cn) would allow users to quickly get access to information about these hormone-related genes, including sequences, functional category, mutant information, phenotypic description, microarray data and linked publications. Several applications of this database in studying plant hormonal regulation and hormone cross-talk will be presented and discussed. PMID:19015126

Arabidopsis Hormone Database: a comprehensive genetic and phenotypic information database for plant hormone research in Arabidopsis.

PubMed

Peng, Zhi-yu; Zhou, Xin; Li, Linchuan; Yu, Xiangchun; Li, Hongjiang; Jiang, Zhiqiang; Cao, Guangyu; Bai, Mingyi; Wang, Xingchun; Jiang, Caifu; Lu, Haibin; Hou, Xianhui; Qu, Lijia; Wang, Zhiyong; Zuo, Jianru; Fu, Xiangdong; Su, Zhen; Li, Songgang; Guo, Hongwei

2009-01-01

Plant hormones are small organic molecules that influence almost every aspect of plant growth and development. Genetic and molecular studies have revealed a large number of genes that are involved in responses to numerous plant hormones, including auxin, gibberellin, cytokinin, abscisic acid, ethylene, jasmonic acid, salicylic acid, and brassinosteroid. Here, we develop an Arabidopsis hormone database, which aims to provide a systematic and comprehensive view of genes participating in plant hormonal regulation, as well as morphological phenotypes controlled by plant hormones. Based on data from mutant studies, transgenic analysis and gene ontology (GO) annotation, we have identified a total of 1026 genes in the Arabidopsis genome that participate in plant hormone functions. Meanwhile, a phenotype ontology is developed to precisely describe myriad hormone-regulated morphological processes with standardized vocabularies. A web interface (http://ahd.cbi.pku.edu.cn) would allow users to quickly get access to information about these hormone-related genes, including sequences, functional category, mutant information, phenotypic description, microarray data and linked publications. Several applications of this database in studying plant hormonal regulation and hormone cross-talk will be presented and discussed.

CellLineNavigator: a workbench for cancer cell line analysis

PubMed Central

Krupp, Markus; Itzel, Timo; Maass, Thorsten; Hildebrandt, Andreas; Galle, Peter R.; Teufel, Andreas

2013-01-01

The CellLineNavigator database, freely available at http://www.medicalgenomics.org/celllinenavigator, is a web-based workbench for large scale comparisons of a large collection of diverse cell lines. It aims to support experimental design in the fields of genomics, systems biology and translational biomedical research. Currently, this compendium holds genome wide expression profiles of 317 different cancer cell lines, categorized into 57 different pathological states and 28 individual tissues. To enlarge the scope of CellLineNavigator, the database was furthermore closely linked to commonly used bioinformatics databases and knowledge repositories. To ensure easy data access and search ability, a simple data and an intuitive querying interface were implemented. It allows the user to explore and filter gene expression, focusing on pathological or physiological conditions. For a more complex search, the advanced query interface may be used to query for (i) differentially expressed genes; (ii) pathological or physiological conditions; or (iii) gene names or functional attributes, such as Kyoto Encyclopaedia of Genes and Genomes pathway maps. These queries may also be combined. Finally, CellLineNavigator allows additional advanced analysis of differentially regulated genes by a direct link to the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources. PMID:23118487

CyanoBase: the cyanobacteria genome database update 2010.

PubMed

Nakao, Mitsuteru; Okamoto, Shinobu; Kohara, Mitsuyo; Fujishiro, Tsunakazu; Fujisawa, Takatomo; Sato, Shusei; Tabata, Satoshi; Kaneko, Takakazu; Nakamura, Yasukazu

2010-01-01

CyanoBase (http://genome.kazusa.or.jp/cyanobase) is the genome database for cyanobacteria, which are model organisms for photosynthesis. The database houses cyanobacteria species information, complete genome sequences, genome-scale experiment data, gene information, gene annotations and mutant information. In this version, we updated these datasets and improved the navigation and the visual display of the data views. In addition, a web service API now enables users to retrieve the data in various formats with other tools, seamlessly.

The UMD-p53 database: new mutations and analysis tools.

PubMed

Béroud, Christophe; Soussi, Thierry

2003-03-01

The tumor suppressor gene TP53 (p53) is the most extensively studied gene involved in human cancers. More than 1,400 publications have reported mutations of this gene in 150 cancer types for a total of 14,971 mutations. To exploit this huge bulk of data, specific analytic tools were highly warranted. We therefore developed a locus-specific database software called UMD-p53. This database compiles all somatic and germline mutations as well as polymorphisms of the TP53 gene which have been reported in the published literature since 1989, or unpublished data submitted to the database curators. The database is available at www.umd.necker.fr or at http://p53.curie.fr/. In this paper, we describe recent developments of the UMD-p53 database. These developments include new fields and routines. For example, the analysis of putative acceptor or donor splice sites is now automated and gives new insight for the causal role of "silent mutations." Other routines have also been created such as the prescreening module, the UV module, and the cancer distribution module. These new improvements will help users not only for molecular epidemiology and pharmacogenetic studies but also for patient-based studies. To achieve theses purposes we have designed a procedure to check and validate data in order to reach the highest quality data. Copyright 2003 Wiley-Liss, Inc.

Freshwater Biological Traits Database (Final Report)

EPA Science Inventory

EPA announced the release of the final report, Freshwater Biological Traits Database. This report discusses the development of a database of freshwater biological traits. The database combines several existing traits databases into an online format. The database is also...

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery

PubMed Central

Feng, Chunlai; Araki, Michihiro; Kunimoto, Ryo; Tamon, Akiko; Makiguchi, Hiroki; Niijima, Satoshi; Tsujimoto, Gozoh; Okuno, Yasushi

2009-01-01

Background DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. Results GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. Conclusion GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at . PMID:19728865

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

PubMed

Feng, Chunlai; Araki, Michihiro; Kunimoto, Ryo; Tamon, Akiko; Makiguchi, Hiroki; Niijima, Satoshi; Tsujimoto, Gozoh; Okuno, Yasushi

2009-09-03

DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network.

1-CMDb: A Curated Database of Genomic Variations of the One-Carbon Metabolism Pathway.

PubMed

Bhat, Manoj K; Gadekar, Veerendra P; Jain, Aditya; Paul, Bobby; Rai, Padmalatha S; Satyamoorthy, Kapaettu

2017-01-01

The one-carbon metabolism pathway is vital in maintaining tissue homeostasis by driving the critical reactions of folate and methionine cycles. A myriad of genetic and epigenetic events mark the rate of reactions in a tissue-specific manner. Integration of these to predict and provide personalized health management requires robust computational tools that can process multiomics data. The DNA sequences that may determine the chain of biological events and the endpoint reactions within one-carbon metabolism genes remain to be comprehensively recorded. Hence, we designed the one-carbon metabolism database (1-CMDb) as a platform to interrogate its association with a host of human disorders. DNA sequence and network information of a total of 48 genes were extracted from a literature survey and KEGG pathway that are involved in the one-carbon folate-mediated pathway. The information generated, collected, and compiled for all these genes from the UCSC genome browser included the single nucleotide polymorphisms (SNPs), CpGs, copy number variations (CNVs), and miRNAs, and a comprehensive database was created. Furthermore, a significant correlation analysis was performed for SNPs in the pathway genes. Detailed data of SNPs, CNVs, CpG islands, and miRNAs for 48 folate pathway genes were compiled. The SNPs in CNVs (9670), CpGs (984), and miRNAs (14) were also compiled for all pathway genes. The SIFT score, the prediction and PolyPhen score, as well as the prediction for each of the SNPs were tabulated and represented for folate pathway genes. Also included in the database for folate pathway genes were the links to 124 various phenotypes and disease associations as reported in the literature and from publicly available information. A comprehensive database was generated consisting of genomic elements within and among SNPs, CNVs, CpGs, and miRNAs of one-carbon metabolism pathways to facilitate (a) single source of information and (b) integration into large-genome scale network analysis to be developed in the future by the scientific community. The database can be accessed at http://slsdb.manipal.edu/ocm/. © 2017 S. Karger AG, Basel.

PTGBase: an integrated database to study tandem duplicated genes in plants.

PubMed

Yu, Jingyin; Ke, Tao; Tehrim, Sadia; Sun, Fengming; Liao, Boshou; Hua, Wei

2015-01-01

Tandem duplication is a wide-spread phenomenon in plant genomes and plays significant roles in evolution and adaptation to changing environments. Tandem duplicated genes related to certain functions will lead to the expansion of gene families and bring increase of gene dosage in the form of gene cluster arrays. Many tandem duplication events have been studied in plant genomes; yet, there is a surprising shortage of efforts to systematically present the integration of large amounts of information about publicly deposited tandem duplicated gene data across the plant kingdom. To address this shortcoming, we developed the first plant tandem duplicated genes database, PTGBase. It delivers the most comprehensive resource available to date, spanning 39 plant genomes, including model species and newly sequenced species alike. Across these genomes, 54 130 tandem duplicated gene clusters (129 652 genes) are presented in the database. Each tandem array, as well as its member genes, is characterized in complete detail. Tandem duplicated genes in PTGBase can be explored through browsing or searching by identifiers or keywords of functional annotation and sequence similarity. Users can download tandem duplicated gene arrays easily to any scale, up to the complete annotation data set for an entire plant genome. PTGBase will be updated regularly with newly sequenced plant species as they become available. © The Author(s) 2015. Published by Oxford University Press.

Genome-wide identification and evolution of the PIN-FORMED (PIN) gene family in Glycine max.

PubMed

Liu, Yuan; Wei, Haichao

2017-07-01

Soybean (Glycine max) is one of the most important crop plants. Wild and cultivated soybean varieties have significant differences worth further investigation, such as plant morphology, seed size, and seed coat development; these characters may be related to auxin biology. The PIN gene family encodes essential transport proteins in cell-to-cell auxin transport, but little research on soybean PIN genes (GmPIN genes) has been done, especially with respect to the evolution and differences between wild and cultivated soybean. In this study, we retrieved 23 GmPIN genes from the latest updated G. max genome database; six GmPIN protein sequences were changed compared with the previous database. Based on the Plant Genome Duplication Database, 18 GmPIN genes have been involved in segment duplication. Three pairs of GmPIN genes arose after the second soybean genome duplication, and six occurred after the first genome duplication. The duplicated GmPIN genes retained similar expression patterns. All the duplicated GmPIN genes experienced purifying selection (K a /K s < 1) to prevent accumulation of non-synonymous mutations and thus remained more similar. In addition, we also focused on the artificial selection of the soybean PIN genes. Five artificially selected GmPIN genes were identified by comparing the genome sequence of 17 wild and 14 cultivated soybean varieties. Our research provides useful and comprehensive basic information for understanding GmPIN genes.

GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.

PubMed

Subhash, Santhilal; Kanduri, Chandrasekhar

2016-09-13

High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

Possible association between Helicobacter pylori infection and nonalcoholic fatty liver disease.

PubMed

Chen, Chang-Xi; Mao, Yu-Shan; Foster, Parker; Zhu, Zhong-Wei; Du, Juan; Guo, Chuan-Yong

2017-03-01

Possible association between Helicobacter pylori infection (HPI) and nonalcoholic fatty liver disease (NAFLD) has been proposed by several studies with inconsistent conclusions. Here, we studied the association between HPI and NAFLD at 3 levels: (i) genetic level; (ii) small molecular level; and (iii) clinical level. Relation data between diseases, genes, and small molecules were acquired from Pathway Studio ResNet Mammalian database. Clinical data were acquired from 2263 elderly South Chinese subjects, including 603 NAFLD patients and 1660 subjects without NAFLD. Results showed that HPI and NAFLD present significantly shared genetic bases (95 genes, p value = 2.5E-72), demonstrating multiple common genetic pathways (enrichment p value ≤ 4.38E-20 for the top 10 pathways). Genetic network analysis suggested that mutual regulation may exist between HPI and NAFLD through 21 out of 95 genes. Furthermore, 85 out of the 95 genes manifested strong interaction with 12 small molecules/drugs that demonstrate effectiveness in treating both diseases. Clinical results showed that HPI rate in the NAFLD group was significantly higher than that in the group without NAFLD (51.9% vs. 43.6%; p value = 4.9E-4). Multivariate logistic regression results supported the observations and suggested that HPI served as a risk factor for NAFLD in the experiment data studied (odds ratio: 1.387, p value = 0.018). Results from this study support the hypothesis that complex biological association may exist between HPI and NAFLD, which partially explains the significant clinical co-incidence in the elderly population of south China.

Human Chromosome Y and Haplogroups; introducing YDHS Database.

PubMed

Tiirikka, Timo; Moilanen, Jukka S

2015-12-01

As the high throughput sequencing efforts generate more biological information, scientists from different disciplines are interpreting the polymorphisms that make us unique. In addition, there is an increasing trend in general public to research their own genealogy, find distant relatives and to know more about their biological background. Commercial vendors are providing analyses of mitochondrial and Y-chromosomal markers for such purposes. Clearly, an easy-to-use free interface to the existing data on the identified variants would be in the interest of general public and professionals less familiar with the field. Here we introduce a novel metadatabase YDHS that aims to provide such an interface for Y-chromosomal DNA (Y-DNA) haplogroups and sequence variants. The database uses ISOGG Y-DNA tree as the source of mutations and haplogroups and by using genomic positions of the mutations the database links them to genes and other biological entities. YDHS contains analysis tools for deeper Y-SNP analysis. YDHS addresses the shortage of Y-DNA related databases. We have tested our database using a set of different cases from literature ranging from infertility to autism. The database is at http://www.semanticgen.net/ydhs Y-chromosomal DNA (Y-DNA) haplogroups and sequence variants have not been in the scientific limelight, excluding certain specialized fields like forensics, mainly because there is not much freely available information or it is scattered in different sources. However, as we have demonstrated Y-SNPs do play a role in various cases on the haplogroup level and it is possible to create a free Y-DNA dedicated bioinformatics resource.

Regulators of Androgen Action Resource: a one-stop shop for the comprehensive study of androgen receptor action.

PubMed

DePriest, Adam D; Fiandalo, Michael V; Schlanger, Simon; Heemers, Frederike; Mohler, James L; Liu, Song; Heemers, Hannelore V

2016-01-01

Androgen receptor (AR) is a ligand-activated transcription factor that is the main target for treatment of non-organ-confined prostate cancer (CaP). Failure of life-prolonging AR-targeting androgen deprivation therapy is due to flexibility in steroidogenic pathways that control intracrine androgen levels and variability in the AR transcriptional output. Androgen biosynthesis enzymes, androgen transporters and AR-associated coregulators are attractive novel CaP treatment targets. These proteins, however, are characterized by multiple transcript variants and isoforms, are subject to genomic alterations, and are differentially expressed among CaPs. Determining their therapeutic potential requires evaluation of extensive, diverse datasets that are dispersed over multiple databases, websites and literature reports. Mining and integrating these datasets are cumbersome, time-consuming tasks and provide only snapshots of relevant information. To overcome this impediment to effective, efficient study of AR and potential drug targets, we developed the Regulators of Androgen Action Resource (RAAR), a non-redundant, curated and user-friendly searchable web interface. RAAR centralizes information on gene function, clinical relevance, and resources for 55 genes that encode proteins involved in biosynthesis, metabolism and transport of androgens and for 274 AR-associated coregulator genes. Data in RAAR are organized in two levels: (i) Information pertaining to production of androgens is contained in a 'pre-receptor level' database, and coregulator gene information is provided in a 'post-receptor level' database, and (ii) an 'other resources' database contains links to additional databases that are complementary to and useful to pursue further the information provided in RAAR. For each of its 329 entries, RAAR provides access to more than 20 well-curated publicly available databases, and thus, access to thousands of data points. Hyperlinks provide direct access to gene-specific entries in the respective database(s). RAAR is a novel, freely available resource that provides fast, reliable and easy access to integrated information that is needed to develop alternative CaP therapies. Database URL: http://www.lerner.ccf.org/cancerbio/heemers/RAAR/search/. © The Author(s) 2016. Published by Oxford University Press.

PvTFDB: a Phaseolus vulgaris transcription factors database for expediting functional genomics in legumes.

PubMed

Bhawna; Bonthala, V S; Gajula, Mnv Prasad

2016-01-01

The common bean [Phaseolus vulgaris (L.)] is one of the essential proteinaceous vegetables grown in developing countries. However, its production is challenged by low yields caused by numerous biotic and abiotic stress conditions. Regulatory transcription factors (TFs) symbolize a key component of the genome and are the most significant targets for producing stress tolerant crop and hence functional genomic studies of these TFs are important. Therefore, here we have constructed a web-accessible TFs database for P. vulgaris, called PvTFDB, which contains 2370 putative TF gene models in 49 TF families. This database provides a comprehensive information for each of the identified TF that includes sequence data, functional annotation, SSRs with their primer sets, protein physical properties, chromosomal location, phylogeny, tissue-specific gene expression data, orthologues, cis-regulatory elements and gene ontology (GO) assignment. Altogether, this information would be used in expediting the functional genomic studies of a specific TF(s) of interest. The objectives of this database are to understand functional genomics study of common bean TFs and recognize the regulatory mechanisms underlying various stress responses to ease breeding strategy for variety production through a couple of search interfaces including gene ID, functional annotation and browsing interfaces including by family and by chromosome. This database will also serve as a promising central repository for researchers as well as breeders who are working towards crop improvement of legume crops. In addition, this database provide the user unrestricted public access and the user can download entire data present in the database freely.Database URL: http://www.multiomics.in/PvTFDB/. © The Author(s) 2016. Published by Oxford University Press.

Highly Conserved Mitochondrial Genomes among Multicellular Red Algae of the Florideophyceae

PubMed Central

Yang, Eun Chan; Kim, Kyeong Mi; Kim, Su Yeon; Lee, JunMo; Boo, Ga Hun; Lee, Jung-Hyun; Nelson, Wendy A.; Yi, Gangman; Schmidt, William E.; Fredericq, Suzanne; Boo, Sung Min; Bhattacharya, Debashish; Yoon, Hwan Su

2015-01-01

Two red algal classes, the Florideophyceae (approximately 7,100 spp.) and Bangiophyceae (approximately 193 spp.), comprise 98% of red algal diversity in marine and freshwater habitats. These two classes form well-supported monophyletic groups in most phylogenetic analyses. Nonetheless, the interordinal relationships remain largely unresolved, in particular in the largest subclass Rhodymeniophycidae that includes 70% of all species. To elucidate red algal phylogenetic relationships and study organelle evolution, we determined the sequence of 11 mitochondrial genomes (mtDNA) from 5 florideophycean subclasses. These mtDNAs were combined with existing data, resulting in a database of 25 florideophytes and 12 bangiophytes (including cyanidiophycean species). A concatenated alignment of mt proteins was used to resolve ordinal relationships in the Rhodymeniophycidae. Red algal mtDNA genome comparisons showed 47 instances of gene rearrangement including 12 that distinguish Bangiophyceae from Hildenbrandiophycidae, and 5 that distinguish Hildenbrandiophycidae from Nemaliophycidae. These organelle data support a rapid radiation and surprisingly high conservation of mtDNA gene syntheny among the morphologically divergent multicellular lineages of Rhodymeniophycidae. In contrast, we find extensive mitochondrial gene rearrangements when comparing Bangiophyceae and Florideophyceae and multiple examples of gene loss among the different red algal lineages. PMID:26245677

The Pax gene family: Highlights from cephalopods

PubMed Central

Baratte, Sébastien; Andouche, Aude; Bonnaud-Ponticelli, Laure

2017-01-01

Pax genes play important roles in Metazoan development. Their evolution has been extensively studied but Lophotrochozoa are usually omitted. We addressed the question of Pax paralog diversity in Lophotrochozoa by a thorough review of available databases. The existence of six Pax families (Pax1/9, Pax2/5/8, Pax3/7, Pax4/6, Paxβ, PoxNeuro) was confirmed and the lophotrochozoan Paxβ subfamily was further characterized. Contrary to the pattern reported in chordates, the Pax2/5/8 family is devoid of homeodomain in Lophotrochozoa. Expression patterns of the three main pax classes (pax2/5/8, pax3/7, pax4/6) during Sepia officinalis development showed that Pax roles taken as ancestral and common in metazoans are modified in S. officinalis, most likely due to either the morphological specificities of cephalopods or to their direct development. Some expected expression patterns were missing (e.g. pax6 in the developing retina), and some expressions in unexpected tissues have been found (e.g. pax2/5/8 in dermal tissue and in gills). This study underlines the diversity and functional plasticity of Pax genes and illustrates the difficulty of using probable gene homology as strict indicator of homology between biological structures. PMID:28253300

Extracellular matrix family proteins that are potential targets of Dd-STATa in Dictyostelium discoideum.

PubMed

Shimada, Nao; Nishio, Keiko; Maeda, Mineko; Urushihara, Hideko; Kawata, Takefumi

2004-10-01

Dd-STATa is a functional Dictyostelium homologue of metazoan STAT (signal transducers and activators of transcription) proteins, which is activated by cAMP and is thereby translocated into the nuclei of anterior tip cells of the prestalk region of the slug. By using in situ hybridization analyses, we found that the SLF308 cDNA clone, which contains the ecmF gene that encodes a putative extracellular matrix protein and is expressed in the anterior tip cells, was greatly down-regulated in the Dd-STATa-null mutant. Disruption of the ecmF gene, however, resulted in almost no phenotypic change. The absence of any obvious mutant phenotype in the ecmF-null mutant could be due to a redundancy of similar genes. In fact, a search of the Dictyostelium whole genome database demonstrates the existence of an additional 16 homologues, all of which contain a cellulose-binding module. Among these homologues, four genes show Dd-STATa-dependent expression, while the others are Dd-STATa-independent. We discuss the potential role of Dd-STATa in morphogenesis via its effect on the interaction between cellulose and these extracellular matrix family proteins.

Using comparative genome analysis to identify problems in annotated microbial genomes.

PubMed

Poptsova, Maria S; Gogarten, J Peter

2010-07-01

Genome annotation is a tedious task that is mostly done by automated methods; however, the accuracy of these approaches has been questioned since the beginning of the sequencing era. Genome annotation is a multilevel process, and errors can emerge at different stages: during sequencing, as a result of gene-calling procedures, and in the process of assigning gene functions. Missed or wrongly annotated genes differentially impact different types of analyses. Here we discuss and demonstrate how the methods of comparative genome analysis can refine annotations by locating missing orthologues. We also discuss possible reasons for errors and show that the second-generation annotation systems, which combine multiple gene-calling programs with similarity-based methods, perform much better than the first annotation tools. Since old errors may propagate to the newly sequenced genomes, we emphasize that the problem of continuously updating popular public databases is an urgent and unresolved one. Due to the progress in genome-sequencing technologies, automated annotation techniques will remain the main approach in the future. Researchers need to be aware of the existing errors in the annotation of even well-studied genomes, such as Escherichia coli, and consider additional quality control for their results.

Computational biology for ageing

PubMed Central

Wieser, Daniela; Papatheodorou, Irene; Ziehm, Matthias; Thornton, Janet M.

2011-01-01

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions. PMID:21115530

Determining Clostridium difficile intra-taxa diversity by mining multilocus sequence typing databases.

PubMed

Muñoz, Marina; Ríos-Chaparro, Dora Inés; Patarroyo, Manuel Alfonso; Ramírez, Juan David

2017-03-14

Multilocus sequence typing (MLST) is a highly discriminatory typing strategy; it is reproducible and scalable. There is a MLST scheme for Clostridium difficile (CD), a gram positive bacillus causing different pathologies of the gastrointestinal tract. This work was aimed at describing the frequency of sequence types (STs) and Clades (C) reported and evalute the intra-taxa diversity in the CD MLST database (CD-MLST-db) using an MLSA approach. Analysis of 1778 available isolates showed that clade 1 (C1) was the most frequent worldwide (57.7%), followed by C2 (29.1%). Regarding sequence types (STs), it was found that ST-1, belonging to C2, was the most frequent. The isolates analysed came from 17 countries, mostly from the United Kingdom (UK) (1541 STs, 87.0%). The diversity of the seven housekeeping genes in the MLST scheme was evaluated, and alleles from the profiles (STs), for identifying CD population structure. It was found that adk and atpA are conserved genes allowing a limited amount of clusters to be discriminated; however, different genes such as drx, glyA and particularly sodA showed high diversity indexes and grouped CD populations in many clusters, suggesting that these genes' contribution to CD typing should be revised. It was identified that CD STs reported to date have a mostly clonal population structure with foreseen events of recombination; however, one group of STs was not assigned to a clade being highly different containing at least nine well-supported clusters, suggesting a greater amount of clades for CD. This study shows the usefulness of CD-MLST-db as a tool for studying CD distribution and population structure, identifying the need for reviewing the usefulness of sodA as housekeeping gene within the MLST scheme and suggesting the existence of a greater amount of CD clades. The study also shows the plausible exchange of genetic material between STs, contributing towards intra-taxa genetic diversity.

Viral Genome DataBase: storing and analyzing genes and proteins from complete viral genomes.

PubMed

Hiscock, D; Upton, C

2000-05-01

The Viral Genome DataBase (VGDB) contains detailed information of the genes and predicted protein sequences from 15 completely sequenced genomes of large (&100 kb) viruses (2847 genes). The data that is stored includes DNA sequence, protein sequence, GenBank and user-entered notes, molecular weight (MW), isoelectric point (pI), amino acid content, A + T%, nucleotide frequency, dinucleotide frequency and codon use. The VGDB is a mySQL database with a user-friendly JAVA GUI. Results of queries can be easily sorted by any of the individual parameters. The software and additional figures and information are available at http://athena.bioc.uvic.ca/genomes/index.html .

Healthcare Databases in Thailand and Japan: Potential Sources for Health Technology Assessment Research.

PubMed

Saokaew, Surasak; Sugimoto, Takashi; Kamae, Isao; Pratoomsoot, Chayanin; Chaiyakunapruk, Nathorn

2015-01-01

Health technology assessment (HTA) has been continuously used for value-based healthcare decisions over the last decade. Healthcare databases represent an important source of information for HTA, which has seen a surge in use in Western countries. Although HTA agencies have been established in Asia-Pacific region, application and understanding of healthcare databases for HTA is rather limited. Thus, we reviewed existing databases to assess their potential for HTA in Thailand where HTA has been used officially and Japan where HTA is going to be officially introduced. Existing healthcare databases in Thailand and Japan were compiled and reviewed. Databases' characteristics e.g. name of database, host, scope/objective, time/sample size, design, data collection method, population/sample, and variables were described. Databases were assessed for its potential HTA use in terms of safety/efficacy/effectiveness, social/ethical, organization/professional, economic, and epidemiological domains. Request route for each database was also provided. Forty databases- 20 from Thailand and 20 from Japan-were included. These comprised of national censuses, surveys, registries, administrative data, and claimed databases. All databases were potentially used for epidemiological studies. In addition, data on mortality, morbidity, disability, adverse events, quality of life, service/technology utilization, length of stay, and economics were also found in some databases. However, access to patient-level data was limited since information about the databases was not available on public sources. Our findings have shown that existing databases provided valuable information for HTA research with limitation on accessibility. Mutual dialogue on healthcare database development and usage for HTA among Asia-Pacific region is needed.

Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

PubMed Central

Chang, Chia-Ming; Chuang, Chi-Mu; Wang, Mong-Lien; Yang, Yi-Ping; Chuang, Jen-Hua; Yang, Ming-Jie; Yen, Ming-Shyen; Chiou, Shih-Hwa; Chang, Cheng-Chang

2016-01-01

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis. PMID:27527159

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Sequence evaluation of four specific cDNA libraries for developmental genomics of sunflower.

PubMed

Tamborindeguy, C; Ben, C; Liboz, T; Gentzbittel, L

2004-04-01

Four different cDNA libraries were constructed from sunflower protoplasts growing under embryogenic and non-embryogenic conditions: one standard library from each condition and two subtractive libraries in opposite sense. A total of 22,876 cDNA clones were obtained and 4800 ESTs were sequenced, giving rise to 2479 high quality ESTs representing an unigene set of 1502 sequences. This set was compared with ESTs represented in public databases using the programs BLASTN and BLASTX, and its members were classified according to putative function using the catalog in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Some 33% of sequences failed to align with existing plant ESTs and therefore represent putative novel genes. The libraries show a low level of redundancy and, on average, 50% of the present ESTs have not been previously reported for sunflower. Several potentially interesting genes were identified, based on their homology with genes involved in animal zygotic division or plant embryogenesis. We also identified two ESTs that show significantly different levels of expression under embryogenic and non-embryogenic conditions. The libraries described here represent an original and valuable resource for the discovery of yet unknown genes putatively involved in dicot embryogenesis and improving our knowledge of the mechanisms involved in polarity acquisition by plant embryos.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Identification and characterization of UDP-glucose:Phloretin 4'-O-glycosyltransferase from Malus x domestica Borkh.

PubMed

Yahyaa, Mosaab; Davidovich-Rikanati, Rachel; Eyal, Yoram; Sheachter, Alona; Marzouk, Sally; Lewinsohn, Efraim; Ibdah, Mwafaq

2016-10-01

Apples (Malus x domestica Brokh.) are among the world's most important food crops with nutritive and medicinal importance. Many of the health beneficial properties of apple fruit are suggested to be due to (poly)phenolic metabolites, including various dihydrochalcones. Although many of the genes and enzymes involved in polyphenol biosynthesis are known in many plant species, the specific reactions that lead to the biosynthesis of the sweet tasting dihydrochalcones, such as trilobatin, are unknown. To identify candidate genes for involvement in the glycosylation of dihydrochalcones, existing genome databases of the Rosaceae were screened for apple genes with significant sequence similarity to Bacillus subtilis phloretin glycosyltransferase. Herein reported is the identification and functional characterization of a Malus x domestica gene encoding phloretin-4'-O-glycosyltransferase designated MdPh-4'-OGT. Recombinant MdPh-4'-OGT protein glycosylates phloretin in the presence of UDP-glucose into trilobatin in vitro. Its apparent Km values for phloretin and UDP-glucose were 26.1 μM and 1.2 mM, respectively. Expression analysis of the MdPh-4'-OGT gene indicated that its transcript levels showed significant variation in apple tissues of different developmental stages. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

iMETHYL: an integrative database of human DNA methylation, gene expression, and genomic variation.

PubMed

Komaki, Shohei; Shiwa, Yuh; Furukawa, Ryohei; Hachiya, Tsuyoshi; Ohmomo, Hideki; Otomo, Ryo; Satoh, Mamoru; Hitomi, Jiro; Sobue, Kenji; Sasaki, Makoto; Shimizu, Atsushi

2018-01-01

We launched an integrative multi-omics database, iMETHYL (http://imethyl.iwate-megabank.org). iMETHYL provides whole-DNA methylation (~24 million autosomal CpG sites), whole-genome (~9 million single-nucleotide variants), and whole-transcriptome (>14 000 genes) data for CD4 + T-lymphocytes, monocytes, and neutrophils collected from approximately 100 subjects. These data were obtained from whole-genome bisulfite sequencing, whole-genome sequencing, and whole-transcriptome sequencing, making iMETHYL a comprehensive database.

A 5.8S nuclear ribosomal RNA gene sequence database: applications to ecology and evolution

NASA Technical Reports Server (NTRS)

Cullings, K. W.; Vogler, D. R.

1998-01-01

We complied a 5.8S nuclear ribosomal gene sequence database for animals, plants, and fungi using both newly generated and GenBank sequences. We demonstrate the utility of this database as an internal check to determine whether the target organism and not a contaminant has been sequenced, as a diagnostic tool for ecologists and evolutionary biologists to determine the placement of asexual fungi within larger taxonomic groups, and as a tool to help identify fungi that form ectomycorrhizae.

CyanoBase: the cyanobacteria genome database update 2010

PubMed Central

Nakao, Mitsuteru; Okamoto, Shinobu; Kohara, Mitsuyo; Fujishiro, Tsunakazu; Fujisawa, Takatomo; Sato, Shusei; Tabata, Satoshi; Kaneko, Takakazu; Nakamura, Yasukazu

2010-01-01

CyanoBase (http://genome.kazusa.or.jp/cyanobase) is the genome database for cyanobacteria, which are model organisms for photosynthesis. The database houses cyanobacteria species information, complete genome sequences, genome-scale experiment data, gene information, gene annotations and mutant information. In this version, we updated these datasets and improved the navigation and the visual display of the data views. In addition, a web service API now enables users to retrieve the data in various formats with other tools, seamlessly. PMID:19880388

The androgen receptor gene mutations database.

PubMed

Gottlieb, B; Trifiro, M; Lumbroso, R; Vasiliou, D M; Pinsky, L

1996-01-01

The current version of the androgen receptor (AR) gene mutations database is described. We have added (if available) data on the androgen binding phenotype of the mutant AR, the clinical phenotype of the affected persons, the family history and whether the pathogenicity of a mutation has been proven. Exonic mutations are now listed in 5'-->3' sequence regardless of type and single base pair changes are presented in codon context. Splice site and intronic mutations are listed separately. The database has allowed us to substantiate and amplify the observation of mutational hot spots within exons encoding the AR androgen binding domain. The database is available from EML (ftp://www.ebi.ac.uk/pub/databases/androgen) or as a Macintosh Filemaker file (MC33@musica.mcgill.ca).

PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database.

PubMed

Huang, Weiliang; Brewer, Luke K; Jones, Jace W; Nguyen, Angela T; Marcu, Ana; Wishart, David S; Oglesby-Sherrouse, Amanda G; Kane, Maureen A; Wilks, Angela

2018-01-04

The Pseudomonas aeruginosaMetabolome Database (PAMDB, http://pseudomonas.umaryland.edu) is a searchable, richly annotated metabolite database specific to P. aeruginosa. P. aeruginosa is a soil organism and significant opportunistic pathogen that adapts to its environment through a versatile energy metabolism network. Furthermore, P. aeruginosa is a model organism for the study of biofilm formation, quorum sensing, and bioremediation processes, each of which are dependent on unique pathways and metabolites. The PAMDB is modelled on the Escherichia coli (ECMDB), yeast (YMDB) and human (HMDB) metabolome databases and contains >4370 metabolites and 938 pathways with links to over 1260 genes and proteins. The database information was compiled from electronic databases, journal articles and mass spectrometry (MS) metabolomic data obtained in our laboratories. For each metabolite entered, we provide detailed compound descriptions, names and synonyms, structural and physiochemical information, nuclear magnetic resonance (NMR) and MS spectra, enzymes and pathway information, as well as gene and protein sequences. The database allows extensive searching via chemical names, structure and molecular weight, together with gene, protein and pathway relationships. The PAMBD and its future iterations will provide a valuable resource to biologists, natural product chemists and clinicians in identifying active compounds, potential biomarkers and clinical diagnostics. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary.

PubMed

Akiyama, Kenji; Kurotani, Atsushi; Iida, Kei; Kuromori, Takashi; Shinozaki, Kazuo; Sakurai, Tetsuya

2014-01-01

Arabidopsis thaliana is one of the most popular experimental plants. However, only 40% of its genes have at least one experimental Gene Ontology (GO) annotation assigned. Systematic observation of mutant phenotypes is an important technique for elucidating gene functions. Indeed, several large-scale phenotypic analyses have been performed and have generated phenotypic data sets from many Arabidopsis mutant lines and overexpressing lines, which are freely available online. Since each Arabidopsis mutant line database uses individual phenotype expression, the differences in the structured term sets used by each database make it difficult to compare data sets and make it impossible to search across databases. Therefore, we obtained publicly available information for a total of 66,209 Arabidopsis mutant lines, including loss-of-function (RATM and TARAPPER) and gain-of-function (AtFOX and OsFOX) lines, and integrated the phenotype data by mapping the descriptions onto Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) terms. This approach made it possible to manage the four different phenotype databases as one large data set. Here, we report a publicly accessible web-based database, the RIKEN Arabidopsis Genome Encyclopedia II (RARGE II; http://rarge-v2.psc.riken.jp/), in which all of the data described in this study are included. Using the database, we demonstrated consistency (in terms of protein function) with a previous study and identified the presumed function of an unknown gene. We provide examples of AT1G21600, which is a subunit in the plastid-encoded RNA polymerase complex, and AT5G56980, which is related to the jasmonic acid signaling pathway.

The barley EST DNA Replication and Repair Database (bEST-DRRD) as a tool for the identification of the genes involved in DNA replication and repair.

PubMed

Gruszka, Damian; Marzec, Marek; Szarejko, Iwona

2012-06-14

The high level of conservation of genes that regulate DNA replication and repair indicates that they may serve as a source of information on the origin and evolution of the species and makes them a reliable system for the identification of cross-species homologs. Studies that had been conducted to date shed light on the processes of DNA replication and repair in bacteria, yeast and mammals. However, there is still much to be learned about the process of DNA damage repair in plants. These studies, which were conducted mainly using bioinformatics tools, enabled the list of genes that participate in various pathways of DNA repair in Arabidopsis thaliana (L.) Heynh to be outlined; however, information regarding these mechanisms in crop plants is still very limited. A similar, functional approach is particularly difficult for a species whose complete genomic sequences are still unavailable. One of the solutions is to apply ESTs (Expressed Sequence Tags) as the basis for gene identification. For the construction of the barley EST DNA Replication and Repair Database (bEST-DRRD), presented here, the Arabidopsis nucleotide and protein sequences involved in DNA replication and repair were used to browse for and retrieve the deposited sequences, derived from four barley (Hordeum vulgare L.) sequence databases, including the "Barley Genome version 0.05" database (encompassing ca. 90% of barley coding sequences) and from two databases covering the complete genomes of two monocot models: Oryza sativa L. and Brachypodium distachyon L. in order to identify homologous genes. Sequences of the categorised Arabidopsis queries are used for browsing the repositories, which are located on the ViroBLAST platform. The bEST-DRRD is currently used in our project during the identification and validation of the barley genes involved in DNA repair. The presented database provides information about the Arabidopsis genes involved in DNA replication and repair, their expression patterns and models of protein interactions. It was designed and established to provide an open-access tool for the identification of monocot homologs of known Arabidopsis genes that are responsible for DNA-related processes. The barley genes identified in the project are currently being analysed to validate their function.

A novel approach: chemical relational databases, and the role of the ISSCAN database on assessing chemical carcinogenicity.

PubMed

Benigni, Romualdo; Bossa, Cecilia; Richard, Ann M; Yang, Chihae

2008-01-01

Mutagenicity and carcinogenicity databases are crucial resources for toxicologists and regulators involved in chemicals risk assessment. Until recently, existing public toxicity databases have been constructed primarily as "look-up-tables" of existing data, and most often did not contain chemical structures. Concepts and technologies originated from the structure-activity relationships science have provided powerful tools to create new types of databases, where the effective linkage of chemical toxicity with chemical structure can facilitate and greatly enhance data gathering and hypothesis generation, by permitting: a) exploration across both chemical and biological domains; and b) structure-searchability through the data. This paper reviews the main public databases, together with the progress in the field of chemical relational databases, and presents the ISSCAN database on experimental chemical carcinogens.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks.

PubMed

Guo, Liyuan; Wang, Jing

2018-01-04

Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element-target gene pairs (E-G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks

PubMed Central

2018-01-01

Abstract Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element–target gene pairs (E–G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. PMID:29140525

HOWDY: an integrated database system for human genome research

PubMed Central

Hirakawa, Mika

2002-01-01

HOWDY is an integrated database system for accessing and analyzing human genomic information (http://www-alis.tokyo.jst.go.jp/HOWDY/). HOWDY stores information about relationships between genetic objects and the data extracted from a number of databases. HOWDY consists of an Internet accessible user interface that allows thorough searching of the human genomic databases using the gene symbols and their aliases. It also permits flexible editing of the sequence data. The database can be searched using simple words and the search can be restricted to a specific cytogenetic location. Linear maps displaying markers and genes on contig sequences are available, from which an object can be chosen. Any search starting point identifies all the information matching the query. HOWDY provides a convenient search environment of human genomic data for scientists unsure which database is most appropriate for their search. PMID:11752279

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

Phytoremediation of chromium using Salix species: cloning ESTs and candidate genes involved in the Cr response.

PubMed

Quaggiotti, Silvia; Barcaccia, Gianni; Schiavon, Michela; Nicolé, Silvia; Galla, Giulio; Rossignolo, Virginia; Soattin, Marica; Malagoli, Mario

2007-11-01

In this research a differential display based on the detection of cDNA-AFLP markers was used to identify candidate genes potentially involved in the regulation of the response to chromium in four different willow species (Salix alba, Salix eleagnos, Salix fragilis and Salix matsudana) chosen on the basis of their suitability in phytoremediation techniques. Our approach enabled the assay of a large set of mRNA-related fragments and increased the reliability of amplification-based transcriptome analysis. The vast majority of transcript-derived fragments were shared among samples within species and thus attributable to constitutively expressed genes. However, a number of differentially expressed mRNAs were scored in each species and a total of 68 transcripts displaying an altered expression in response to Cr were isolated and sequenced. Public database querying revealed that 44.1% and 4.4% of the cloned ESTs score significant similarity with genes encoding proteins having known or putative function, or with genes coding for unknown proteins, respectively, whereas the remaining 51.5% did not retrieve any homology. Semi-quantitative RT-PCR analysis of seven candidate genes fully confirmed the expression patterns obtained by cDNA-AFLP. Our results indicate the existence of common mechanisms of gene regulation in response to Cr, pathogen attack and senescence-mediated programmed cell death, and suggest a role for the genes isolated in the cross-talk of the signaling pathways governing the adaptation to biotic and abiotic stresses.

Development of a DNA microarray to detect antimicrobial resistance genes identified in the national center for biotechnology information database

USDA-ARS?s Scientific Manuscript database

High density genotyping techniques are needed for investigating antimicrobial resistance especially in the case of multi-drug resistant (MDR) isolates. To achieve this all antimicrobial resistance genes in the NCBI Genbank database were identified by key word searches of sequence annotations and the...

An ensemble rank learning approach for gene prioritization.

PubMed

Lee, Po-Feng; Soo, Von-Wun

2013-01-01

Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.

PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification.

PubMed

Thomas, Paul D; Kejariwal, Anish; Campbell, Michael J; Mi, Huaiyu; Diemer, Karen; Guo, Nan; Ladunga, Istvan; Ulitsky-Lazareva, Betty; Muruganujan, Anushya; Rabkin, Steven; Vandergriff, Jody A; Doremieux, Olivier

2003-01-01

The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. The ontology terms and protein families and subfamilies, as well as Drosophila gene c;assifications, can be browsed and searched for free. Due to outstanding contractual obligations, access to human gene classifications and to protein family trees and multiple sequence alignments will temporarily require a nominal registration fee. PANTHER is publicly available on the web at http://panther.celera.com.

HbVar: A relational database of human hemoglobin variants and thalassemia mutations at the globin gene server.

PubMed

Hardison, Ross C; Chui, David H K; Giardine, Belinda; Riemer, Cathy; Patrinos, George P; Anagnou, Nicholas; Miller, Webb; Wajcman, Henri

2002-03-01

We have constructed a relational database of hemoglobin variants and thalassemia mutations, called HbVar, which can be accessed on the web at http://globin.cse.psu.edu. Extensive information is recorded for each variant and mutation, including a description of the variant and associated pathology, hematology, electrophoretic mobility, methods of isolation, stability information, ethnic occurrence, structure studies, functional studies, and references. The initial information was derived from books by Dr. Titus Huisman and colleagues [Huisman et al., 1996, 1997, 1998]. The current database is updated regularly with the addition of new data and corrections to previous data. Queries can be formulated based on fields in the database. Tables of common categories of variants, such as all those involving the alpha1-globin gene (HBA1) or all those that result in high oxygen affinity, are maintained by automated queries on the database. Users can formulate more precise queries, such as identifying "all beta-globin variants associated with instability and found in Scottish populations." This new database should be useful for clinical diagnosis as well as in fundamental studies of hemoglobin biochemistry, globin gene regulation, and human sequence variation at these loci. Copyright 2002 Wiley-Liss, Inc.

GMDD: a database of GMO detection methods.

PubMed

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans J P; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-06-04

Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.

SpliceDisease database: linking RNA splicing and disease.

PubMed

Wang, Juan; Zhang, Jie; Li, Kaibo; Zhao, Wei; Cui, Qinghua

2012-01-01

RNA splicing is an important aspect of gene regulation in many organisms. Splicing of RNA is regulated by complicated mechanisms involving numerous RNA-binding proteins and the intricate network of interactions among them. Mutations in cis-acting splicing elements or its regulatory proteins have been shown to be involved in human diseases. Defects in pre-mRNA splicing process have emerged as a common disease-causing mechanism. Therefore, a database integrating RNA splicing and disease associations would be helpful for understanding not only the RNA splicing but also its contribution to disease. In SpliceDisease database, we manually curated 2337 splicing mutation disease entries involving 303 genes and 370 diseases, which have been supported experimentally in 898 publications. The SpliceDisease database provides information including the change of the nucleotide in the sequence, the location of the mutation on the gene, the reference Pubmed ID and detailed description for the relationship among gene mutations, splicing defects and diseases. We standardized the names of the diseases and genes and provided links for these genes to NCBI and UCSC genome browser for further annotation and genomic sequences. For the location of the mutation, we give direct links of the entry to the respective position/region in the genome browser. The users can freely browse, search and download the data in SpliceDisease at http://cmbi.bjmu.edu.cn/sdisease.

GETPrime: a gene- or transcript-specific primer database for quantitative real-time PCR.

PubMed

Gubelmann, Carine; Gattiker, Alexandre; Massouras, Andreas; Hens, Korneel; David, Fabrice; Decouttere, Frederik; Rougemont, Jacques; Deplancke, Bart

2011-01-01

The vast majority of genes in humans and other organisms undergo alternative splicing, yet the biological function of splice variants is still very poorly understood in large part because of the lack of simple tools that can map the expression profiles and patterns of these variants with high sensitivity. High-throughput quantitative real-time polymerase chain reaction (qPCR) is an ideal technique to accurately quantify nucleic acid sequences including splice variants. However, currently available primer design programs do not distinguish between splice variants and also differ substantially in overall quality, functionality or throughput mode. Here, we present GETPrime, a primer database supported by a novel platform that uniquely combines and automates several features critical for optimal qPCR primer design. These include the consideration of all gene splice variants to enable either gene-specific (covering the majority of splice variants) or transcript-specific (covering one splice variant) expression profiling, primer specificity validation, automated best primer pair selection according to strict criteria and graphical visualization of the latter primer pairs within their genomic context. GETPrime primers have been extensively validated experimentally, demonstrating high transcript specificity in complex samples. Thus, the free-access, user-friendly GETPrime database allows fast primer retrieval and visualization for genes or groups of genes of most common model organisms, and is available at http://updepla1srv1.epfl.ch/getprime/. Database URL: http://deplanckelab.epfl.ch.

pseudoMap: an innovative and comprehensive resource for identification of siRNA-mediated mechanisms in human transcribed pseudogenes.

PubMed

Chan, Wen-Ling; Yang, Wen-Kuang; Huang, Hsien-Da; Chang, Jan-Gowth

2013-01-01

RNA interference (RNAi) is a gene silencing process within living cells, which is controlled by the RNA-induced silencing complex with a sequence-specific manner. In flies and mice, the pseudogene transcripts can be processed into short interfering RNAs (siRNAs) that regulate protein-coding genes through the RNAi pathway. Following these findings, we construct an innovative and comprehensive database to elucidate siRNA-mediated mechanism in human transcribed pseudogenes (TPGs). To investigate TPG producing siRNAs that regulate protein-coding genes, we mapped the TPGs to small RNAs (sRNAs) that were supported by publicly deep sequencing data from various sRNA libraries and constructed the TPG-derived siRNA-target interactions. In addition, we also presented that TPGs can act as a target for miRNAs that actually regulate the parental gene. To enable the systematic compilation and updating of these results and additional information, we have developed a database, pseudoMap, capturing various types of information, including sequence data, TPG and cognate annotation, deep sequencing data, RNA-folding structure, gene expression profiles, miRNA annotation and target prediction. As our knowledge, pseudoMap is the first database to demonstrate two mechanisms of human TPGs: encoding siRNAs and decoying miRNAs that target the parental gene. pseudoMap is freely accessible at http://pseudomap.mbc.nctu.edu.tw/. Database URL: http://pseudomap.mbc.nctu.edu.tw/

GETPrime: a gene- or transcript-specific primer database for quantitative real-time PCR

PubMed Central

Gubelmann, Carine; Gattiker, Alexandre; Massouras, Andreas; Hens, Korneel; David, Fabrice; Decouttere, Frederik; Rougemont, Jacques; Deplancke, Bart

2011-01-01

The vast majority of genes in humans and other organisms undergo alternative splicing, yet the biological function of splice variants is still very poorly understood in large part because of the lack of simple tools that can map the expression profiles and patterns of these variants with high sensitivity. High-throughput quantitative real-time polymerase chain reaction (qPCR) is an ideal technique to accurately quantify nucleic acid sequences including splice variants. However, currently available primer design programs do not distinguish between splice variants and also differ substantially in overall quality, functionality or throughput mode. Here, we present GETPrime, a primer database supported by a novel platform that uniquely combines and automates several features critical for optimal qPCR primer design. These include the consideration of all gene splice variants to enable either gene-specific (covering the majority of splice variants) or transcript-specific (covering one splice variant) expression profiling, primer specificity validation, automated best primer pair selection according to strict criteria and graphical visualization of the latter primer pairs within their genomic context. GETPrime primers have been extensively validated experimentally, demonstrating high transcript specificity in complex samples. Thus, the free-access, user-friendly GETPrime database allows fast primer retrieval and visualization for genes or groups of genes of most common model organisms, and is available at http://updepla1srv1.epfl.ch/getprime/. Database URL: http://deplanckelab.epfl.ch. PMID:21917859

MIRNA-DISTILLER: A Stand-Alone Application to Compile microRNA Data from Databases.

PubMed

Rieger, Jessica K; Bodan, Denis A; Zanger, Ulrich M

2011-01-01

MicroRNAs (miRNA) are small non-coding RNA molecules of ∼22 nucleotides which regulate large numbers of genes by binding to seed sequences at the 3'-untranslated region of target gene transcripts. The target mRNA is then usually degraded or translation is inhibited, although thus resulting in posttranscriptional down regulation of gene expression at the mRNA and/or protein level. Due to the bioinformatic difficulties in predicting functional miRNA binding sites, several publically available databases have been developed that predict miRNA binding sites based on different algorithms. The parallel use of different databases is currently indispensable, but highly uncomfortable and time consuming, especially when working with numerous genes of interest. We have therefore developed a new stand-alone program, termed MIRNA-DISTILLER, which allows to compile miRNA data for given target genes from public databases. Currently implemented are TargetScan, microCosm, and miRDB, which may be queried independently, pairwise, or together to calculate the respective intersections. Data are stored locally for application of further analysis tools including freely definable biological parameter filters, customized output-lists for both miRNAs and target genes, and various graphical facilities. The software, a data example file and a tutorial are freely available at http://www.ikp-stuttgart.de/content/language1/html/10415.asp.

MIRNA-DISTILLER: A Stand-Alone Application to Compile microRNA Data from Databases

PubMed Central

Rieger, Jessica K.; Bodan, Denis A.; Zanger, Ulrich M.

2011-01-01

MicroRNAs (miRNA) are small non-coding RNA molecules of ∼22 nucleotides which regulate large numbers of genes by binding to seed sequences at the 3′-untranslated region of target gene transcripts. The target mRNA is then usually degraded or translation is inhibited, although thus resulting in posttranscriptional down regulation of gene expression at the mRNA and/or protein level. Due to the bioinformatic difficulties in predicting functional miRNA binding sites, several publically available databases have been developed that predict miRNA binding sites based on different algorithms. The parallel use of different databases is currently indispensable, but highly uncomfortable and time consuming, especially when working with numerous genes of interest. We have therefore developed a new stand-alone program, termed MIRNA-DISTILLER, which allows to compile miRNA data for given target genes from public databases. Currently implemented are TargetScan, microCosm, and miRDB, which may be queried independently, pairwise, or together to calculate the respective intersections. Data are stored locally for application of further analysis tools including freely definable biological parameter filters, customized output-lists for both miRNAs and target genes, and various graphical facilities. The software, a data example file and a tutorial are freely available at http://www.ikp-stuttgart.de/content/language1/html/10415.asp PMID:22303335

MalaCards: an integrated compendium for diseases and their annotation

PubMed Central

Rappaport, Noa; Nativ, Noam; Stelzer, Gil; Twik, Michal; Guan-Golan, Yaron; Iny Stein, Tsippi; Bahir, Iris; Belinky, Frida; Morrey, C. Paul; Safran, Marilyn; Lancet, Doron

2013-01-01

Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/ PMID:23584832

The complex genetics of human insulin-like growth factor 2 are not reflected in public databases.

PubMed

Rotwein, Peter

2018-03-23

Recent advances in genetics present unique opportunities for enhancing knowledge about human physiology and disease susceptibility. Understanding this information at the individual gene level is challenging and requires extracting, collating, and interpreting data from a variety of public gene repositories. Here, I illustrate this challenge by analyzing the gene for human insulin-like growth factor 2 ( IGF2 ) through the lens of several databases. IGF2, a 67-amino acid secreted peptide, is essential for normal prenatal growth and is involved in other physiological and pathophysiological processes in humans. Surprisingly, none of the genetic databases accurately described or completely delineated human IGF2 gene structure or transcript expression, even though all relevant information could be found in the published literature. Although IGF2 shares multiple features with the mouse Igf2 gene, it has several unique properties, including transcription from five promoters. Both genes undergo parental imprinting, with IGF2 / Igf2 being expressed primarily from the paternal chromosome and the adjacent H19 gene from the maternal chromosome. Unlike mouse Igf2 , whose expression declines after birth, human IGF2 remains active throughout life. This characteristic has been attributed to a unique human gene promoter that escapes imprinting, but as shown here, it involves several different promoters with distinct tissue-specific expression patterns. Because new testable hypotheses could lead to critical insights into IGF2 actions in human physiology and disease, it is incumbent that our fundamental understanding is accurate. Similar challenges affecting knowledge of other human genes should promote attempts to critically evaluate, interpret, and correct human genetic data in publicly available databases. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

GExplore: a web server for integrated queries of protein domains, gene expression and mutant phenotypes

PubMed Central

2009-01-01

Background The majority of the genes even in well-studied multi-cellular model organisms have not been functionally characterized yet. Mining the numerous genome wide data sets related to protein function to retrieve potential candidate genes for a particular biological process remains a challenge. Description GExplore has been developed to provide a user-friendly database interface for data mining at the gene expression/protein function level to help in hypothesis development and experiment design. It supports combinatorial searches for proteins with certain domains, tissue- or developmental stage-specific expression patterns, and mutant phenotypes. GExplore operates on a stand-alone database and has fast response times, which is essential for exploratory searches. The interface is not only user-friendly, but also modular so that it accommodates additional data sets in the future. Conclusion GExplore is an online database for quick mining of data related to gene and protein function, providing a multi-gene display of data sets related to the domain composition of proteins as well as expression and phenotype data. GExplore is publicly available at: http://genome.sfu.ca/gexplore/ PMID:19917126

Bayesian approach to transforming public gene expression repositories into disease diagnosis databases.

PubMed

Huang, Haiyan; Liu, Chun-Chi; Zhou, Xianghong Jasmine

2010-04-13

The rapid accumulation of gene expression data has offered unprecedented opportunities to study human diseases. The National Center for Biotechnology Information Gene Expression Omnibus is currently the largest database that systematically documents the genome-wide molecular basis of diseases. However, thus far, this resource has been far from fully utilized. This paper describes the first study to transform public gene expression repositories into an automated disease diagnosis database. Particularly, we have developed a systematic framework, including a two-stage Bayesian learning approach, to achieve the diagnosis of one or multiple diseases for a query expression profile along a hierarchical disease taxonomy. Our approach, including standardizing cross-platform gene expression data and heterogeneous disease annotations, allows analyzing both sources of information in a unified probabilistic system. A high level of overall diagnostic accuracy was shown by cross validation. It was also demonstrated that the power of our method can increase significantly with the continued growth of public gene expression repositories. Finally, we showed how our disease diagnosis system can be used to characterize complex phenotypes and to construct a disease-drug connectivity map.