FOAM (Functional Ontology Assignments for Metagenomes): A Hidden Markov Model (HMM) database with environmental focus

DOE PAGES

Prestat, Emmanuel; David, Maude M.; Hultman, Jenni; ...

2014-09-26

A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associatedmore » functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.« less

Inferring gene ontologies from pairwise similarity data

PubMed Central

Kramer, Michael; Dutkowski, Janusz; Yu, Michael; Bafna, Vineet; Ideker, Trey

2014-01-01

Motivation: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene–gene pairwise similarities from -omics data;infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; andrespect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge—none has been evaluated for GO inference. Methods: We consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method’s ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast. Results: For task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20–25% precision, recall). Conclusion: This study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data. Contact: tideker@ucsd.edu PMID:24932003

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

PubMed

Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

Gene Ontology: Pitfalls, Biases, and Remedies.

PubMed

Gaudet, Pascale; Dessimoz, Christophe

2017-01-01

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.

PubMed

Jani, Saurin D; Argraves, Gary L; Barth, Jeremy L; Argraves, W Scott

2010-04-01

An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

GONUTS: the Gene Ontology Normal Usage Tracking System

PubMed Central

Renfro, Daniel P.; McIntosh, Brenley K.; Venkatraman, Anand; Siegele, Deborah A.; Hu, James C.

2012-01-01

The Gene Ontology Normal Usage Tracking System (GONUTS) is a community-based browser and usage guide for Gene Ontology (GO) terms and a community system for general GO annotation of proteins. GONUTS uses wiki technology to allow registered users to share and edit notes on the use of each term in GO, and to contribute annotations for specific genes of interest. By providing a site for generation of third-party documentation at the granularity of individual terms, GONUTS complements the official documentation of the Gene Ontology Consortium. To provide examples for community users, GONUTS displays the complete GO annotations from seven model organisms: Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus and Arabidopsis thaliana. To support community annotation, GONUTS allows automated creation of gene pages for gene products in UniProt. GONUTS will improve the consistency of annotation efforts across genome projects, and should be useful in training new annotators and consumers in the production of GO annotations and the use of GO terms. GONUTS can be accessed at http://gowiki.tamu.edu. The source code for generating the content of GONUTS is available upon request. PMID:22110029

[Key effect genes responding to nerve injury identified by gene ontology and computer pattern recognition].

PubMed

Pan, Qian; Peng, Jin; Zhou, Xue; Yang, Hao; Zhang, Wei

2012-07-01

In order to screen out important genes from large gene data of gene microarray after nerve injury, we combine gene ontology (GO) method and computer pattern recognition technology to find key genes responding to nerve injury, and then verify one of these screened-out genes. Data mining and gene ontology analysis of gene chip data GSE26350 was carried out through MATLAB software. Cd44 was selected from screened-out key gene molecular spectrum by comparing genes' different GO terms and positions on score map of principal component. Function interferences were employed to influence the normal binding of Cd44 and one of its ligands, chondroitin sulfate C (CSC), to observe neurite extension. Gene ontology analysis showed that the first genes on score map (marked by red *) mainly distributed in molecular transducer activity, receptor activity, protein binding et al molecular function GO terms. Cd44 is one of six effector protein genes, and attracted us with its function diversity. After adding different reagents into the medium to interfere the normal binding of CSC and Cd44, varying-degree remissions of CSC's inhibition on neurite extension were observed. CSC can inhibit neurite extension through binding Cd44 on the neuron membrane. This verifies that important genes in given physiological processes can be identified by gene ontology analysis of gene chip data.

Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

PubMed Central

Dameron, Olivier; Bettembourg, Charles; Le Meur, Nolwenn

2013-01-01

Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure. The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred. The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable. PMID:24146805

The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science

PubMed Central

Klie, Sebastian; Nikoloski, Zoran

2012-01-01

Since the introduction of the Gene Ontology (GO), the analysis of high-throughput data has become tightly coupled with the use of ontologies to establish associations between knowledge and data in an automated fashion. Ontologies provide a systematic description of knowledge by a controlled vocabulary of defined structure in which ontological concepts are connected by pre-defined relationships. In plant science, MapMan and GO offer two alternatives for ontology-driven analyses. Unlike GO, initially developed to characterize microbial systems, MapMan was specifically designed to cover plant-specific pathways and processes. While the dependencies between concepts in MapMan are modeled as a tree, in GO these are captured in a directed acyclic graph. Therefore, the difference in ontologies may cause discrepancies in data reduction, visualization, and hypothesis generation. Here provide the first systematic comparative analysis of GO and MapMan for the case of the model plant species Arabidopsis thaliana (Arabidopsis) with respect to their structural properties and difference in distributions of information content. In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of co-expression networks and the guilt-by-association principle. Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes. The results highlight the need for structured representation of species-specific biological knowledge, and warrants caution in the design principles employed in future ontologies. PMID:22754563

Representing virus-host interactions and other multi-organism processes in the Gene Ontology.

PubMed

Foulger, R E; Osumi-Sutherland, D; McIntosh, B K; Hulo, C; Masson, P; Poux, S; Le Mercier, P; Lomax, J

2015-07-28

The Gene Ontology project is a collaborative effort to provide descriptions of gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but up to now has been largely under-utilized for prokaryotes and viruses, in part because of a lack of appropriate ontology terms. To address this issue, we have developed a set of Gene Ontology classes that are applicable to microbes and their hosts, improving both coverage and quality in this area of the Gene Ontology. Describing microbial and viral gene products brings with it the additional challenge of capturing both the host and the microbe. Recognising this, we have worked closely with annotation groups to test and optimize the GO classes, and we describe here a set of annotation guidelines that allow the controlled description of two interacting organisms. Building on the microbial resources already in existence such as ViralZone, UniProtKB keywords and MeGO, this project provides an integrated ontology to describe interactions between microbial species and their hosts, with mappings to the external resources above. Housing this information within the freely-accessible Gene Ontology project allows the classes and annotation structure to be utilized by a large community of biologists and users.

Functional Abstraction as a Method to Discover Knowledge in Gene Ontologies

PubMed Central

Ultsch, Alfred; Lötsch, Jörn

2014-01-01

Computational analyses of functions of gene sets obtained in microarray analyses or by topical database searches are increasingly important in biology. To understand their functions, the sets are usually mapped to Gene Ontology knowledge bases by means of over-representation analysis (ORA). Its result represents the specific knowledge of the functionality of the gene set. However, the specific ontology typically consists of many terms and relationships, hindering the understanding of the ‘main story’. We developed a methodology to identify a comprehensibly small number of GO terms as “headlines” of the specific ontology allowing to understand all central aspects of the roles of the involved genes. The Functional Abstraction method finds a set of headlines that is specific enough to cover all details of a specific ontology and is abstract enough for human comprehension. This method exceeds the classical approaches at ORA abstraction and by focusing on information rather than decorrelation of GO terms, it directly targets human comprehension. Functional abstraction provides, with a maximum of certainty, information value, coverage and conciseness, a representation of the biological functions in a gene set plays a role. This is the necessary means to interpret complex Gene Ontology results thus strengthening the role of functional genomics in biomarker and drug discovery. PMID:24587272

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

PubMed

Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

Protein classification using probabilistic chain graphs and the Gene Ontology structure.

PubMed

Carroll, Steven; Pavlovic, Vladimir

2006-08-01

Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own 'annotation space'. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms. The proposed method was evaluated on a set of 4879 proteins from the Saccharomyces Genome Database whose interactions were also recorded in the GRID project. Results indicate that direct utilization of the Gene Ontology improves predictive ability, outperforming traditional models that do not take advantage of dependencies among functional terms. Average increase in accuracy (precision) of positive and negative term predictions of 27.8% (2.0%) over three different similarity measures and three subontologies was observed. C/C++/Perl implementation is available from authors upon request.

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature

PubMed Central

Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V.; Lawson, Andrew; Zeng, Jia; Johnson, Amber M.; Holla, Vijaykumar; Bailey, Ann M.; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W.

2015-01-01

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org PMID:25858285

Modeling biochemical pathways in the gene ontology

DOE PAGES

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

2016-09-01

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

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature.

PubMed

Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V; Lawson, Andrew; Zeng, Jia; Johnson, Amber M; Holla, Vijaykumar; Bailey, Ann M; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W

2015-01-01

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org © The Author(s) 2015. Published by Oxford University Press.

GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways.

PubMed

Gamalielsson, Jonas; Olsson, Bjorn

2008-01-01

We present a new method for semantic comparison of biological pathways, aiming to discover evolutionary conservation of pathways between species. Our method uses all three sub-ontologies of Gene Ontology (GO) and a measure of semantic similarity to calculate match scores between gene products. These scores are used for finding local pairwise pathway alignments. This approach has the advantage of being applicable to all types of pathways where nodes are gene products, e.g., regulatory pathways, signalling pathways and metabolic enzyme-to-enzyme pathways. We demonstrate the usefulness of the method using regulatory and metabolic pathways from E. coli and S. cerevisiae as examples.

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology

PubMed Central

Roncaglia, Paola; Howe, Douglas G.; Laulederkind, Stanley J.F.; Khodiyar, Varsha K.; Berardini, Tanya Z.; Tweedie, Susan; Foulger, Rebecca E.; Osumi-Sutherland, David; Campbell, Nancy H.; Huntley, Rachael P.; Talmud, Philippa J.; Blake, Judith A.; Breckenridge, Ross; Riley, Paul R.; Lambiase, Pier D.; Elliott, Perry M.; Clapp, Lucie; Tinker, Andrew; Hill, David P.

2018-01-01

Background: A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products. Methods and Results: In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci. Conclusions: We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects. PMID:29440116

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology.

PubMed

Lovering, Ruth C; Roncaglia, Paola; Howe, Douglas G; Laulederkind, Stanley J F; Khodiyar, Varsha K; Berardini, Tanya Z; Tweedie, Susan; Foulger, Rebecca E; Osumi-Sutherland, David; Campbell, Nancy H; Huntley, Rachael P; Talmud, Philippa J; Blake, Judith A; Breckenridge, Ross; Riley, Paul R; Lambiase, Pier D; Elliott, Perry M; Clapp, Lucie; Tinker, Andrew; Hill, David P

2018-02-01

A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products. In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci. We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects. © 2018 The Authors.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

PubMed

Manda, Prashanti; McCarthy, Fiona; Bridges, Susan M

2013-10-01

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

From genomics to chemical genomics: new developments in KEGG

PubMed Central

Kanehisa, Minoru; Goto, Susumu; Hattori, Masahiro; Aoki-Kinoshita, Kiyoko F.; Itoh, Masumi; Kawashima, Shuichi; Katayama, Toshiaki; Araki, Michihiro; Hirakawa, Mika

2006-01-01

The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource () provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps. PMID:16381885

Muscle Research and Gene Ontology: New standards for improved data integration

PubMed Central

Feltrin, Erika; Campanaro, Stefano; Diehl, Alexander D; Ehler, Elisabeth; Faulkner, Georgine; Fordham, Jennifer; Gardin, Chiara; Harris, Midori; Hill, David; Knoell, Ralph; Laveder, Paolo; Mittempergher, Lorenza; Nori, Alessandra; Reggiani, Carlo; Sorrentino, Vincenzo; Volpe, Pompeo; Zara, Ivano; Valle, Giorgio; Deegan née Clark, Jennifer

2009-01-01

Background The Gene Ontology Project provides structured controlled vocabularies for molecular biology that can be used for the functional annotation of genes and gene products. In a collaboration between the Gene Ontology (GO) Consortium and the muscle biology community, we have made large-scale additions to the GO biological process and cellular component ontologies. The main focus of this ontology development work concerns skeletal muscle, with specific consideration given to the processes of muscle contraction, plasticity, development, and regeneration, and to the sarcomere and membrane-delimited compartments. Our aims were to update the existing structure to reflect current knowledge, and to resolve, in an accommodating manner, the ambiguity in the language used by the community. Results The updated muscle terminologies have been incorporated into the GO. There are now 159 new terms covering critical research areas, and 57 existing terms have been improved and reorganized to follow their usage in muscle literature. Conclusion The revised GO structure should improve the interpretation of data from high-throughput (e.g. microarray and proteomic) experiments in the area of muscle science and muscle disease. We actively encourage community feedback on, and gene product annotation with these new terms. Please visit the Muscle Community Annotation Wiki . PMID:19178689

Muscle Research and Gene Ontology: New standards for improved data integration.

PubMed

Feltrin, Erika; Campanaro, Stefano; Diehl, Alexander D; Ehler, Elisabeth; Faulkner, Georgine; Fordham, Jennifer; Gardin, Chiara; Harris, Midori; Hill, David; Knoell, Ralph; Laveder, Paolo; Mittempergher, Lorenza; Nori, Alessandra; Reggiani, Carlo; Sorrentino, Vincenzo; Volpe, Pompeo; Zara, Ivano; Valle, Giorgio; Deegan, Jennifer

2009-01-29

The Gene Ontology Project provides structured controlled vocabularies for molecular biology that can be used for the functional annotation of genes and gene products. In a collaboration between the Gene Ontology (GO) Consortium and the muscle biology community, we have made large-scale additions to the GO biological process and cellular component ontologies. The main focus of this ontology development work concerns skeletal muscle, with specific consideration given to the processes of muscle contraction, plasticity, development, and regeneration, and to the sarcomere and membrane-delimited compartments. Our aims were to update the existing structure to reflect current knowledge, and to resolve, in an accommodating manner, the ambiguity in the language used by the community. The updated muscle terminologies have been incorporated into the GO. There are now 159 new terms covering critical research areas, and 57 existing terms have been improved and reorganized to follow their usage in muscle literature. The revised GO structure should improve the interpretation of data from high-throughput (e.g. microarray and proteomic) experiments in the area of muscle science and muscle disease. We actively encourage community feedback on, and gene product annotation with these new terms. Please visit the Muscle Community Annotation Wiki http://wiki.geneontology.org/index.php/Muscle_Biology.

The Gene Ontology of eukaryotic cilia and flagella.

PubMed

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

2017-01-01

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

Expansion of the Gene Ontology knowledgebase and resources

PubMed Central

2017-01-01

The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/. PMID:27899567

Guidelines for the functional annotation of microRNAs using the Gene Ontology

PubMed Central

D'Eustachio, Peter; Smith, Jennifer R.; Zampetaki, Anna

2016-01-01

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual). PMID:26917558

Ontology based molecular signatures for immune cell types via gene expression analysis

PubMed Central

2013-01-01

Background New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights. PMID:24004649

KEGG Bioinformatics Resource for Plant Genomics and Metabolomics.

PubMed

Kanehisa, Minoru

2016-01-01

In the era of high-throughput biology it is necessary to develop not only elaborate computational methods but also well-curated databases that can be used as reference for data interpretation. KEGG ( http://www.kegg.jp/ ) is such a reference knowledge base with two specific aims. One is to compile knowledge on high-level functions of the cell and the organism in terms of the molecular interaction and reaction networks, which is implemented in KEGG pathway maps, BRITE functional hierarchies, and KEGG modules. The other is to expand knowledge on genes and proteins involved in the molecular networks from experimentally observed organisms to other organisms using the concept of orthologs, which is implemented in the KEGG Orthology (KO) system. Thus, KEGG is a generic resource applicable to all organisms and enables interpretation of high-level functions from genomic and molecular data. Here we first present a brief overview of the entire KEGG resource, and then give an introduction of how to use KEGG in plant genomics and metabolomics research.

The effects of shared information on semantic calculations in the gene ontology.

PubMed

Bible, Paul W; Sun, Hong-Wei; Morasso, Maria I; Loganantharaj, Rasiah; Wei, Lai

2017-01-01

The structured vocabulary that describes gene function, the gene ontology (GO), serves as a powerful tool in biological research. One application of GO in computational biology calculates semantic similarity between two concepts to make inferences about the functional similarity of genes. A class of term similarity algorithms explicitly calculates the shared information (SI) between concepts then substitutes this calculation into traditional term similarity measures such as Resnik, Lin, and Jiang-Conrath. Alternative SI approaches, when combined with ontology choice and term similarity type, lead to many gene-to-gene similarity measures. No thorough investigation has been made into the behavior, complexity, and performance of semantic methods derived from distinct SI approaches. We apply bootstrapping to compare the generalized performance of 57 gene-to-gene semantic measures across six benchmarks. Considering the number of measures, we additionally evaluate whether these methods can be leveraged through ensemble machine learning to improve prediction performance. Results showed that the choice of ontology type most strongly influenced performance across all evaluations. Combining measures into an ensemble classifier reduces cross-validation error beyond any individual measure for protein interaction prediction. This improvement resulted from information gained through the combination of ontology types as ensemble methods within each GO type offered no improvement. These results demonstrate that multiple SI measures can be leveraged for machine learning tasks such as automated gene function prediction by incorporating methods from across the ontologies. To facilitate future research in this area, we developed the GO Graph Tool Kit (GGTK), an open source C++ library with Python interface (github.com/paulbible/ggtk).

Analyzing gene expression data in mice with the Neuro Behavior Ontology.

PubMed

Hoehndorf, Robert; Hancock, John M; Hardy, Nigel W; Mallon, Ann-Marie; Schofield, Paul N; Gkoutos, Georgios V

2014-02-01

We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We developed the OntoFUNC tool and have applied it to enrichment analyses over the NBO to provide high-level behavioral interpretations of gene expression datasets. The resulting increase in the number of gene annotations facilitates the identification of behavioral or neurologic processes by assisting the formulation of hypotheses about the relationships between gene, processes, and phenotypic manifestations resulting from behavioral observations.

Quality of Computationally Inferred Gene Ontology Annotations

PubMed Central

Škunca, Nives; Altenhoff, Adrian; Dessimoz, Christophe

2012-01-01

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation. PMID:22693439

Representing Ontogeny Through Ontology: A Developmental Biologist’s Guide to The Gene Ontology

PubMed Central

Hill, David P.; Berardini, Tanya Z.; Howe, Douglas G.; Van Auken, Kimberly M.

2010-01-01

Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible. PMID:19921742

Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology

PubMed Central

2013-01-01

Background The Gene Ontology (GO) facilitates the description of the action of gene products in a biological context. Many GO terms refer to chemical entities that participate in biological processes. To facilitate accurate and consistent systems-wide biological representation, it is necessary to integrate the chemical view of these entities with the biological view of GO functions and processes. We describe a collaborative effort between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure that the representation of chemicals in the GO is both internally consistent and in alignment with the chemical expertise captured in ChEBI. Results We have examined and integrated the ChEBI structural hierarchy into the GO resource through computationally-assisted manual curation of both GO and ChEBI. Our work has resulted in the creation of computable definitions of GO terms that contain fully defined semantic relationships to corresponding chemical terms in ChEBI. Conclusions The set of logical definitions using both the GO and ChEBI has already been used to automate aspects of GO development and has the potential to allow the integration of data across the domains of biology and chemistry. These logical definitions are available as an extended version of the ontology from http://purl.obolibrary.org/obo/go/extensions/go-plus.owl. PMID:23895341

A methodology to migrate the gene ontology to a description logic environment using DAML+OIL.

PubMed

Wroe, C J; Stevens, R; Goble, C A; Ashburner, M

2003-01-01

The Gene Ontology Next Generation Project (GONG) is developing a staged methodology to evolve the current representation of the Gene Ontology into DAML+OIL in order to take advantage of the richer formal expressiveness and the reasoning capabilities of the underlying description logic. Each stage provides a step level increase in formal explicit semantic content with a view to supporting validation, extension and multiple classification of the Gene Ontology. The paper introduces DAML+OIL and demonstrates the activity within each stage of the methodology and the functionality gained.

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

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

PubMed

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

2016-03-11

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

Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway

PubMed Central

2011-01-01

Background Sporadic amyotrophic lateral sclerosis (sALS) is a motor neuron disease with poorly understood etiology. Results of gene expression profiling studies of whole blood from ALS patients have not been validated and are difficult to relate to ALS pathogenesis because gene expression profiles depend on the relative abundance of the different cell types present in whole blood. We conducted microarray analyses using Agilent Human Whole Genome 4 × 44k Arrays on a more homogeneous cell population, namely purified peripheral blood lymphocytes (PBLs), from ALS patients and healthy controls to identify molecular signatures possibly relevant to ALS pathogenesis. Methods Differentially expressed genes were determined by LIMMA (Linear Models for MicroArray) and SAM (Significance Analysis of Microarrays) analyses. The SAFE (Significance Analysis of Function and Expression) procedure was used to identify molecular pathway perturbations. Proteasome inhibition assays were conducted on cultured peripheral blood mononuclear cells (PBMCs) from ALS patients to confirm alteration of the Ubiquitin/Proteasome System (UPS). Results For the first time, using SAFE in a global gene ontology analysis (gene set size 5-100), we show significant perturbation of the KEGG (Kyoto Encyclopedia of Genes and Genomes) ALS pathway of motor neuron degeneration in PBLs from ALS patients. This was the only KEGG disease pathway significantly upregulated among 25, and contributing genes, including SOD1, represented 54% of the encoded proteins or protein complexes of the KEGG ALS pathway. Further SAFE analysis, including gene set sizes >100, showed that only neurodegenerative diseases (4 out of 34 disease pathways) including ALS were significantly upregulated. Changes in UBR2 expression correlated inversely with time since onset of disease and directly with ALSFRS-R, implying that UBR2 was increased early in the course of ALS. Cultured PBMCs from ALS patients accumulated more ubiquitinated proteins

GO(vis), a gene ontology visualization tool based on multi-dimensional values.

PubMed

Ning, Zi; Jiang, Zhenran

2010-05-01

Most of gene product similarity measurements concentrate on the information content of Gene Ontology (GO) terms or use a path-based similarity between GO terms, which may ignore other important information contained in the structure of the ontology. In our study, we integrate different GO similarity measure approaches to analyze the functional relationship of genes and gene products with a new triangle-based visualization tool called GO(Vis). The purpose of this tool is to demonstrate the effect of three important information factors when measuring the similarity between gene products. One advantage of this tool is that its important ratio can be adjusted to meet different measuring requirements according to the biological knowledge of each factor. The experimental results demonstrate that GO(Vis) can display diagrams of the functional relationship for gene products effectively.

Genetic resources for advanced biofuel production described with the Gene Ontology.

PubMed

Torto-Alalibo, Trudy; Purwantini, Endang; Lomax, Jane; Setubal, João C; Mukhopadhyay, Biswarup; Tyler, Brett M

2014-01-01

Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary. The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of gene products in a consistent manner across all kingdoms of life. The Microbial ENergy processes Gene Ontology () project is extending the GO to include new terms to describe microbial processes of interest to bioenergy production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of advanced biofuels. These include alcohols such as butanol, isopropanol, isobutanol, and fuels derived from fatty acids, isoprenoids, and polyhydroxyalkanoates. These fuels are superior to first generation biofuels (ethanol and biodiesel esterified from vegetable oil or animal fat), can be generated from non-food feedstock sources, can be used as supplements or substitutes for gasoline, diesel and jet fuels, and can be stored and distributed using existing infrastructure. Here we review the roles of genes associated with synthesis of advanced biofuels, and at the same time introduce the use of the GO to describe the functions of these genes in a standardized way.

Genetic resources for advanced biofuel production described with the Gene Ontology

PubMed Central

Torto-Alalibo, Trudy; Purwantini, Endang; Lomax, Jane; Setubal, João C.; Mukhopadhyay, Biswarup; Tyler, Brett M.

2014-01-01

Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary. The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of gene products in a consistent manner across all kingdoms of life. The Microbial ENergy processes Gene Ontology () project is extending the GO to include new terms to describe microbial processes of interest to bioenergy production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of advanced biofuels. These include alcohols such as butanol, isopropanol, isobutanol, and fuels derived from fatty acids, isoprenoids, and polyhydroxyalkanoates. These fuels are superior to first generation biofuels (ethanol and biodiesel esterified from vegetable oil or animal fat), can be generated from non-food feedstock sources, can be used as supplements or substitutes for gasoline, diesel and jet fuels, and can be stored and distributed using existing infrastructure. Here we review the roles of genes associated with synthesis of advanced biofuels, and at the same time introduce the use of the GO to describe the functions of these genes in a standardized way. PMID:25346727

Genetic resources for advanced biofuel production described with the Gene Ontology

DOE PAGES

Torto-Alalibo, Trudy; Purwantini, Endang; Lomax, Jane; ...

2014-10-10

Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary.The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of gene products in a consistent manner across all kingdoms of life. The Microbial ENergy processes Gene Ontology (http://www.mengo.biochem.vt.edu) project is extending the GO to include new terms to describe microbial processes of interest to bioenergymore » production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of advanced biofuels. These include alcohols such as butanol, isopropanol, isobutanol, and fuels derived from fatty acids, isoprenoids, and polyhydroxyalkanoates. These fuels are superior to first generation biofuels (ethanol and biodiesel esterified from vegetable oil or animal fat), can be generated from non-food feedstock sources, can be used as supplements or substitutes for gasoline, diesel and jet fuels, and can be stored and distributed using existing infrastructure. We review the roles of genes associated with synthesis of advanced biofuels, and at the same time introduce the use of the GO to describe the functions of these genes in a standardized way.« less

Genetic resources for advanced biofuel production described with the Gene Ontology

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

Torto-Alalibo, Trudy; Purwantini, Endang; Lomax, Jane

Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary.The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of gene products in a consistent manner across all kingdoms of life. The Microbial ENergy processes Gene Ontology (http://www.mengo.biochem.vt.edu) project is extending the GO to include new terms to describe microbial processes of interest to bioenergymore » production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of advanced biofuels. These include alcohols such as butanol, isopropanol, isobutanol, and fuels derived from fatty acids, isoprenoids, and polyhydroxyalkanoates. These fuels are superior to first generation biofuels (ethanol and biodiesel esterified from vegetable oil or animal fat), can be generated from non-food feedstock sources, can be used as supplements or substitutes for gasoline, diesel and jet fuels, and can be stored and distributed using existing infrastructure. We review the roles of genes associated with synthesis of advanced biofuels, and at the same time introduce the use of the GO to describe the functions of these genes in a standardized way.« less

The Functional Genetics of Handedness and Language Lateralization: Insights from Gene Ontology, Pathway and Disease Association Analyses.

PubMed

Schmitz, Judith; Lor, Stephanie; Klose, Rena; Güntürkün, Onur; Ocklenburg, Sebastian

2017-01-01

Handedness and language lateralization are partially determined by genetic influences. It has been estimated that at least 40 (and potentially more) possibly interacting genes may influence the ontogenesis of hemispheric asymmetries. Recently, it has been suggested that analyzing the genetics of hemispheric asymmetries on the level of gene ontology sets, rather than at the level of individual genes, might be more informative for understanding the underlying functional cascades. Here, we performed gene ontology, pathway and disease association analyses on genes that have previously been associated with handedness and language lateralization. Significant gene ontology sets for handedness were anatomical structure development, pattern specification (especially asymmetry formation) and biological regulation. Pathway analysis highlighted the importance of the TGF-beta signaling pathway for handedness ontogenesis. Significant gene ontology sets for language lateralization were responses to different stimuli, nervous system development, transport, signaling, and biological regulation. Despite the fact that some authors assume that handedness and language lateralization share a common ontogenetic basis, gene ontology sets barely overlap between phenotypes. Compared to genes involved in handedness, which mostly contribute to structural development, genes involved in language lateralization rather contribute to activity-dependent cognitive processes. Disease association analysis revealed associations of genes involved in handedness with diseases affecting the whole body, while genes involved in language lateralization were specifically engaged in mental and neurological diseases. These findings further support the idea that handedness and language lateralization are ontogenetically independent, complex phenotypes.

The Functional Genetics of Handedness and Language Lateralization: Insights from Gene Ontology, Pathway and Disease Association Analyses

PubMed Central

Schmitz, Judith; Lor, Stephanie; Klose, Rena; Güntürkün, Onur; Ocklenburg, Sebastian

2017-01-01

Handedness and language lateralization are partially determined by genetic influences. It has been estimated that at least 40 (and potentially more) possibly interacting genes may influence the ontogenesis of hemispheric asymmetries. Recently, it has been suggested that analyzing the genetics of hemispheric asymmetries on the level of gene ontology sets, rather than at the level of individual genes, might be more informative for understanding the underlying functional cascades. Here, we performed gene ontology, pathway and disease association analyses on genes that have previously been associated with handedness and language lateralization. Significant gene ontology sets for handedness were anatomical structure development, pattern specification (especially asymmetry formation) and biological regulation. Pathway analysis highlighted the importance of the TGF-beta signaling pathway for handedness ontogenesis. Significant gene ontology sets for language lateralization were responses to different stimuli, nervous system development, transport, signaling, and biological regulation. Despite the fact that some authors assume that handedness and language lateralization share a common ontogenetic basis, gene ontology sets barely overlap between phenotypes. Compared to genes involved in handedness, which mostly contribute to structural development, genes involved in language lateralization rather contribute to activity-dependent cognitive processes. Disease association analysis revealed associations of genes involved in handedness with diseases affecting the whole body, while genes involved in language lateralization were specifically engaged in mental and neurological diseases. These findings further support the idea that handedness and language lateralization are ontogenetically independent, complex phenotypes. PMID:28729848

BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks.

PubMed

Maere, Steven; Heymans, Karel; Kuiper, Martin

2005-08-15

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.

The Gene Ontology (GO) project: structured vocabularies for molecular biology and their application to genome and expression analysis.

PubMed

Blake, Judith A; Harris, Midori A

2008-09-01

Scientists wishing to utilize genomic data have quickly come to realize the benefit of standardizing descriptions of experimental procedures and results for computer-driven information retrieval systems. The focus of the Gene Ontology project is three-fold. First, the project goal is to compile the Gene Ontologies: structured vocabularies describing domains of molecular biology. Second, the project supports the use of these structured vocabularies in the annotation of gene products. Third, the gene product-to-GO annotation sets are provided by participating groups to the public through open access to the GO database and Web resource. This unit describes the current ontologies and what is beyond the scope of the Gene Ontology project. It addresses the issue of how GO vocabularies are constructed and related to genes and gene products. It concludes with a discussion of how researchers can access, browse, and utilize the GO project in the course of their own research. Copyright 2008 by John Wiley & Sons, Inc.

Gene Ontology Terms and Automated Annotation for Energy-Related Microbial Genomes

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

Mukhopadhyay, Biswarup; Tyler, Brett M.; Setubal, Joao

Gene Ontology (GO) is one of the more widely used functional ontologies for describing gene functions at various levels. The project developed 660 GO terms for describing energy-related microbial processes and filled the known gaps in this area of the GO system, and then used these terms to describe functions of 179 genes to showcase the utilities of the new resources. It hosted a series of workshops and made presentations at key meetings to inform and train scientific community members on these terms and to receive inputs from them for the GO term generation efforts. The project has developed amore » website for storing and displaying the resources (http://www.mengo.biochem.vt.edu/). The outcome of the project was further disseminated through peer-reviewed publications and poster and seminar presentations.« less

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.

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

PubMed Central

Zhang, Bing; Schmoyer, Denise; Kirov, Stefan; Snoddy, Jay

2004-01-01

Background Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. Results We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . Conclusion GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets. PMID:14975175

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.

Exploring autophagy with Gene Ontology

PubMed Central

2018-01-01

ABSTRACT Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of ‘annotations’ that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study. PMID:29455577

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

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.

Extending gene ontology with gene association networks.

PubMed

Peng, Jiajie; Wang, Tao; Wang, Jixuan; Wang, Yadong; Chen, Jin

2016-04-15

Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO. We proposed a new algorithm 'GOExtender' to efficiently identify all the connected gene pairs labeled by the same parent GO terms. GOExtender is used to predict new GO terms with biological network data, and connect them to the existing GO. Evaluation tests on biological process and cellular component categories of different GO releases showed that GOExtender can extend new GO terms automatically based on the biological network. Furthermore, we applied GOExtender to the recent release of GO and discovered new GO terms with strong support from literature. Software and supplementary document are available at www.msu.edu/%7Ejinchen/GOExtender jinchen@msu.edu or ydwang@hit.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

PubMed

Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Quality assurance of the gene ontology using abstraction networks.

PubMed

Ochs, Christopher; Perl, Yehoshua; Halper, Michael; Geller, James; Lomax, Jane

2016-06-01

The gene ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks (AbNs) are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology. They have been shown to successfully support QA work in the context of various ontologies. Two kinds of AbNs, called the area taxonomy and the partial-area taxonomy, are developed for GO hierarchies and derived specifically for the biological process (BP) hierarchy. Within this framework, several QA heuristics, based on the identification of groups of anomalous terms which exhibit certain taxonomy-defined characteristics, are introduced. Such groups are expected to have higher error rates when compared to other terms. Thus, by focusing QA efforts on anomalous terms one would expect to find relatively more erroneous content. By automatically identifying these potential problem areas within an ontology, time and effort will be saved during manual reviews of GO's content. BP is used as a testbed, with samples of three kinds of anomalous BP terms chosen for a taxonomy-based QA review. Additional heuristics for QA are demonstrated. From the results of this QA effort, it is observed that different kinds of inconsistencies in the modeling of GO can be exposed with the use of the proposed heuristics. For comparison, the results of QA work on a sample of terms chosen from GO's general population are presented.

De Novo Transcriptome Assembly and Characterization of Lithospermum officinale to Discover Putative Genes Involved in Specialized Metabolites Biosynthesis.

PubMed

Rai, Amit; Nakaya, Taiki; Shimizu, Yohei; Rai, Megha; Nakamura, Michimi; Suzuki, Hideyuki; Saito, Kazuki; Yamazaki, Mami

2018-05-29

Lithospermum officinale is a valuable source of bioactive metabolites with medicinal and industrial values. However, little is known about genes involved in the biosynthesis of these metabolites, primarily due to the lack of genome or transcriptome resources. This study presents the first effort to establish and characterize de novo transcriptome assembly resource for L. officinale and expression analysis for three of its tissues, namely leaf, stem, and root. Using over 4Gbps of RNA-sequencing datasets, we obtained de novo transcriptome assembly of L. officinale , consisting of 77,047 unigenes with assembly N50 value as 1524 bps. Based on transcriptome annotation and functional classification, 52,766 unigenes were assigned with putative genes functions, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. KEGG pathway and gene ontology enrichment analysis using highly expressed unigenes across three tissues and targeted metabolome analysis showed active secondary metabolic processes enriched specifically in the root of L. officinale . Using co-expression analysis, we also identified 20 and 48 unigenes representing different enzymes of lithospermic/chlorogenic acid and shikonin biosynthesis pathways, respectively. We further identified 15 candidate unigenes annotated as cytochrome P450 with the highest expression in the root of L. officinale as novel genes with a role in key biochemical reactions toward shikonin biosynthesis. Thus, through this study, we not only generated a high-quality genomic resource for L. officinale but also propose candidate genes to be involved in shikonin biosynthesis pathways for further functional characterization. Georg Thieme Verlag KG Stuttgart · New York.

Is the crowd better as an assistant or a replacement in ontology engineering? An exploration through the lens of the Gene Ontology.

PubMed

Mortensen, Jonathan M; Telis, Natalie; Hughey, Jacob J; Fan-Minogue, Hua; Van Auken, Kimberly; Dumontier, Michel; Musen, Mark A

2016-04-01

Biomedical ontologies contain errors. Crowdsourcing, defined as taking a job traditionally performed by a designated agent and outsourcing it to an undefined large group of people, provides scalable access to humans. Therefore, the crowd has the potential to overcome the limited accuracy and scalability found in current ontology quality assurance approaches. Crowd-based methods have identified errors in SNOMED CT, a large, clinical ontology, with an accuracy similar to that of experts, suggesting that crowdsourcing is indeed a feasible approach for identifying ontology errors. This work uses that same crowd-based methodology, as well as a panel of experts, to verify a subset of the Gene Ontology (200 relationships). Experts identified 16 errors, generally in relationships referencing acids and metals. The crowd performed poorly in identifying those errors, with an area under the receiver operating characteristic curve ranging from 0.44 to 0.73, depending on the methods configuration. However, when the crowd verified what experts considered to be easy relationships with useful definitions, they performed reasonably well. Notably, there are significantly fewer Google search results for Gene Ontology concepts than SNOMED CT concepts. This disparity may account for the difference in performance - fewer search results indicate a more difficult task for the worker. The number of Internet search results could serve as a method to assess which tasks are appropriate for the crowd. These results suggest that the crowd fits better as an expert assistant, helping experts with their verification by completing the easy tasks and allowing experts to focus on the difficult tasks, rather than an expert replacement. Copyright © 2016 Elsevier Inc. All rights reserved.

Fuzzy measures on the Gene Ontology for gene product similarity.

PubMed

Popescu, Mihail; Keller, James M; Mitchell, Joyce A

2006-01-01

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.

Integration of the Gene Ontology into an object-oriented architecture.

PubMed

Shegogue, Daniel; Zheng, W Jim

2005-05-10

To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes.

Integration of the Gene Ontology into an object-oriented architecture

PubMed Central

Shegogue, Daniel; Zheng, W Jim

2005-01-01

Background To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model. Results Using object-oriented models we have captured the static and dynamic events that occur during a representative GO process, "transforming growth factor-beta (TGF-beta) receptor complex assembly" (GO:0007181). Conclusion We demonstrate that the utility of GO terms can be enhanced by object-oriented technology, and that the GO terms can be integrated into an object-oriented model by serving as a basis for the generation of object functions and attributes. PMID:15885145

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

PubMed Central

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

2013-01-01

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

Approaching the axiomatic enrichment of the Gene Ontology from a lexical perspective.

PubMed

Quesada-Martínez, Manuel; Mikroyannidi, Eleni; Fernández-Breis, Jesualdo Tomás; Stevens, Robert

2015-09-01

The main goal of this work is to measure how lexical regularities in biomedical ontology labels can be used for the automatic creation of formal relationships between classes, and to evaluate the results of applying our approach to the Gene Ontology (GO). In recent years, we have developed a method for the lexical analysis of regularities in biomedical ontology labels, and we showed that the labels can present a high degree of regularity. In this work, we extend our method with a cross-products extension (CPE) metric, which estimates the potential interest of a specific regularity for axiomatic enrichment in the lexical analysis, using information on exact matches in external ontologies. The GO consortium recently enriched the GO by using so-called cross-product extensions. Cross-products are generated by establishing axioms that relate a given GO class with classes from the GO or other biomedical ontologies. We apply our method to the GO and study how its lexical analysis can identify and reconstruct the cross-products that are defined by the GO consortium. The label of the classes of the GO are highly regular in lexical terms, and the exact matches with labels of external ontologies affect 80% of the GO classes. The CPE metric reveals that 31.48% of the classes that exhibit regularities have fragments that are classes into two external ontologies that are selected for our experiment, namely, the Cell Ontology and the Chemical Entities of Biological Interest ontology, and 18.90% of them are fully decomposable into smaller parts. Our results show that the CPE metric permits our method to detect GO cross-product extensions with a mean recall of 62% and a mean precision of 28%. The study is completed with an analysis of false positives to explain this precision value. We think that our results support the claim that our lexical approach can contribute to the axiomatic enrichment of biomedical ontologies and that it can provide new insights into the engineering of

De novo transcriptome sequencing in Frankliniella occidentalis to identify genes involved in plant virus transmission and insecticide resistance.

PubMed

Zhang, Zhijun; Zhang, Pengjun; Li, Weidi; Zhang, Jinming; Huang, Fang; Yang, Jian; Bei, Yawei; Lu, Yaobin

2013-05-01

The western flower thrips (WFT), Frankliniella occidentalis, a world-wide invasive insect, causes agricultural damage by directly feeding and by indirectly vectoring Tospoviruses, such as Tomato spotted wilt virus (TSWV). We characterized the transcriptome of WFT and analyzed global gene expression of WFT response to TSWV infection using Illumina sequencing platform. We compiled 59,932 unigenes, and identified 36,339 unigenes by similarity analysis against public databases, most of which were annotated using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Within these annotated transcripts, we collected 278 sequences related to insecticide resistance. GO and KEGG analysis of different expression genes between TSWV-infected and non-infected WFT population revealed that TSWV can regulate cellular process and immune response, which might lead to low virus titers in thrips cells and no detrimental effects on F. occidentalis. This data-set not only enriches genomic resource for WFT, but also benefits research into its molecular genetics and functional genomics. Copyright © 2013 Elsevier Inc. All rights reserved.

RuleGO: a logical rules-based tool for description of gene groups by means of Gene Ontology

PubMed Central

Gruca, Aleksandra; Sikora, Marek; Polanski, Andrzej

2011-01-01

Genome-wide expression profiles obtained with the use of DNA microarray technology provide abundance of experimental data on biological and molecular processes. Such amount of data need to be further analyzed and interpreted in order to obtain biological conclusions on the basis of experimental results. The analysis requires a lot of experience and is usually time-consuming process. Thus, frequently various annotation databases are used to improve the whole process of analysis. Here, we present RuleGO—the web-based application that allows the user to describe gene groups on the basis of logical rules that include Gene Ontology (GO) terms in their premises. Presented application allows obtaining rules that reflect coappearance of GO-terms describing genes supported by the rules. The ontology level and number of coappearing GO-terms is adjusted in automatic manner. The user limits the space of possible solutions only. The RuleGO application is freely available at http://rulego.polsl.pl/. PMID:21715384

GO-based functional dissimilarity of gene sets.

PubMed

Díaz-Díaz, Norberto; Aguilar-Ruiz, Jesús S

2011-09-01

The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together. To implement this approach to functional assessment, we present GFD (GO-based Functional Dissimilarity), a novel dissimilarity measure for evaluating groups of genes based on the most relevant functions of the whole set. The measure assigns a numerical value to the gene set for each of the three GO sub-ontologies. Results show that GFD performs robustly when applied to gene set of known functionality (extracted from KEGG). It performs particularly well on randomly generated gene sets. An ROC analysis reveals that the performance of GFD in evaluating the functional dissimilarity of gene sets is very satisfactory. A comparative analysis against other functional measures, such as GS2 and those presented by Resnik and Wang, also demonstrates the robustness of GFD.

Text Mining to Support Gene Ontology Curation and Vice Versa.

PubMed

Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Serial analysis of gene expression in a rat lung model of asthma.

PubMed

Yin, Lei-Miao; Jiang, Gong-Hao; Wang, Yu; Wang, Yan; Liu, Yan-Yan; Jin, Wei-Rong; Zhang, Zen; Xu, Yu-Dong; Yang, Yong-Qing

2008-11-01

The pathogenesis and molecular mechanism underlying asthma remain undetermined. The purpose of this study was to identify genes and pathways involved in the early airway response (EAR) phase of asthma by using serial analysis of gene expression (SAGE). Two SAGE tag libraries of lung tissues derived from a rat model of asthma and controls were generated. Bioinformatic analyses were carried out using the Database for Annotation, Visualization and IntegratedDiscovery Functional Annotation Tool, Gene Ontology (GO) TreeMachine and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A total of 26 552 SAGE tags of asthmatic rat lung were obtained, of which 12 221 were unique tags. Of the unique tags, 55.5% were matched with known genes. By comparison of the two libraries, 186 differentially expressed tags (P < 0.05) were identified, of which 103 were upregulated and 83 were downregulated. Using the bioinformatic tools these genes were classified into 23 functional groups, 15 KEGG pathways and 37 enriched GO categories. The bioinformatic analyses of gene distribution, enriched categories and the involvement of specific pathways in the SAGE libraries have provided information on regulatory networks of the EAR phase of asthma. Analyses of the regulated genes of interest may inform new hypotheses, increase our understanding of the disease and provide a foundation for future research.

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application

PubMed Central

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-01-01

Background microRNAs (miRNAs) are single-stranded RNA molecules of about 20–23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. Results GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. Conclusion GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA. PMID:19534746

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application.

PubMed

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-06-16

microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA.

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

The Representation of Heart Development in the Gene Ontology

PubMed Central

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

2012-01-01

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

PPDB - A tool for investigation of plants physiology based on gene ontology.

PubMed

Sharma, Ajay Shiv; Gupta, Hari Om; Prasad, Rajendra

2014-09-02

Representing the way forward, from functional genomics and its ontology to functional understanding and physiological model, in a computationally tractable fashion is one of the ongoing challenges faced by computational biology. To tackle the standpoint, we herein feature the applications of contemporary database management to the development of PPDB, a searching and browsing tool for the Plants Physiology Database that is based upon the mining of a large amount of gene ontology data currently available. The working principles and search options associated with the PPDB are publicly available and freely accessible on-line ( http://www.iitr.ernet.in/ajayshiv/ ) through a user friendly environment generated by means of Drupal-6.24. By knowing that genes are expressed in temporally and spatially characteristic patterns and that their functionally distinct products often reside in specific cellular compartments and may be part of one or more multi-component complexes, this sort of work is intended to be relevant for investigating the functional relationships of gene products at a system level and, thus, helps us approach to the full physiology.

PPDB: A Tool for Investigation of Plants Physiology Based on Gene Ontology.

PubMed

Sharma, Ajay Shiv; Gupta, Hari Om; Prasad, Rajendra

2015-09-01

Representing the way forward, from functional genomics and its ontology to functional understanding and physiological model, in a computationally tractable fashion is one of the ongoing challenges faced by computational biology. To tackle the standpoint, we herein feature the applications of contemporary database management to the development of PPDB, a searching and browsing tool for the Plants Physiology Database that is based upon the mining of a large amount of gene ontology data currently available. The working principles and search options associated with the PPDB are publicly available and freely accessible online ( http://www.iitr.ac.in/ajayshiv/ ) through a user-friendly environment generated by means of Drupal-6.24. By knowing that genes are expressed in temporally and spatially characteristic patterns and that their functionally distinct products often reside in specific cellular compartments and may be part of one or more multicomponent complexes, this sort of work is intended to be relevant for investigating the functional relationships of gene products at a system level and, thus, helps us approach to the full physiology.

Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining

PubMed Central

2012-01-01

Background Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Results Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction

GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis.

PubMed

Zheng, Qi; Wang, Xiu-Jie

2008-07-01

Gene Ontology (GO) analysis has become a commonly used approach for functional studies of large-scale genomic or transcriptomic data. Although there have been a lot of software with GO-related analysis functions, new tools are still needed to meet the requirements for data generated by newly developed technologies or for advanced analysis purpose. Here, we present a Gene Ontology Enrichment Analysis Software Toolkit (GOEAST), an easy-to-use web-based toolkit that identifies statistically overrepresented GO terms within given gene sets. Compared with available GO analysis tools, GOEAST has the following improved features: (i) GOEAST displays enriched GO terms in graphical format according to their relationships in the hierarchical tree of each GO category (biological process, molecular function and cellular component), therefore, provides better understanding of the correlations among enriched GO terms; (ii) GOEAST supports analysis for data from various sources (probe or probe set IDs of Affymetrix, Illumina, Agilent or customized microarrays, as well as different gene identifiers) and multiple species (about 60 prokaryote and eukaryote species); (iii) One unique feature of GOEAST is to allow cross comparison of the GO enrichment status of multiple experiments to identify functional correlations among them. GOEAST also provides rigorous statistical tests to enhance the reliability of analysis results. GOEAST is freely accessible at http://omicslab.genetics.ac.cn/GOEAST/

SUPERFAMILY 1.75 including a domain-centric gene ontology method.

PubMed

de Lima Morais, David A; Fang, Hai; Rackham, Owen J L; Wilson, Derek; Pethica, Ralph; Chothia, Cyrus; Gough, Julian

2011-01-01

The SUPERFAMILY resource provides protein domain assignments at the structural classification of protein (SCOP) superfamily level for over 1400 completely sequenced genomes, over 120 metagenomes and other gene collections such as UniProt. All models and assignments are available to browse and download at http://supfam.org. A new hidden Markov model library based on SCOP 1.75 has been created and a previously ignored class of SCOP, coiled coils, is now included. Our scoring component now uses HMMER3, which is in orders of magnitude faster and produces superior results. A cloud-based pipeline was implemented and is publicly available at Amazon web services elastic computer cloud. The SUPERFAMILY reference tree of life has been improved allowing the user to highlight a chosen superfamily, family or domain architecture on the tree of life. The most significant advance in SUPERFAMILY is that now it contains a domain-based gene ontology (GO) at the superfamily and family levels. A new methodology was developed to ensure a high quality GO annotation. The new methodology is general purpose and has been used to produce domain-based phenotypic ontologies in addition to GO.

BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases.

PubMed

Jamialahmadi, Oveis; Motamedian, Ehsan; Hashemi-Najafabadi, Sameereh

2016-10-18

Development of an interface tool between the Biochemical, Genetic and Genomic (BiGG) and KEGG databases is necessary for simultaneous access to the features of both databases. For this purpose, we present the BiKEGG toolbox, an open source COBRA toolbox extension providing a set of functions to infer the reaction correspondences between the KEGG reaction identifiers and those in the BiGG knowledgebase using a combination of manual verification and computational methods. Inferred reaction correspondences using this approach are supported by evidence from the literature, which provides a higher number of reconciled reactions between these two databases compared to the MetaNetX and MetRxn databases. This set of equivalent reactions is then used to automatically superimpose the predicted fluxes using COBRA methods on classical KEGG pathway maps or to create a customized metabolic map based on the KEGG global metabolic pathway, and to find the corresponding reactions in BiGG based on the genome annotation of an organism in the KEGG database. Customized metabolic maps can be created for a set of pathways of interest, for the whole KEGG global map or exclusively for all pathways for which there exists at least one flux carrying reaction. This flexibility in visualization enables BiKEGG to indicate reaction directionality as well as to visualize the reaction fluxes for different static or dynamic conditions in an animated manner. BiKEGG allows the user to export (1) the output visualized metabolic maps to various standard image formats or save them as a video or animated GIF file, and (2) the equivalent reactions for an organism as an Excel spreadsheet.

Weighted gene co‑expression network analysis in identification of key genes and networks for ischemic‑reperfusion remodeling myocardium.

PubMed

Guo, Nan; Zhang, Nan; Yan, Liqiu; Lian, Zheng; Wang, Jiawang; Lv, Fengfeng; Wang, Yunfei; Cao, Xufen

2018-06-14

Acute myocardial infarction induces ventricular remodeling, which is implicated in dilated heart and heart failure. The pathogenical mechanism of myocardium remodeling remains to be elucidated. The aim of the present study was to identify key genes and networks for myocardium remodeling following ischemia‑reperfusion (IR). First, the mRNA expression data from the National Center for Biotechnology Information database were downloaded to identify differences in mRNA expression of the IR heart at days 2 and 7. Then, weighted gene co‑expression network analysis, hierarchical clustering, protein‑protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were used to identify key genes and networks for the heart remodeling process following IR. A total of 3,321 differentially expressed genes were identified during the heart remodeling process. A total of 6 modules were identified through gene co‑expression network analysis. GO and KEGG analysis results suggested that each module represented a different biological function and was associated with different pathways. Finally, hub genes of each module were identified by PPI network construction. The present study revealed that heart remodeling following IR is a complicated process, involving extracellular matrix organization, neural development, apoptosis and energy metabolism. The dysregulated genes, including SRC proto‑oncogene, non‑receptor tyrosine kinase, discs large MAGUK scaffold protein 1, ATP citrate lyase, RAN, member RAS oncogene family, tumor protein p53, and polo like kinase 2, may be essential for heart remodeling following IR and may be used as potential targets for the inhibition of heart remodeling following acute myocardial infarction.

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.

PubMed

Peng, Jiajie; Zhang, Xuanshuo; Hui, Weiwei; Lu, Junya; Li, Qianqian; Liu, Shuhui; Shang, Xuequn

2018-03-19

Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

De novo transcriptome assembly and analysis of differential gene expression following peptidoglycan (PGN) challenge in Antheraea pernyi.

PubMed

Liu, Yu; Xin, Zhao-Zhe; Zhang, Dai-Zhen; Zhu, Xiao-Yu; Wang, Ying; Chen, Li; Tang, Bo-Ping; Zhou, Chun-Lin; Chai, Xin-Yue; Tian, Ji-Wu; Liu, Qiu-Ning

2018-06-01

Antheraea pernyi is not only an important economic insect, it is increasingly employed as a model organism due to a variety of advantages, including ease of rearing and experimental manipulation compared with other Lepidoptera. Peptidoglycan (PGN) is a major component of the bacterial cell wall, and interactions between PGN and A. pernyi cause a series of physiological changes in the insect. In the present study, we constructed cDNA libraries from a A. pernyi PGN-infected group and a control group stimulated with phosphate-buffered saline (PBS). The transcriptome was de novo assembled using the Trinity platform, and 1698 differentially expressed genes (DEGs) were identified, comprising 894 up-regulated and 804 down-regulated genes. To further investigate immune-related DEGs, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed. GO analysis identified major immune-related GO terms and KEGG enrichment indicated gene responses to three pathways related to the insect immune system. Several homologous genes related to the immune response of the A. pernyi fat body post-PGN infection were identified and categorised. Taken together, the results provide insight into the complex molecular mechanisms of the responses to bacterial infection at the transcriptional level. Copyright © 2018 Elsevier B.V. All rights reserved.

The Proteasix Ontology.

PubMed

Arguello Casteleiro, Mercedes; Klein, Julie; Stevens, Robert

2016-06-04

The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides) The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis. The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results. The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone.

Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis.

PubMed

Pashaiasl, Maryam; Ebrahimi, Mansour; Ebrahimie, Esmaeil

2016-09-01

Diminished ovarian reserve (DOR) is one of the reasons for infertility that not only affects both older and young women. Ovarian reserve assessment can be used as a new prognostic tool for infertility treatment decision making. Here, up- and down-regulated gene expression profiles of granulosa cells were analysed to generate a putative interaction map of the involved genes. In addition, gene ontology (GO) analysis was used to get insight intol the biological processes and molecular functions of involved proteins in DOR. Eleven up-regulated genes and nine down-regulated genes were identified and assessed by constructing interaction networks based on their biological processes. PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3 were the key nodes in the up-regulated networks, while the IGF2, AMH, GREM, and FOXC1 proteins were key in the down-regulated networks. MIRN101-1, MIRN153-1 and MIRN194-1 inhibited the expression of SOCS2, while CSH1 and BMP2 positively regulated IGF1 and IGF2. Ossification, ovarian follicle development, vasculogenesis, sequence-specific DNA binding transcription factor activity, and golgi apparatus are the major differential groups between up-regulated and down-regulated genes in DOR. Meta-analysis of publicly available transcriptomic data highlighted the high coexpression of CTGF, connective tissue growth factor, with the other key regulators of DOR. CTGF is involved in organ senescence and focal adhesion pathway according to GO analysis. These findings provide a comprehensive system biology based insight into the aetiology of DOR through network and gene ontology analyses.

An integrative approach for measuring semantic similarities using gene ontology.

PubMed

Peng, Jiajie; Li, Hongxiang; Jiang, Qinghua; Wang, Yadong; Chen, Jin

2014-01-01

Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding. We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories. InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.

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

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Effects of subchronic benzo(a)pyrene exposure on neurotransmitter receptor gene expression in the rat hippocampus related with spatial learning and memory change.

PubMed

Qiu, Chongying; Cheng, Shuqun; Xia, Yinyin; Peng, Bin; Tang, Qian; Tu, Baijie

2011-11-18

Exposure of laboratory rats to Benzo(a)pyrene (BaP), an environmental contaminant with its high lipophilicify which is widely dispersed in the environment and can easily cross the blood brain barrier presenting in the central nervous system, is associated with impaired learning and memory. The purpose of the research was to examine whether subchronic exposure to BaP affects spatial learning and memory, and how it alters normal gene expression in hippocampus, as well as selection of candidate genes involving neurotransmitter receptor attributed to learning and memory. Morris water maze (MWM) was used to evaluate behavioral differences between BaP-treated and vehicle-treated groups. To gain a better insight into the mechanism of BaP-induced neurotoxicity on learning and memory, we used whole genome oligo microarrays as well as Polymerase Chain Reaction (PCR) to assess the global impact of gene expression. Male Sprague-Dawley rats were intraperitoneally injected with 6.25mg/kg of BaP or vehicle for 14 weeks. The results from the Morris water maze (MWM) test showed that rats treated with BaP exhibited significantly higher mean latencies as compared to vehicle controls. BaP exposure significantly decreased the number of crossing the platform and the time spent in the target area. After the hippocampus was collected from each rat, total RNA was isolated. Microarray and PCR revealed that exposure to BaP affected mRNA expression of neurotransmitter receptors. The web tool DAVID was used to analyze the significantly enriched gene ontology (GO) and KEGG pathways in the differentially expressed genes. Analysis showed that the most significantly affected gene ontology category was behavior. Furthermore, the fourth highest significantly affected gene ontology category was learning and memory. KEGG molecular pathway analysis showed that "neuroactive ligand-receptor interaction" was affected by BaP with highest statistical significance, and 9 candidate neurotransmitter receptor

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...

How Does the Scientific Community Contribute to Gene Ontology?

PubMed

Lovering, Ruth C

2017-01-01

Collaborations between the scientific community and members of the Gene Ontology (GO) Consortium have led to an increase in the number and specificity of GO terms, as well as increasing the number of GO annotations. A variety of approaches have been taken to encourage research scientists to contribute to the GO, but the success of these approaches has been variable. This chapter reviews both the successes and failures of engaging the scientific community in GO development and annotation, as well as, providing motivation and advice to encourage individual researchers to contribute to GO.

Investigation of candidate genes for osteoarthritis based on gene expression profiles.

PubMed

Dong, Shuanghai; Xia, Tian; Wang, Lei; Zhao, Qinghua; Tian, Jiwei

2016-12-01

To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein-protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine-cytokine receptor

Analytical workflow profiling gene expression in murine macrophages

PubMed Central

Nixon, Scott E.; González-Peña, Dianelys; Lawson, Marcus A.; McCusker, Robert H.; Hernandez, Alvaro G.; O’Connor, Jason C.; Dantzer, Robert; Kelley, Keith W.

2015-01-01

Comprehensive and simultaneous analysis of all genes in a biological sample is a capability of RNA-Seq technology. Analysis of the entire transcriptome benefits from summarization of genes at the functional level. As a cellular response of interest not previously explored with RNA-Seq, peritoneal macrophages from mice under two conditions (control and immunologically challenged) were analyzed for gene expression differences. Quantification of individual transcripts modeled RNA-Seq read distribution and uncertainty (using a Beta Negative Binomial distribution), then tested for differential transcript expression (False Discovery Rate-adjusted p-value < 0.05). Enrichment of functional categories utilized the list of differentially expressed genes. A total of 2079 differentially expressed transcripts representing 1884 genes were detected. Enrichment of 92 categories from Gene Ontology Biological Processes and Molecular Functions, and KEGG pathways were grouped into 6 clusters. Clusters included defense and inflammatory response (Enrichment Score = 11.24) and ribosomal activity (Enrichment Score = 17.89). Our work provides a context to the fine detail of individual gene expression differences in murine peritoneal macrophages during immunological challenge with high throughput RNA-Seq. PMID:25708305

Identification of pathogenic genes related to rheumatoid arthritis through integrated analysis of DNA methylation and gene expression profiling.

PubMed

Zhang, Lei; Ma, Shiyun; Wang, Huailiang; Su, Hang; Su, Ke; Li, Longjie

2017-11-15

The purpose of our study was to identify new pathogenic genes used for exploring the pathogenesis of rheumatoid arthritis (RA). To screen pathogenic genes of RA, an integrated analysis was performed by using the microarray datasets in RA derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further discovered by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Afterwards, the integrated analysis of DNA methylation and gene expression profiling was used to screen crucial genes. In addition, we used RT-PCR and MSP to verify the expression levels and methylation status of these crucial genes in 20 synovial biopsy samples obtained from 10 RA model mice and 10 normal mice. BCL11B, CCDC88C, FCRLA and APOL6 were both up-regulated and hypomethylated in RA according to integrated analysis, RT-PCR and MSP verification. Four crucial genes (BCL11B, CCDC88C, FCRLA and APOL6) identified and analyzed in this study might be closely connected with the pathogenesis of RA. Copyright © 2017. Published by Elsevier B.V.

The Orthology Ontology: development and applications.

PubMed

Fernández-Breis, Jesualdo Tomás; Chiba, Hirokazu; Legaz-García, María Del Carmen; Uchiyama, Ikuo

2016-06-04

Computational comparative analysis of multiple genomes provides valuable opportunities to biomedical research. In particular, orthology analysis can play a central role in comparative genomics; it guides establishing evolutionary relations among genes of organisms and allows functional inference of gene products. However, the wide variations in current orthology databases necessitate the research toward the shareability of the content that is generated by different tools and stored in different structures. Exchanging the content with other research communities requires making the meaning of the content explicit. The need for a common ontology has led to the creation of the Orthology Ontology (ORTH) following the best practices in ontology construction. Here, we describe our model and major entities of the ontology that is implemented in the Web Ontology Language (OWL), followed by the assessment of the quality of the ontology and the application of the ORTH to existing orthology datasets. This shareable ontology enables the possibility to develop Linked Orthology Datasets and a meta-predictor of orthology through standardization for the representation of orthology databases. The ORTH is freely available in OWL format to all users at http://purl.org/net/orth . The Orthology Ontology can serve as a framework for the semantic standardization of orthology content and it will contribute to a better exploitation of orthology resources in biomedical research. The results demonstrate the feasibility of developing shareable datasets using this ontology. Further applications will maximize the usefulness of this ontology.

Markov Chain Ontology Analysis (MCOA)

PubMed Central

2012-01-01

Background Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. Results In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. Conclusion A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches

Markov Chain Ontology Analysis (MCOA).

PubMed

Frost, H Robert; McCray, Alexa T

2012-02-03

Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.

Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.

PubMed

Funk, Christopher S; Cohen, K Bretonnel; Hunter, Lawrence E; Verspoor, Karin M

2016-09-09

Gene Ontology (GO) terms represent the standard for annotation and representation of molecular functions, biological processes and cellular compartments, but a large gap exists between the way concepts are represented in the ontology and how they are expressed in natural language text. The construction of highly specific GO terms is formulaic, consisting of parts and pieces from more simple terms. We present two different types of manually generated rules to help capture the variation of how GO terms can appear in natural language text. The first set of rules takes into account the compositional nature of GO and recursively decomposes the terms into their smallest constituent parts. The second set of rules generates derivational variations of these smaller terms and compositionally combines all generated variants to form the original term. By applying both types of rules, new synonyms are generated for two-thirds of all GO terms and an increase in F-measure performance for recognition of GO on the CRAFT corpus from 0.498 to 0.636 is observed. Additionally, we evaluated the combination of both types of rules over one million full text documents from Elsevier; manual validation and error analysis show we are able to recognize GO concepts with reasonable accuracy (88 %) based on random sampling of annotations. In this work we present a set of simple synonym generation rules that utilize the highly compositional and formulaic nature of the Gene Ontology concepts. We illustrate how the generated synonyms aid in improving recognition of GO concepts on two different biomedical corpora. We discuss other applications of our rules for GO ontology quality assurance, explore the issue of overgeneration, and provide examples of how similar methodologies could be applied to other biomedical terminologies. Additionally, we provide all generated synonyms for use by the text-mining community.

Key genes and pathways in measles and their interaction with environmental chemicals

PubMed Central

Zhang, Rongqiang; Jiang, Hualin; Li, Fengying; Su, Ning; Ding, Yi; Mao, Xiang; Ren, Dan; Wang, Jing

2018-01-01

The aim of the present study was to explore key genes that may have a role in the pathology of measles virus infection and to clarify the interaction networks between environmental factors and differentially expressed genes (DEGs). After screening the database of the Gene Expression Omnibus of the National Center for Biotechnology Information, the dataset GSE5808 was downloaded and analyzed. A global normalization method was performed to minimize data inconsistencies and heterogeneity. DEGs during different stages of measles virus infection were explored using R software (v3.4.0). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs were performed using Cytoscape 3.4.0 software. A protein-protein interaction (PPI) network of the DEGs was obtained from the STRING database v9.05. A total of 43 DEGs were obtained from four analyzed sample groups, including 10 highly expressed genes and 33 genes with decreased expression. The most enriched pathways based on KEGG analysis were fatty acid elongation, cytokine-cytokine receptor interaction and RNA degradation. The genes mentioned in the PPI network were mainly associated with protein binding and chemokine activity. A total of 219 chemicals were identified that may, jointly or on their own, interact with the 6 DEGs between the control group and patients with measles (at hospital entry), including benzo(a)pyrene (BaP) and tetrachlorodibenzodioxin (TCDD). In conclusion, the present study revealed that chemokines and environmental chemicals, e.g. BaP and TCDD, may affect the development of measles. PMID:29805511

Key genes and pathways in measles and their interaction with environmental chemicals.

PubMed

Zhang, Rongqiang; Jiang, Hualin; Li, Fengying; Su, Ning; Ding, Yi; Mao, Xiang; Ren, Dan; Wang, Jing

2018-06-01

The aim of the present study was to explore key genes that may have a role in the pathology of measles virus infection and to clarify the interaction networks between environmental factors and differentially expressed genes (DEGs). After screening the database of the Gene Expression Omnibus of the National Center for Biotechnology Information, the dataset GSE5808 was downloaded and analyzed. A global normalization method was performed to minimize data inconsistencies and heterogeneity. DEGs during different stages of measles virus infection were explored using R software (v3.4.0). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs were performed using Cytoscape 3.4.0 software. A protein-protein interaction (PPI) network of the DEGs was obtained from the STRING database v9.05. A total of 43 DEGs were obtained from four analyzed sample groups, including 10 highly expressed genes and 33 genes with decreased expression. The most enriched pathways based on KEGG analysis were fatty acid elongation, cytokine-cytokine receptor interaction and RNA degradation. The genes mentioned in the PPI network were mainly associated with protein binding and chemokine activity. A total of 219 chemicals were identified that may, jointly or on their own, interact with the 6 DEGs between the control group and patients with measles (at hospital entry), including benzo(a)pyrene (BaP) and tetrachlorodibenzodioxin (TCDD). In conclusion, the present study revealed that chemokines and environmental chemicals, e.g. BaP and TCDD, may affect the development of measles.

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

DOE PAGES

Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; ...

2015-02-14

Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstratemore » that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited.« less

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

PubMed Central

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-01

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

PubMed

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

Protein annotation from protein interaction networks and Gene Ontology.

PubMed

Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

2011-10-01

We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

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

Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

PubMed

Zhang, Shu-Bo; Tang, Qiang-Rong

2016-07-21

Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability.

PubMed

Diehl, Alexander D; Meehan, Terrence F; Bradford, Yvonne M; Brush, Matthew H; Dahdul, Wasila M; Dougall, David S; He, Yongqun; Osumi-Sutherland, David; Ruttenberg, Alan; Sarntivijai, Sirarat; Van Slyke, Ceri E; Vasilevsky, Nicole A; Haendel, Melissa A; Blake, Judith A; Mungall, Christopher J

2016-07-04

The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the

Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer.

PubMed

Liu, Xiaozhen; Jin, Gan; Qian, Jiacheng; Yang, Hongjian; Tang, Hongchao; Meng, Xuli; Li, Yongfeng

2018-04-23

This study aimed to screen sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer. In this study, Illumina digital gene expression sequencing technology was applied and differentially expressed genes (DEGs) between patients presenting pathological complete response (pCR) and non-pathological complete response (NpCR) were identified. Further, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed. The genes in significant enriched pathways were finally quantified by quantitative real-time PCR (qRT-PCR) to confirm that they were differentially expressed. Additionally, GSE23988 from Gene Expression Omnibus database was used as the validation dataset to confirm the DEGs. After removing the low-quality reads, 715 DEGs were finally detected. After mapping to KEGG pathways, 10 DEGs belonging to the ubiquitin proteasome pathway (HECTD3, PSMB10, UBD, UBE2C, and UBE2S) and cytokine-cytokine receptor interactions (CCL2, CCR1, CXCL10, CXCL11, and IL2RG) were selected for further analysis. These 10 genes were finally quantified by qRT-PCR to confirm that they were differentially expressed (the log 2 fold changes of selected genes were - 5.34, 7.81, 6.88, 5.74, 3.11, 19.58, 8.73, 8.88, 7.42, and 34.61 for HECTD3, PSMB10, UBD, UBE2C, UBE2S, CCL2, CCR1, CXCL10, CXCL11, and IL2RG, respectively). Moreover, 53 common genes were confirmed by the validation dataset, including downregulated UBE2C and UBE2S. Our results suggested that these 10 genes belonging to these two pathways might be useful as sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer.

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

PubMed

Wang, Anping; Zhang, Guibin

2017-11-01

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in

The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics

PubMed Central

Cooper, Laurel; Meier, Austin; Laporte, Marie-Angélique; Elser, Justin L; Mungall, Chris; Sinn, Brandon T; Cavaliere, Dario; Carbon, Seth; Dunn, Nathan A; Smith, Barry; Qu, Botong; Preece, Justin; Zhang, Eugene; Todorovic, Sinisa; Gkoutos, Georgios; Doonan, John H; Stevenson, Dennis W; Arnaud, Elizabeth

2018-01-01

Abstract The Planteome project (http://www.planteome.org) provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.com/Planteome) for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser (http://browser.planteome.org/amigo) and our data repository. PMID:29186578

Identification of transcriptional factors and key genes in primary osteoporosis by DNA microarray.

PubMed

Xie, Wengui; Ji, Lixin; Zhao, Teng; Gao, Pengfei

2015-05-09

A number of genes have been identified to be related with primary osteoporosis while less is known about the comprehensive interactions between regulating genes and proteins. We aimed to identify the differentially expressed genes (DEGs) and regulatory effects of transcription factors (TFs) involved in primary osteoporosis. The gene expression profile GSE35958 was obtained from Gene Expression Omnibus database, including 5 primary osteoporosis and 4 normal bone tissues. The differentially expressed genes between primary osteoporosis and normal bone tissues were identified by the same package in R language. The TFs of these DEGs were predicted with the Essaghir A method. DAVID (The Database for Annotation, Visualization and Integrated Discovery) was applied to perform the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of DEGs. After analyzing regulatory effects, a regulatory network was built between TFs and the related DEGs. A total of 579 DEGs was screened, including 310 up-regulated genes and 269 down-regulated genes in primary osteoporosis samples. In GO terms, more up-regulated genes were enriched in transcription regulator activity, and secondly in transcription factor activity. A total 10 significant pathways were enriched in KEGG analysis, including colorectal cancer, Wnt signaling pathway, Focal adhesion, and MAPK signaling pathway. Moreover, total 7 TFs were enriched, of which CTNNB1, SP1, and TP53 regulated most up-regulated DEGs. The discovery of the enriched TFs might contribute to the understanding of the mechanism of primary osteoporosis. Further research on genes and TFs related to the WNT signaling pathway and MAPK pathway is urgent for clinical diagnosis and directing treatment of primary osteoporosis.

Identification of pathogenic genes and upstream regulators in age-related macular degeneration.

PubMed

Zhao, Bin; Wang, Mengya; Xu, Jing; Li, Min; Yu, Yuhui

2017-06-26

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. Our study aims to identify the key genes and upstream regulators in AMD. To screen pathogenic genes of AMD, an integrated analysis was performed by using the microarray datasets in AMD derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further discovered by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We constructed the AMD-specific transcriptional regulatory network to find the crucial transcriptional factors (TFs) which target the DEGs in AMD. Quantitative real time polymerase chain reaction (qRT-PCR) was performed to verify the DEGs and TFs obtained by integrated analysis. From two GEO datasets obtained, we identified 1280 DEGs (730 up-regulated and 550 down-regulated genes) between AMD and normal control (NC). After KEGG analysis, steroid biosynthesis is a significantly enriched pathway for DEGs. The expression of 8 genes (TNC, GRP, TRAF6, ADAMTS5, GPX3, FAP, DHCR7 and FDFT1) was detected. Except for TNC and GPX3, the other 6 genes in qRT-PCR played the same pattern with that in our integrated analysis. The dysregulation of these eight genes may involve with the process of AMD. Two crucial transcription factors (c-rel and myogenin) were concluded to play a role in AMD. Especially, myogenin was associated with AMD by regulating TNC, GRP and FAP. Our finding can contribute to developing new potential biomarkers, revealing the underlying pathogenesis, and further raising new therapeutic targets for AMD.

Exploring the key genes and pathways in enchondromas using a gene expression microarray.

PubMed

Shi, Zhongju; Zhou, Hengxing; Pan, Bin; Lu, Lu; Kang, Yi; Liu, Lu; Wei, Zhijian; Feng, Shiqing

2017-07-04

Enchondromas are the most common primary benign osseous neoplasms that occur in the medullary bone; they can undergo malignant transformation into chondrosarcoma. However, enchondromas are always undetected in patients, and the molecular mechanism is unclear. To identify key genes and pathways associated with the occurrence and development of enchondromas, we downloaded the gene expression dataset GSE22855 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in enchondromas. In total, 635 genes were identified as DEGs. Of these, 225 genes (35.43%) were up-regulated, and the remaining 410 genes (64.57%) were down-regulated. We identified the predominant gene ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly over-represented in the enchondromas samples compared with the control samples. Subsequently the top 10 core genes were identified from the protein-protein interaction (PPI) network. The enrichment analyses of the genes mainly involved in two significant modules showed that the DEGs were principally related to ribosomes, protein digestion and absorption, ECM-receptor interaction, focal adhesion, amoebiasis and the PI3K-Akt signaling pathway.Together, these data elucidate the molecular mechanisms underlying the occurrence and development of enchondromas and provide promising candidates for therapeutic intervention and prognostic evaluation. However, further experimental studies are needed to confirm these results.

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability

DOE PAGES

Diehl, Alexander D.; Meehan, Terrence F.; Bradford, Yvonne M.; ...

2016-07-04

Background: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. Construction and content: Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologiesmore » in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. Utility and discussion: The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. Conclusions: The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the

The language of gene ontology: a Zipf's law analysis.

PubMed

Kalankesh, Leila Ranandeh; Stevens, Robert; Brass, Andy

2012-06-07

Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipf's law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language. Annotations from the Gene Ontology Annotation project were found to follow Zipf's law. Surprisingly, the measured power law exponents were consistently different between annotation captured using the three GO sub-ontologies in the corpora (function, process and component). On filtering the corpora using GO evidence codes we found that the value of the measured power law exponent responded in a predictable way as a function of the evidence codes used to support the annotation. Techniques from computational linguistics can provide new insights into the annotation process. GO annotations show similar statistical behaviours to those seen in natural language with measured exponents that provide a signal which correlates with the nature of the evidence codes used to support the annotations, suggesting that the measured exponent might provide a signal regarding the information content of the annotation.

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

PubMed

Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

2015-07-01

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

Ontology design patterns to disambiguate relations between genes and gene products in GENIA

PubMed Central

2011-01-01

Motivation Annotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challenging due to the inherent ambiguity of natural language. The provision of formal definitions and axioms for semantic annotations offers the means for ensuring consistency as well as enables the development of verifiable annotation guidelines. Consistent semantic annotations facilitate the automatic discovery of new information through deductive inferences. Results We provide a formal characterization of the relations used in the recent GENIA corpus annotations. For this purpose, we both select existing axiom systems based on the desired properties of the relations within the domain and develop new axioms for several relations. To apply this ontology of relations to the semantic annotation of text corpora, we implement two ontology design patterns. In addition, we provide a software application to convert annotated GENIA abstracts into OWL ontologies by combining both the ontology of relations and the design patterns. As a result, the GENIA abstracts become available as OWL ontologies and are amenable for automated verification, deductive inferences and other knowledge-based applications. Availability Documentation, implementation and examples are available from http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/. PMID:22166341

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

PubMed Central

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

2008-01-01

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

A multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors for functional gene analysis.

PubMed

Weber, Kristoffer; Bartsch, Udo; Stocking, Carol; Fehse, Boris

2008-04-01

Functional gene analysis requires the possibility of overexpression, as well as downregulation of one, or ideally several, potentially interacting genes. Lentiviral vectors are well suited for this purpose as they ensure stable expression of complementary DNAs (cDNAs), as well as short-hairpin RNAs (shRNAs), and can efficiently transduce a wide spectrum of cell targets when packaged within the coat proteins of other viruses. Here we introduce a multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors designed according to the "building blocks" principle. Using a wide spectrum of different fluorescent markers, including drug-selectable enhanced green fluorescent protein (eGFP)- and dTomato-blasticidin-S resistance fusion proteins, LeGO vectors allow simultaneous analysis of multiple genes and shRNAs of interest within single, easily identifiable cells. Furthermore, each functional module is flanked by unique cloning sites, ensuring flexibility and individual optimization. The efficacy of these vectors for analyzing multiple genes in a single cell was demonstrated in several different cell types, including hematopoietic, endothelial, and neural stem and progenitor cells, as well as hepatocytes. LeGO vectors thus represent a valuable tool for investigating gene networks using conditional ectopic expression and knock-down approaches simultaneously.

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

PubMed

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

2018-01-01

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

Mapping between the OBO and OWL ontology languages.

PubMed

Tirmizi, Syed Hamid; Aitken, Stuart; Moreira, Dilvan A; Mungall, Chris; Sequeda, Juan; Shah, Nigam H; Miranker, Daniel P

2011-03-07

Ontologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL. We have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source. Our transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner.

Mapping between the OBO and OWL ontology languages

PubMed Central

2011-01-01

Background Ontologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL. Results We have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source. Conclusions Our transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner

Identification of key microRNAs and genes in preeclampsia by bioinformatics analysis

PubMed Central

Luo, Shouling; Cao, Nannan; Tang, Yao; Gu, Weirong

2017-01-01

Preeclampsia is a leading cause of perinatal maternal–foetal mortality and morbidity. The aim of this study is to identify the key microRNAs and genes in preeclampsia and uncover their potential functions. We downloaded the miRNA expression profile of GSE84260 and the gene expression profile of GSE73374 from the Gene Expression Omnibus database. Differentially expressed miRNAs and genes were identified and compared to miRNA-target information from MiRWalk 2.0, and a total of 65 differentially expressed miRNAs (DEMIs), including 32 up-regulated miRNAs and 33 down-regulated miRNAs, and 91 differentially expressed genes (DEGs), including 83 up-regulated genes and 8 down-regulated genes, were identified. The pathway enrichment analyses of the DEMIs showed that the up-regulated DEMIs were enriched in the Hippo signalling pathway and MAPK signalling pathway, and the down-regulated DEMIs were enriched in HTLV-I infection and miRNAs in cancers. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses of the DEGs were performed using Multifaceted Analysis Tool for Human Transcriptome. The up-regulated DEGs were enriched in biological processes (BPs), including the response to cAMP, response to hydrogen peroxide and cell-cell adhesion mediated by integrin; no enrichment of down-regulated DEGs was identified. KEGG analysis showed that the up-regulated DEGs were enriched in the Hippo signalling pathway and pathways in cancer. A PPI network of the DEGs was constructed by using Cytoscape software, and FOS, STAT1, MMP14, ITGB1, VCAN, DUSP1, LDHA, MCL1, MET, and ZFP36 were identified as the hub genes. The current study illustrates a characteristic microRNA profile and gene profile in preeclampsia, which may contribute to the interpretation of the progression of preeclampsia and provide novel biomarkers and therapeutic targets for preeclampsia. PMID:28594854

T-cell lymphomas associated gene expression signature: Bioinformatics analysis based on gene expression Omnibus.

PubMed

Zhou, Lei-Lei; Xu, Xiao-Yue; Ni, Jie; Zhao, Xia; Zhou, Jian-Wei; Feng, Ji-Feng

2018-06-01

Due to the low incidence and the heterogeneity of subtypes, the biological process of T-cell lymphomas is largely unknown. Although many genes have been detected in T-cell lymphomas, the role of these genes in biological process of T-cell lymphomas was not further analyzed. Two qualified datasets were downloaded from Gene Expression Omnibus database. The biological functions of differentially expressed genes were evaluated by gene ontology enrichment and KEGG pathway analysis. The network for intersection genes was constructed by the cytoscape v3.0 software. Kaplan-Meier survival curves and log-rank test were employed to assess the association between differentially expressed genes and clinical characters. The intersection mRNAs were proved to be associated with fundamental processes of T-cell lymphoma cells. These intersection mRNAs were involved in the activation of some cancer-related pathways, including PI3K/AKT, Ras, JAK-STAT, and NF-kappa B signaling pathway. PDGFRA, CXCL12, and CCL19 were the most significant central genes in the signal-net analysis. The results of survival analysis are not entirely credible. Our findings uncovered aberrantly expressed genes and a complex RNA signal network in T-cell lymphomas and indicated cancer-related pathways involved in disease initiation and progression, providing a new insight for biotargeted therapy in T-cell lymphomas. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient-gene-phenotype interactions.

PubMed

Joslin, A C; Green, R; German, J B; Lange, M C

2014-09-01

Advances in the development of bioinformatic tools continue to improve investigators' ability to interrogate, organize, and derive knowledge from large amounts of heterogeneous information. These tools often require advanced technical skills not possessed by life scientists. User-friendly, low-barrier-to-entry methods of visualizing nutrigenomics information are yet to be developed. We utilized concept mapping software from the Institute for Human and Machine Cognition to create a conceptual model of diet and health-related data that provides a foundation for future nutrigenomics ontologies describing published nutrient-gene/polymorphism-phenotype data. In this model, maps containing phenotype, nutrient, gene product, and genetic polymorphism interactions are visualized as triples of two concepts linked together by a linking phrase. These triples, or "knowledge propositions," contextualize aggregated data and information into easy-to-read knowledge maps. Maps of these triples enable visualization of genes spanning the One-Carbon Metabolism (OCM) pathway, their sequence variants, and multiple literature-mined associations including concepts relevant to nutrition, phenotypes, and health. The concept map development process documents the incongruity of information derived from pathway databases versus literature resources. This conceptual model highlights the importance of incorporating information about genes in upstream pathways that provide substrates, as well as downstream pathways that utilize products of the pathway under investigation, in this case OCM. Other genes and their polymorphisms, such as TCN2 and FUT2, although not directly involved in OCM, potentially alter OCM pathway functionality. These upstream gene products regulate substrates such as B12. Constellations of polymorphisms affecting the functionality of genes along OCM, together with substrate and cofactor availability, may impact resultant phenotypes. These conceptual maps provide a foundational

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.

PubMed

Cheng, Liang; Jiang, Yue; Ju, Hong; Sun, Jie; Peng, Jiajie; Zhou, Meng; Hu, Yang

2018-01-19

Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown. We proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations. The experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.

Sequencing, de novo assembly and characterization of the spotted scat Scatophagus argus (Linnaeus 1766) transcriptome for discovery of reproduction related genes and SSRs

NASA Astrophysics Data System (ADS)

Yang, Wei; Chen, Huapu; Cui, Xuefan; Zhang, Kewei; Jiang, Dongneng; Deng, Siping; Zhu, Chunhua; Li, Guangli

2017-09-01

Spotted scat (Scatophagus argus) is an economically important farmed fish, particularly in East and Southeast Asia. Because there has been little research on reproductive development and regulation in this species, the lack of a mature artificial reproduction technology remains a barrier for the sustainable development of the aquaculture industry. More genetic and genomic background knowledge is urgently needed for an in-depth understanding of the molecular mechanism of reproductive process and identification of functional genes related to sexual differentiation, gonad maturation and gametogenesis. For these reasons, we performed transcriptomic analysis on spotted scat using a multiple tissue sample mixing strategy. The Illumina RNA sequencing generated 118 510 486 raw reads. After trimming, de novo assembly was performed and yielded 99 888 unigenes with an average length of 905.75 bp. A total of 45 015 unigenes were successfully annotated to the Nr, Swiss-Prot, KOG and KEGG databases. Additionally, 23 783 and 27 183 annotated unigenes were assigned to 56 Gene Ontology (GO) functional groups and 228 KEGG pathways, respectively. Subsequently, 2 474 transcripts associated with reproduction were selected using GO term and KEGG pathway assignments, and a number of reproduction-related genes involved in sex differentiation, gonad development and gametogenesis were identified. Furthermore, 22 279 simple sequence repeat (SSR) loci were discovered and characterized. The comprehensive transcript dataset described here greatly increases the genetic information available for spotted scat and contributes valuable sequence resources for functional gene mining and analysis. Candidate transcripts involved in reproduction would make good starting points for future studies on reproductive mechanisms, and the putative sex differentiation-related genes will be helpful for sex-determining gene identification and sex-specific marker isolation. Lastly, the SSRs can serve as marker

Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex.

PubMed

Pavlidis, Paul; Qin, Jie; Arango, Victoria; Mann, John J; Sibille, Etienne

2004-06-01

One of the challenges in the analysis of gene expression data is placing the results in the context of other data available about genes and their relationships to each other. Here, we approach this problem in the study of gene expression changes associated with age in two areas of the human prefrontal cortex, comparing two computational methods. The first method, "overrepresentation analysis" (ORA), is based on statistically evaluating the fraction of genes in a particular gene ontology class found among the set of genes showing age-related changes in expression. The second method, "functional class scoring" (FCS), examines the statistical distribution of individual gene scores among all genes in the gene ontology class and does not involve an initial gene selection step. We find that FCS yields more consistent results than ORA, and the results of ORA depended strongly on the gene selection threshold. Our findings highlight the utility of functional class scoring for the analysis of complex expression data sets and emphasize the advantage of considering all available genomic information rather than sets of genes that pass a predetermined "threshold of significance."

Automated Gene Ontology annotation for anonymous sequence data.

PubMed

Hennig, Steffen; Groth, Detlef; Lehrach, Hans

2003-07-01

Gene Ontology (GO) is the most widely accepted attempt to construct a unified and structured vocabulary for the description of genes and their products in any organism. Annotation by GO terms is performed in most of the current genome projects, which besides generality has the advantage of being very convenient for computer based classification methods. However, direct use of GO in small sequencing projects is not easy, especially for species not commonly represented in public databases. We present a software package (GOblet), which performs annotation based on GO terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. The paper also addresses the reliability of automated GO annotations by using a reference set of more than 6000 human proteins. The GOblet server is accessible at http://goblet.molgen.mpg.de.

Predicting the Extension of Biomedical Ontologies

PubMed Central

Pesquita, Catia; Couto, Francisco M.

2012-01-01

Developing and extending a biomedical ontology is a very demanding task that can never be considered complete given our ever-evolving understanding of the life sciences. Extension in particular can benefit from the automation of some of its steps, thus releasing experts to focus on harder tasks. Here we present a strategy to support the automation of change capturing within ontology extension where the need for new concepts or relations is identified. Our strategy is based on predicting areas of an ontology that will undergo extension in a future version by applying supervised learning over features of previous ontology versions. We used the Gene Ontology as our test bed and obtained encouraging results with average f-measure reaching 0.79 for a subset of biological process terms. Our strategy was also able to outperform state of the art change capturing methods. In addition we have identified several issues concerning prediction of ontology evolution, and have delineated a general framework for ontology extension prediction. Our strategy can be applied to any biomedical ontology with versioning, to help focus either manual or semi-automated extension methods on areas of the ontology that need extension. PMID:23028267

Genetic resources for methane production from biomass described with the Gene Ontology.

PubMed

Purwantini, Endang; Torto-Alalibo, Trudy; Lomax, Jane; Setubal, João C; Tyler, Brett M; Mukhopadhyay, Biswarup

2014-01-01

Methane (CH4) is a valuable fuel, constituting 70-95% of natural gas, and a potent greenhouse gas. Release of CH4 into the atmosphere contributes to climate change. Biological CH4 production or methanogenesis is mostly performed by methanogens, a group of strictly anaerobic archaea. The direct substrates for methanogenesis are H2 plus CO2, acetate, formate, methylamines, methanol, methyl sulfides, and ethanol or a secondary alcohol plus CO2. In numerous anaerobic niches in nature, methanogenesis facilitates mineralization of complex biopolymers such as carbohydrates, lipids and proteins generated by primary producers. Thus, methanogens are critical players in the global carbon cycle. The same process is used in anaerobic treatment of municipal, industrial and agricultural wastes, reducing the biological pollutants in the wastes and generating methane. It also holds potential for commercial production of natural gas from renewable resources. This process operates in digestive systems of many animals, including cattle, and humans. In contrast, in deep-sea hydrothermal vents methanogenesis is a primary production process, allowing chemosynthesis of biomaterials from H2 plus CO2. In this report we present Gene Ontology (GO) terms that can be used to describe processes, functions and cellular components involved in methanogenic biodegradation and biosynthesis of specialized coenzymes that methanogens use. Some of these GO terms were previously available and the rest were generated in our Microbial Energy Gene Ontology (MENGO) project. A recently discovered non-canonical CH4 production process is also described. We have performed manual GO annotation of selected methanogenesis genes, based on experimental evidence, providing "gold standards" for machine annotation and automated discovery of methanogenesis genes or systems in diverse genomes. Most of the GO-related information presented in this report is available at the MENGO website (http://www.mengo.biochem.vt.edu/).

Genetic resources for methane production from biomass described with the Gene Ontology

PubMed Central

Purwantini, Endang; Torto-Alalibo, Trudy; Lomax, Jane; Setubal, João C.; Tyler, Brett M.; Mukhopadhyay, Biswarup

2014-01-01

Methane (CH4) is a valuable fuel, constituting 70–95% of natural gas, and a potent greenhouse gas. Release of CH4 into the atmosphere contributes to climate change. Biological CH4 production or methanogenesis is mostly performed by methanogens, a group of strictly anaerobic archaea. The direct substrates for methanogenesis are H2 plus CO2, acetate, formate, methylamines, methanol, methyl sulfides, and ethanol or a secondary alcohol plus CO2. In numerous anaerobic niches in nature, methanogenesis facilitates mineralization of complex biopolymers such as carbohydrates, lipids and proteins generated by primary producers. Thus, methanogens are critical players in the global carbon cycle. The same process is used in anaerobic treatment of municipal, industrial and agricultural wastes, reducing the biological pollutants in the wastes and generating methane. It also holds potential for commercial production of natural gas from renewable resources. This process operates in digestive systems of many animals, including cattle, and humans. In contrast, in deep-sea hydrothermal vents methanogenesis is a primary production process, allowing chemosynthesis of biomaterials from H2 plus CO2. In this report we present Gene Ontology (GO) terms that can be used to describe processes, functions and cellular components involved in methanogenic biodegradation and biosynthesis of specialized coenzymes that methanogens use. Some of these GO terms were previously available and the rest were generated in our Microbial Energy Gene Ontology (MENGO) project. A recently discovered non-canonical CH4 production process is also described. We have performed manual GO annotation of selected methanogenesis genes, based on experimental evidence, providing “gold standards” for machine annotation and automated discovery of methanogenesis genes or systems in diverse genomes. Most of the GO-related information presented in this report is available at the MENGO website (http

Differential protein-coding gene and long noncoding RNA expression in smoking-related lung squamous cell carcinoma.

PubMed

Li, Shicheng; Sun, Xiao; Miao, Shuncheng; Liu, Jia; Jiao, Wenjie

2017-11-01

Cigarette smoking is one of the greatest preventable risk factors for developing cancer, and most cases of lung squamous cell carcinoma (lung SCC) are associated with smoking. The pathogenesis mechanism of tumor progress is unclear. This study aimed to identify biomarkers in smoking-related lung cancer, including protein-coding gene, long noncoding RNA, and transcription factors. We selected and obtained messenger RNA microarray datasets and clinical data from the Gene Expression Omnibus database to identify gene expression altered by cigarette smoking. Integrated bioinformatic analysis was used to clarify biological functions of the identified genes, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the construction of a protein-protein interaction network, transcription factor, and statistical analyses. Subsequent quantitative real-time PCR was utilized to verify these bioinformatic analyses. Five hundred and ninety-eight differentially expressed genes and 21 long noncoding RNA were identified in smoking-related lung SCC. GO and KEGG pathway analysis showed that identified genes were enriched in the cancer-related functions and pathways. The protein-protein interaction network revealed seven hub genes identified in lung SCC. Several transcription factors and their binding sites were predicted. The results of real-time quantitative PCR revealed that AURKA and BIRC5 were significantly upregulated and LINC00094 was downregulated in the tumor tissues of smoking patients. Further statistical analysis indicated that dysregulation of AURKA, BIRC5, and LINC00094 indicated poor prognosis in lung SCC. Protein-coding genes AURKA, BIRC5, and LINC00094 could be biomarkers or therapeutic targets for smoking-related lung SCC. © 2017 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

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

PubMed

Mazandu, Gaston K; Mulder, Nicola J

2013-09-25

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

Ontology or formal ontology

NASA Astrophysics Data System (ADS)

Žáček, Martin

2017-07-01

Ontology or formal ontology? Which word is correct? The aim of this article is to introduce correct terms and explain their basis. Ontology describes a particular area of interest (domain) in a formal way - defines the classes of objects that are in that area, and relationships that may exist between them. Meaning of ontology consists mainly in facilitating communication between people, improve collaboration of software systems and in the improvement of systems engineering. Ontology in all these areas offer the possibility of unification of view, maintaining consistency and unambiguity.

Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

PubMed

Holliday, Gemma L; Davidson, Rebecca; Akiva, Eyal; Babbitt, Patricia C

2017-01-01

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

Impact of ontology evolution on functional analyses.

PubMed

Groß, Anika; Hartung, Michael; Prüfer, Kay; Kelso, Janet; Rahm, Erhard

2012-10-15

Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. Here, we investigate to what degree modifications of the Gene Ontology (GO) impact these statistical analyses for both experimental and simulated data. The analysis is based on new measures for the stability of result sets and considers different ontology and annotation changes. Our results show that past changes in the GO are non-uniformly distributed over different branches of the ontology. Considering the semantic relatedness of significant categories in analysis results allows a more realistic stability assessment for functional enrichment studies. We observe that the results of term-enrichment analyses tend to be surprisingly stable despite changes in ontology and annotation.

Semantic Similarity in Biomedical Ontologies

PubMed Central

Pesquita, Catia; Faria, Daniel; Falcão, André O.; Lord, Phillip; Couto, Francisco M.

2009-01-01

In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research. PMID:19649320

DOSim: an R package for similarity between diseases based on Disease Ontology.

PubMed

Li, Jiang; Gong, Binsheng; Chen, Xi; Liu, Tao; Wu, Chao; Zhang, Fan; Li, Chunquan; Li, Xiang; Rao, Shaoqi; Li, Xia

2011-06-29

The construction of the Disease Ontology (DO) has helped promote the investigation of diseases and disease risk factors. DO enables researchers to analyse disease similarity by adopting semantic similarity measures, and has expanded our understanding of the relationships between different diseases and to classify them. Simultaneously, similarities between genes can also be analysed by their associations with similar diseases. As a result, disease heterogeneity is better understood and insights into the molecular pathogenesis of similar diseases have been gained. However, bioinformatics tools that provide easy and straight forward ways to use DO to study disease and gene similarity simultaneously are required. We have developed an R-based software package (DOSim) to compute the similarity between diseases and to measure the similarity between human genes in terms of diseases. DOSim incorporates a DO-based enrichment analysis function that can be used to explore the disease feature of an independent gene set. A multilayered enrichment analysis (GO and KEGG annotation) annotation function that helps users explore the biological meaning implied in a newly detected gene module is also part of the DOSim package. We used the disease similarity application to demonstrate the relationship between 128 different DO cancer terms. The hierarchical clustering of these 128 different cancers showed modular characteristics. In another case study, we used the gene similarity application on 361 obesity-related genes. The results revealed the complex pathogenesis of obesity. In addition, the gene module detection and gene module multilayered annotation functions in DOSim when applied on these 361 obesity-related genes helped extend our understanding of the complex pathogenesis of obesity risk phenotypes and the heterogeneity of obesity-related diseases. DOSim can be used to detect disease-driven gene modules, and to annotate the modules for functions and pathways. The DOSim package

InteGO2: A web tool for measuring and visualizing gene semantic similarities using Gene Ontology

DOE PAGES

Peng, Jiajie; Li, Hongxiang; Liu, Yongzhuang; ...

2016-08-31

Here, the Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation. As a result, we present InteGO2, a web toolmore » that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks. In conclusion, InteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface.« less

InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology.

PubMed

Peng, Jiajie; Li, Hongxiang; Liu, Yongzhuang; Juan, Liran; Jiang, Qinghua; Wang, Yadong; Chen, Jin

2016-08-31

The Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation. We present InteGO2, a web tool that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks. InteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface. InteGO2 can be accessed via http://mlg.hit.edu.cn:8089/ .

InteGO2: A web tool for measuring and visualizing gene semantic similarities using Gene Ontology

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

Peng, Jiajie; Li, Hongxiang; Liu, Yongzhuang

Here, the Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation. As a result, we present InteGO2, a web toolmore » that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks. In conclusion, InteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface.« less

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

PubMed

Mazandu, Gaston K; Chimusa, Emile R; Mbiyavanga, Mamana; Mulder, Nicola J

2016-02-01

Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs. A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence). gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

PubMed

Zhang, Song; Cao, Jing; Kong, Y Megan; Scheuermann, Richard H

2010-04-01

A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements.

PubMed

Mi, Huaiyu; Huang, Xiaosong; Muruganujan, Anushya; Tang, Haiming; Mills, Caitlin; Kang, Diane; Thomas, Paul D

2017-01-04

The PANTHER database (Protein ANalysis THrough Evolutionary Relationships, http://pantherdb.org) contains comprehensive information on the evolution and function of protein-coding genes from 104 completely sequenced genomes. PANTHER software tools allow users to classify new protein sequences, and to analyze gene lists obtained from large-scale genomics experiments. In the past year, major improvements include a large expansion of classification information available in PANTHER, as well as significant enhancements to the analysis tools. Protein subfamily functional classifications have more than doubled due to progress of the Gene Ontology Phylogenetic Annotation Project. For human genes (as well as a few other organisms), PANTHER now also supports enrichment analysis using pathway classifications from the Reactome resource. The gene list enrichment tools include a new 'hierarchical view' of results, enabling users to leverage the structure of the classifications/ontologies; the tools also allow users to upload genetic variant data directly, rather than requiring prior conversion to a gene list. The updated coding single-nucleotide polymorphisms (SNP) scoring tool uses an improved algorithm. The hidden Markov model (HMM) search tools now use HMMER3, dramatically reducing search times and improving accuracy of E-value statistics. Finally, the PANTHER Tree-Attribute Viewer has been implemented in JavaScript, with new views for exploring protein sequence evolution. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

De novo assembly of the sea trout (Salmo trutta m. trutta) skin transcriptome to identify putative genes involved in the immune response and epidermal mucus secretion

PubMed Central

Wenne, Roman; Burzynski, Artur

2017-01-01

In fish, the skin is a multifunctional organ and the first barrier against pathogens. Salmonids differ in their susceptibility to microorganisms due to varied skin morphology and gene expression patterns. The brown trout is a salmonid species with important commercial and ecological value in Europe. However, there is a lack of knowledge regarding the genes involved in the immune response and mucus secretion in the skin of this fish. Thus, we characterized the skin transcriptome of anadromous brown trout using next-generation sequencing (NGS). A total of 1,348,306 filtered reads were obtained and assembled into 75,970 contigs. Of these contigs 48.57% were identified using BLAST tool searches against four public databases. KEGG pathway and Gene Ontology analyses revealed that 13.40% and 34.57% of the annotated transcripts, respectively, represent a variety of biological processes and functions. Among the identified KEGG Orthology categories, the best represented were signal transduction (23.28%) and immune system (8.82%), with a variety of genes involved in immune pathways, implying the differentiation of immune responses in the trout skin. We also identified and transcriptionally characterized 8 types of mucin proteins–the main structural components of the mucosal layer. Moreover, 140 genes involved in mucin synthesis were identified, and 1,119 potential simple sequence repeats (SSRs) were detected in 3,134 transcripts. PMID:28212382

Protein-Protein Interaction Network and Gene Ontology

NASA Astrophysics Data System (ADS)

Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

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

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

PubMed Central

2013-01-01

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

Inferring ontology graph structures using OWL reasoning.

PubMed

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Role of miR-452-5p in the tumorigenesis of prostate cancer: A study based on the Cancer Genome Atl(TCGA), Gene Expression Omnibus (GEO), and bioinformatics analysis.

PubMed

Gao, Li; Zhang, Li-Jie; Li, Sheng-Hua; Wei, Li-Li; Luo, Bin; He, Rong-Quan; Xia, Shuang

2018-03-06

MiR-452-5p has been reported to be down-regulated in prostate cancer, affecting the development of this type of cancer. However, the molecular mechanism of miR-452-5p in prostate cancer remains unclear. Therefore, we investigated the network of target genes of miR-452-5p in prostate cancer using bioinformatics analyses. We first analyzed the expression profiles and prognostic value of miR-452-5p in prostate cancer tissues from a public database. Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), PANTHER pathway analyses, and a disease ontology (DG) analysis were performed to find the molecular functions of the target genes from GSE datasets and miRWalk. Finally, we validated hub genes from the protein-protein interaction (PPI) networks of the target genes in the Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA). Narrowing down the optimal target genes was conducted by seeking the common parts of up-regulated genes from GEPIA, down-regulated genes from GSE datasets, and predicted genes in miRWalk. Based on mining of GEO and ArrayExpress microarray chips and miRNA-Seq data in the TCGA database, which includes 1007 prostate cancer samples and 387 non-cancer samples, miR-452-5p is shown to be down-regulated in prostate cancer. GO, KEGG, and PANTHER pathway analyses suggested that the target genes might participate in important biological processes, such as transforming growth factor beta signaling and the positive regulation of brown fat cell differentiation and mesenchymal cell differentiation, as well as the Ras signaling pathway and pathways regulating the pluripotency of stem cells and arrhythmogenic right ventricular cardiomyopathy (ARVC). Nine genes-GABBR, PNISR, NTSR1, DOCK1, EREG, SFRP1, PTGS2, LEF1, and BMP2-were defined as hub genes in the PPI network. Three genes-FAM174B, SLC30A4, and SLIT1-were jointly shared by GEPIA, the GSE datasets, and miRWalk. Down-regulated miR-452-5p might play an

Tutorial on Protein Ontology Resources

PubMed Central

Arighi, Cecilia; Drabkin, Harold; Christie, Karen R.; Ross, Karen; Natale, Darren

2017-01-01

The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, and post-translationally modified forms), and protein complexes. PRO defines classes of proteins and protein complexes, both species-specific and species non-specific, and indicates their relationships in a hierarchical framework, supporting accurate protein annotation at the appropriate level of granularity, analyses of protein conservation across species, and semantic reasoning. In this first section of this chapter, we describe the PRO framework including categories of PRO terms and the relationship of PRO to other ontologies and protein resources. Next, we provide a tutorial about the PRO website (proconsortium.org) where users can browse and search the PRO hierarchy, view reports on individual PRO terms, and visualize relationships among PRO terms in a hierarchical table view, a multiple sequence alignment view, and a Cytoscape network view. Finally, we describe several examples illustrating the unique and rich information available in PRO. PMID:28150233

Codon bias and gene ontology in holometabolous and hemimetabolous insects.

PubMed

Carlini, David B; Makowski, Matthew

2015-12-01

The relationship between preferred codon use (PCU), developmental mode, and gene ontology (GO) was investigated in a sample of nine insect species with sequenced genomes. These species were selected to represent two distinct modes of insect development, holometabolism and hemimetabolism, with an aim toward determining whether the differences in developmental timing concomitant with developmental mode would be mirrored by differences in PCU in their developmental genes. We hypothesized that the developmental genes of holometabolous insects should be under greater selective pressure for efficient translation, manifest as increased PCU, than those of hemimetabolous insects because holometabolism requires abundant protein expression over shorter time intervals than hemimetabolism, where proteins are required more uniformly in time. Preferred codon sets were defined for each species, from which the frequency of PCU for each gene was obtained. Although there were substantial differences in the genomic base composition of holometabolous and hemimetabolous insects, both groups exhibited a general preference for GC-ending codons, with the former group having higher PCU averaged across all genes. For each species, the biological process GO term for each gene was assigned that of its Drosophila homolog(s), and PCU was calculated for each GO term category. The top two GO term categories for PCU enrichment in the holometabolous insects were anatomical structure development and cell differentiation. The increased PCU in the developmental genes of holometabolous insects may reflect a general strategy to maximize the protein production of genes expressed in bursts over short time periods, e.g., heat shock proteins. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 686-698, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

A transversal approach to predict gene product networks from ontology-based similarity

PubMed Central

Chabalier, Julie; Mosser, Jean; Burgun, Anita

2007-01-01

Background Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression. Results The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity. Conclusion Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression. PMID:17605807

STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

PubMed Central

2013-01-01

Background Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins. Results As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms. Conclusion Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/. PMID:23409969

GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology.

PubMed

Caniza, Horacio; Romero, Alfonso E; Heron, Samuel; Yang, Haixuan; Devoto, Alessandra; Frasca, Marco; Mesiti, Marco; Valentini, Giorgio; Paccanaro, Alberto

2014-08-01

We present GOssTo, the Gene Ontology semantic similarity Tool, a user-friendly software system for calculating semantic similarities between gene products according to the Gene Ontology. GOssTo is bundled with six semantic similarity measures, including both term- and graph-based measures, and has extension capabilities to allow the user to add new similarities. Importantly, for any measure, GOssTo can also calculate the Random Walk Contribution that has been shown to greatly improve the accuracy of similarity measures. GOssTo is very fast, easy to use, and it allows the calculation of similarities on a genomic scale in a few minutes on a regular desktop machine. alberto@cs.rhul.ac.uk GOssTo is available both as a stand-alone application running on GNU/Linux, Windows and MacOS from www.paccanarolab.org/gossto and as a web application from www.paccanarolab.org/gosstoweb. The stand-alone application features a simple and concise command line interface for easy integration into high-throughput data processing pipelines. © The Author 2014. Published by Oxford University Press.

DeMO: An Ontology for Discrete-event Modeling and Simulation.

PubMed

Silver, Gregory A; Miller, John A; Hybinette, Maria; Baramidze, Gregory; York, William S

2011-09-01

Several fields have created ontologies for their subdomains. For example, the biological sciences have developed extensive ontologies such as the Gene Ontology, which is considered a great success. Ontologies could provide similar advantages to the Modeling and Simulation community. They provide a way to establish common vocabularies and capture knowledge about a particular domain with community-wide agreement. Ontologies can support significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved knowledge discovery capabilities. This paper discusses the design and development of an ontology for Modeling and Simulation called the Discrete-event Modeling Ontology (DeMO), and it presents prototype applications that demonstrate various uses and benefits that such an ontology may provide to the Modeling and Simulation community.

DeMO: An Ontology for Discrete-event Modeling and Simulation

PubMed Central

Silver, Gregory A; Miller, John A; Hybinette, Maria; Baramidze, Gregory; York, William S

2011-01-01

Several fields have created ontologies for their subdomains. For example, the biological sciences have developed extensive ontologies such as the Gene Ontology, which is considered a great success. Ontologies could provide similar advantages to the Modeling and Simulation community. They provide a way to establish common vocabularies and capture knowledge about a particular domain with community-wide agreement. Ontologies can support significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved knowledge discovery capabilities. This paper discusses the design and development of an ontology for Modeling and Simulation called the Discrete-event Modeling Ontology (DeMO), and it presents prototype applications that demonstrate various uses and benefits that such an ontology may provide to the Modeling and Simulation community. PMID:22919114

XML, Ontologies, and Their Clinical Applications.

PubMed

Yu, Chunjiang; Shen, Bairong

2016-01-01

The development of information technology has resulted in its penetration into every area of clinical research. Various clinical systems have been developed, which produce increasing volumes of clinical data. However, saving, exchanging, querying, and exploiting these data are challenging issues. The development of Extensible Markup Language (XML) has allowed the generation of flexible information formats to facilitate the electronic sharing of structured data via networks, and it has been used widely for clinical data processing. In particular, XML is very useful in the fields of data standardization, data exchange, and data integration. Moreover, ontologies have been attracting increased attention in various clinical fields in recent years. An ontology is the basic level of a knowledge representation scheme, and various ontology repositories have been developed, such as Gene Ontology and BioPortal. The creation of these standardized repositories greatly facilitates clinical research in related fields. In this chapter, we discuss the basic concepts of XML and ontologies, as well as their clinical applications.

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

Identification of Immune-Related Genes and Development of SSR/SNP Markers from the Spleen Transcriptome of Schizothorax prenanti.

PubMed

Luo, Hui; Xiao, Shijun; Ye, Hua; Zhang, Zhengshi; Lv, Changhuan; Zheng, Shuming; Wang, Zhiyong; Wang, Xiaoqing

2016-01-01

Schizothorax prenanti (S. prenanti) is mainly distributed in the upstream regions of the Yangtze River and its tributaries in China. This species is indigenous and commercially important. However, in recent years, wild populations and aquacultures have faced the serious challenges of germplasm variation loss and an increased susceptibility to a range of pathogens. Currently, the genetics and immune mechanisms of S. prenanti are unknown, partly due to a lack of genome and transcriptome information. Here, we sought to identify genes related to immune functions and to identify molecular markers to study the function of these genes and for trait mapping. To this end, the transcriptome from spleen tissues of S. prenanti was analyzed and sequenced. Using paired-end reads from the Illumina Hiseq2500 platform, 48,517 transcripts were isolated from the spleen transcriptome. These transcripts could be clustered into 37,785 unigenes with an N50 length of 2,539 bp. The majority of the unigenes (35,653, 94.4%) were successfully annotated using non-redundant nucleotide sequence analysis (nt), and the non-redundant protein (nr), Swiss-Prot, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. KEGG pathway assignment identified more than 500 immune-related genes. Furthermore, 7,545 putative simple sequence repeats (SSRs), 857,535 single nucleotide polymorphisms (SNPs), and 53,481 insertion/deletion (InDels) were detected from the transcriptome. This is the first reported high-throughput transcriptome analysis of S. prenanti, and it provides valuable genetic resources for the investigation of immune mechanisms, conservation of germplasm, and molecular marker-assisted breeding of S. prenanti.

DeepSAGE Based Differential Gene Expression Analysis under Cold and Freeze Stress in Seabuckthorn (Hippophae rhamnoides L.)

PubMed Central

Chaudhary, Saurabh; Sharma, Prakash C.

2015-01-01

Seabuckthorn (Hippophae rhamnoides L.), an important plant species of Indian Himalayas, is well known for its immense medicinal and nutritional value. The plant has the ability to sustain growth in harsh environments of extreme temperatures, drought and salinity. We employed DeepSAGE, a tag based approach, to identify differentially expressed genes under cold and freeze stress in seabuckthorn. In total 36.2 million raw tags including 13.9 million distinct tags were generated using Illumina sequencing platform for three leaf tissue libraries including control (CON), cold stress (CS) and freeze stress (FS). After discarding low quality tags, 35.5 million clean tags including 7 million distinct clean tags were obtained. In all, 11922 differentially expressed genes (DEGs) including 6539 up regulated and 5383 down regulated genes were identified in three comparative setups i.e. CON vs CS, CON vs FS and CS vs FS. Gene ontology and KEGG pathway analysis were performed to assign gene ontology term to DEGs and ascertain their biological functions. DEGs were mapped back to our existing seabuckthorn transcriptome assembly comprising of 88,297 putative unigenes leading to the identification of 428 cold and freeze stress responsive genes. Expression of randomly selected 22 DEGs was validated using qRT-PCR that further supported our DeepSAGE results. The present study provided a comprehensive view of global gene expression profile of seabuckthorn under cold and freeze stresses. The DeepSAGE data could also serve as a valuable resource for further functional genomics studies aiming selection of candidate genes for development of abiotic stress tolerant transgenic plants. PMID:25803684

DeepSAGE based differential gene expression analysis under cold and freeze stress in seabuckthorn (Hippophae rhamnoides L.).

PubMed

Chaudhary, Saurabh; Sharma, Prakash C

2015-01-01

Seabuckthorn (Hippophae rhamnoides L.), an important plant species of Indian Himalayas, is well known for its immense medicinal and nutritional value. The plant has the ability to sustain growth in harsh environments of extreme temperatures, drought and salinity. We employed DeepSAGE, a tag based approach, to identify differentially expressed genes under cold and freeze stress in seabuckthorn. In total 36.2 million raw tags including 13.9 million distinct tags were generated using Illumina sequencing platform for three leaf tissue libraries including control (CON), cold stress (CS) and freeze stress (FS). After discarding low quality tags, 35.5 million clean tags including 7 million distinct clean tags were obtained. In all, 11922 differentially expressed genes (DEGs) including 6539 up regulated and 5383 down regulated genes were identified in three comparative setups i.e. CON vs CS, CON vs FS and CS vs FS. Gene ontology and KEGG pathway analysis were performed to assign gene ontology term to DEGs and ascertain their biological functions. DEGs were mapped back to our existing seabuckthorn transcriptome assembly comprising of 88,297 putative unigenes leading to the identification of 428 cold and freeze stress responsive genes. Expression of randomly selected 22 DEGs was validated using qRT-PCR that further supported our DeepSAGE results. The present study provided a comprehensive view of global gene expression profile of seabuckthorn under cold and freeze stresses. The DeepSAGE data could also serve as a valuable resource for further functional genomics studies aiming selection of candidate genes for development of abiotic stress tolerant transgenic plants.

Digital gene expression profiling of flax (Linum usitatissimum L.) stem peel identifies genes enriched in fiber-bearing phloem tissue.

PubMed

Guo, Yuan; Qiu, Caisheng; Long, Songhua; Chen, Ping; Hao, Dongmei; Preisner, Marta; Wang, Hui; Wang, Yufu

2017-08-30

To better understand the molecular mechanisms and gene expression characteristics associated with development of bast fiber cell within flax stem phloem, the gene expression profiling of flax stem peels and leaves were screened, using Illumina's Digital Gene Expression (DGE) analysis. Four DGE libraries (2 for stem peel and 2 for leaf), ranging from 6.7 to 9.2 million clean reads were obtained, which produced 7.0 million and 6.8 million mapped reads for flax stem peel and leave, respectively. By differential gene expression analysis, a total of 975 genes, of which 708 (73%) genes have protein-coding annotation, were identified as phloem enriched genes putatively involved in the processes of polysaccharide and cell wall metabolism. Differential expression genes (DEGs) was validated using quantitative RT-PCR, the expression pattern of all nine genes determined by qRT-PCR fitted in well with that obtained by sequencing analysis. Cluster and Gene Ontology (GO) analysis revealed that a large number of genes related to metabolic process, catalytic activity and binding category were expressed predominantly in the stem peels. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the phloem enriched genes suggested approximately 111 biological pathways. The large number of genes and pathways produced from DGE sequencing will expand our understanding of the complex molecular and cellular events in flax bast fiber development and provide a foundation for future studies on fiber development in other bast fiber crops. Copyright © 2017 Elsevier B.V. All rights reserved.

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

NASA Astrophysics Data System (ADS)

Gao, Wei; Zhu, Linli; Wang, Kaiyun

2015-12-01

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

Transcriptome Sequencing Identified Genes and Gene Ontologies Associated with Early Freezing Tolerance in Maize

PubMed Central

Li, Zhao; Hu, Guanghui; Liu, Xiangfeng; Zhou, Yao; Li, Yu; Zhang, Xu; Yuan, Xiaohui; Zhang, Qian; Yang, Deguang; Wang, Tianyu; Zhang, Zhiwu

2016-01-01

Originating in a tropical climate, maize has faced great challenges as cultivation has expanded to the majority of the world's temperate zones. In these zones, frost and cold temperatures are major factors that prevent maize from reaching its full yield potential. Among 30 elite maize inbred lines adapted to northern China, we identified two lines of extreme, but opposite, freezing tolerance levels—highly tolerant and highly sensitive. During the seedling stage of these two lines, we used RNA-seq to measure changes in maize whole genome transcriptome before and after freezing treatment. In total, 19,794 genes were expressed, of which 4550 exhibited differential expression due to either treatment (before or after freezing) or line type (tolerant or sensitive). Of the 4550 differently expressed genes, 948 exhibited differential expression due to treatment within line or lines under freezing condition. Analysis of gene ontology found that these 948 genes were significantly enriched for binding functions (DNA binding, ATP binding, and metal ion binding), protein kinase activity, and peptidase activity. Based on their enrichment, literature support, and significant levels of differential expression, 30 of these 948 genes were selected for quantitative real-time PCR (qRT-PCR) validation. The validation confirmed our RNA-Seq-based findings, with squared correlation coefficients of 80% and 50% in the tolerance and sensitive lines, respectively. This study provided valuable resources for further studies to enhance understanding of the molecular mechanisms underlying maize early freezing response and enable targeted breeding strategies for developing varieties with superior frost resistance to achieve yield potential. PMID:27774095

Ontology-based Brucella vaccine literature indexing and systematic analysis of gene-vaccine association network.

PubMed

Hur, Junguk; Xiang, Zuoshuang; Feldman, Eva L; He, Yongqun

2011-08-26

Vaccine literature indexing is poorly performed in PubMed due to limited hierarchy of Medical Subject Headings (MeSH) annotation in the vaccine field. Vaccine Ontology (VO) is a community-based biomedical ontology that represents various vaccines and their relations. SciMiner is an in-house literature mining system that supports literature indexing and gene name tagging. We hypothesize that application of VO in SciMiner will aid vaccine literature indexing and mining of vaccine-gene interaction networks. As a test case, we have examined vaccines for Brucella, the causative agent of brucellosis in humans and animals. The VO-based SciMiner (VO-SciMiner) was developed to incorporate a total of 67 Brucella vaccine terms. A set of rules for term expansion of VO terms were learned from training data, consisting of 90 biomedical articles related to Brucella vaccine terms. VO-SciMiner demonstrated high recall (91%) and precision (99%) from testing a separate set of 100 manually selected biomedical articles. VO-SciMiner indexing exhibited superior performance in retrieving Brucella vaccine-related papers over that obtained with MeSH-based PubMed literature search. For example, a VO-SciMiner search of "live attenuated Brucella vaccine" returned 922 hits as of April 20, 2011, while a PubMed search of the same query resulted in only 74 hits. Using the abstracts of 14,947 Brucella-related papers, VO-SciMiner identified 140 Brucella genes associated with Brucella vaccines. These genes included known protective antigens, virulence factors, and genes closely related to Brucella vaccines. These VO-interacting Brucella genes were significantly over-represented in biological functional categories, including metabolite transport and metabolism, replication and repair, cell wall biogenesis, intracellular trafficking and secretion, posttranslational modification, and chaperones. Furthermore, a comprehensive interaction network of Brucella vaccines and genes were identified. The asserted

Ontology-based Brucella vaccine literature indexing and systematic analysis of gene-vaccine association network

PubMed Central

2011-01-01

Background Vaccine literature indexing is poorly performed in PubMed due to limited hierarchy of Medical Subject Headings (MeSH) annotation in the vaccine field. Vaccine Ontology (VO) is a community-based biomedical ontology that represents various vaccines and their relations. SciMiner is an in-house literature mining system that supports literature indexing and gene name tagging. We hypothesize that application of VO in SciMiner will aid vaccine literature indexing and mining of vaccine-gene interaction networks. As a test case, we have examined vaccines for Brucella, the causative agent of brucellosis in humans and animals. Results The VO-based SciMiner (VO-SciMiner) was developed to incorporate a total of 67 Brucella vaccine terms. A set of rules for term expansion of VO terms were learned from training data, consisting of 90 biomedical articles related to Brucella vaccine terms. VO-SciMiner demonstrated high recall (91%) and precision (99%) from testing a separate set of 100 manually selected biomedical articles. VO-SciMiner indexing exhibited superior performance in retrieving Brucella vaccine-related papers over that obtained with MeSH-based PubMed literature search. For example, a VO-SciMiner search of "live attenuated Brucella vaccine" returned 922 hits as of April 20, 2011, while a PubMed search of the same query resulted in only 74 hits. Using the abstracts of 14,947 Brucella-related papers, VO-SciMiner identified 140 Brucella genes associated with Brucella vaccines. These genes included known protective antigens, virulence factors, and genes closely related to Brucella vaccines. These VO-interacting Brucella genes were significantly over-represented in biological functional categories, including metabolite transport and metabolism, replication and repair, cell wall biogenesis, intracellular trafficking and secretion, posttranslational modification, and chaperones. Furthermore, a comprehensive interaction network of Brucella vaccines and genes were

Transcriptome Analysis and Its Application in Identifying Genes Associated with Fruiting Body Development in Basidiomycete Hypsizygus marmoreus

PubMed Central

Chen, Hui; Zhao, Mingwen; Shi, Liang; Chen, Mingjie; Wang, Hong; Feng, Zhiyong

2015-01-01

To elucidate the mechanisms of fruit body development in H. marmoreus, a total of 43609521 high-quality RNA-seq reads were obtained from four developmental stages, including the mycelial knot (H-M), mycelial pigmentation (H-V), primordium (H-P) and fruiting body (H-F) stages. These reads were assembled to obtain 40568 unigenes with an average length of 1074 bp. A total of 26800 (66.06%) unigenes were annotated and analyzed with the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Eukaryotic Orthologous Group (KOG) databases. Differentially expressed genes (DEGs) from the four transcriptomes were analyzed. The KEGG enrichment analysis revealed that the mycelium pigmentation stage was associated with the MAPK, cAMP, and blue light signal transduction pathways. In addition, expression of the two-component system members changed with the transition from H-M to H-V, suggesting that light affected the expression of genes related to fruit body initiation in H. marmoreus. During the transition from H-V to H-P, stress signals associated with MAPK, cAMP and ROS signals might be the most important inducers. Our data suggested that nitrogen starvation might be one of the most important factors in promoting fruit body maturation, and nitrogen metabolism and mTOR signaling pathway were associated with this process. In addition, 30 genes of interest were analyzed by quantitative real-time PCR to verify their expression profiles at the four developmental stages. This study advances our understanding of the molecular mechanism of fruiting body development in H. marmoreus by identifying a wealth of new genes that may play important roles in mushroom morphogenesis. PMID:25837428

The gene expression profile of resistant and susceptible Bombyx mori strains reveals cypovirus-associated variations in host gene transcript levels.

PubMed

Guo, Rui; Wang, Simei; Xue, Renyu; Cao, Guangli; Hu, Xiaolong; Huang, Moli; Zhang, Yangqi; Lu, Yahong; Zhu, Liyuan; Chen, Fei; Liang, Zi; Kuang, Sulan; Gong, Chengliang

2015-06-01

High-throughput paired-end RNA sequencing (RNA-Seq) was performed to investigate the gene expression profile of a susceptible Bombyx mori strain, Lan5, and a resistant B. mori strain, Ou17, which were both orally infected with B. mori cypovirus (BmCPV) in the midgut. There were 330 and 218 up-regulated genes, while there were 147 and 260 down-regulated genes in the Lan5 and Ou17 strains, respectively. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment for differentially expressed genes (DEGs) were carried out. Moreover, gene interaction network (STRING) analyses were performed to analyze the relationships among the shared DEGs. Some of these genes were related and formed a large network, in which the genes for B. mori cuticular protein RR-2 motif 123 (BmCPR123) and the gene for B. mori DNA replication licensing factor Mcm2-like (BmMCM2) were key genes among the common up-regulated DEGs, whereas the gene for B. mori heat shock protein 20.1 (Bmhsp20.1) was the central gene among the shared down-regulated DEGs between Lan5 vs Lan5-CPV and Ou17 vs Ou17-CPV. These findings established a comprehensive database of genes that are differentially expressed in response to BmCPV infection between silkworm strains that differed in resistance to BmCPV and implied that these DEGs might be involved in B. mori immune responses against BmCPV infection.

Development of an Ontology for Periodontitis.

PubMed

Suzuki, Asami; Takai-Igarashi, Takako; Nakaya, Jun; Tanaka, Hiroshi

2015-01-01

In the clinical dentists and periodontal researchers' community, there is an obvious demand for a systems model capable of linking the clinical presentation of periodontitis to underlying molecular knowledge. A computer-readable representation of processes on disease development will give periodontal researchers opportunities to elucidate pathways and mechanisms of periodontitis. An ontology for periodontitis can be a model for integration of large variety of factors relating to a complex disease such as chronic inflammation in different organs accompanied by bone remodeling and immune system disorders, which has recently been referred to as osteoimmunology. Terms characteristic of descriptions related to the onset and progression of periodontitis were manually extracted from 194 review articles and PubMed abstracts by experts in periodontology. We specified all the relations between the extracted terms and constructed them into an ontology for periodontitis. We also investigated matching between classes of our ontology and that of Gene Ontology Biological Process. We developed an ontology for periodontitis called Periodontitis-Ontology (PeriO). The pathological progression of periodontitis is caused by complex, multi-factor interrelationships. PeriO consists of all the required concepts to represent the pathological progression and clinical treatment of periodontitis. The pathological processes were formalized with reference to Basic Formal Ontology and Relation Ontology, which accounts for participants in the processes realized by biological objects such as molecules and cells. We investigated the peculiarity of biological processes observed in pathological progression and medical treatments for the disease in comparison with Gene Ontology Biological Process (GO-BP) annotations. The results indicated that peculiarities of Perio existed in 1) granularity and context dependency of both the conceptualizations, and 2) causality intrinsic to the pathological processes

'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.

Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon

PubMed Central

2014-01-01

Background Elucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent ‘natural’ phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes. Results Previously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued. Conclusions The newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies. The ontology release files associated with the ontology merge described in this manuscript are available at: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/ Current ontology release files are available always available at: http://purl.obolibrary.org/obo/uberon/releases/ PMID:25009735

Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.

PubMed

Lamy, Jean-Baptiste

2017-07-01

Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies. From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations. We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning. Owlready has been successfully

First insights into the giant panda (Ailuropoda melanoleuca) blood transcriptome: a resource for novel gene loci and immunogenetics.

PubMed

Du, Lianming; Li, Wujiao; Fan, Zhenxin; Shen, Fujun; Yang, Mingyu; Wang, Zili; Jian, Zuoyi; Hou, Rong; Yue, Bisong; Zhang, Xiuyue

2015-07-01

The giant panda (Ailuropoda melanoleuca) is one of the most famous flagship species for conservation, and its draft genome has recently been assembled. However, the transcriptome is not yet available. In this study, the blood transcriptomes of three pandas were characterized and about 160 million sequencing reads were generated using Illumina HiSeq 2000 paired-end sequencing technology. The assembly yielded 92 598 transcripts with an average length of 1626 bp and N50 length of 2842 bp. Based on a sequence similarity search against nonredundant (nr) protein database, a total of 38 522 (41.6%) transcripts were annotated. Of these annotated transcripts, 25 142 and 8272 transcripts were assigned to gene ontology terms and clusters of orthologous group, respectively. A search against the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG) indicated that 9098 (9.83%) transcripts mapped to 324 KEGG pathways, and the best represented functional categories of pathways were signal transduction and immune system. We have also identified 23 460 microsatellites, 43 560 SNPs as well as 21 456 alternative splicing events in the assembly. Additionally, a total of 24 341 complete open reading frames (ORFs) were detected from the assembly where 1492 ORFs were found to be novel gene loci as these have not been annotated so far in any public database. © 2014 John Wiley & Sons Ltd.

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.

Semantics in support of biodiversity knowledge discovery: an introduction to the biological collections ontology and related ontologies.

PubMed

Walls, Ramona L; Deck, John; Guralnick, Robert; Baskauf, Steve; Beaman, Reed; Blum, Stanley; Bowers, Shawn; Buttigieg, Pier Luigi; Davies, Neil; Endresen, Dag; Gandolfo, Maria Alejandra; Hanner, Robert; Janning, Alyssa; Krishtalka, Leonard; Matsunaga, Andréa; Midford, Peter; Morrison, Norman; Ó Tuama, Éamonn; Schildhauer, Mark; Smith, Barry; Stucky, Brian J; Thomer, Andrea; Wieczorek, John; Whitacre, Jamie; Wooley, John

2014-01-01

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies

PubMed Central

Baskauf, Steve; Blum, Stanley; Bowers, Shawn; Davies, Neil; Endresen, Dag; Gandolfo, Maria Alejandra; Hanner, Robert; Janning, Alyssa; Krishtalka, Leonard; Matsunaga, Andréa; Midford, Peter; Tuama, Éamonn Ó.; Schildhauer, Mark; Smith, Barry; Stucky, Brian J.; Thomer, Andrea; Wieczorek, John; Whitacre, Jamie; Wooley, John

2014-01-01

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers

CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments.

PubMed

Waardenberg, Ashley J; Basset, Samuel D; Bouveret, Romaric; Harvey, Richard P

2015-09-02

Gene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments. However, determining differential GO and pathway enrichment between DNA-binding experiments or using the GO structure to classify experiments has received little attention. Herein, we present a bioinformatics tool, CompGO, for identifying Differentially Enriched Gene Ontologies, called DiEGOs, and pathways, through the use of a z-score derivation of log odds ratios, and visualizing these differences at GO and pathway level. Through public experimental data focused on the cardiac transcription factor NKX2-5, we illustrate the problems associated with comparing GO enrichments between experiments using a simple overlap approach. We have developed an R/Bioconductor package, CompGO, which implements a new statistic normally used in epidemiological studies for performing comparative GO analyses and visualizing comparisons from . BED data containing genomic coordinates as well as gene lists as inputs. We justify the statistic through inclusion of experimental data and compare to the commonly used overlap method. CompGO is freely available as a R/Bioconductor package enabling easy integration into existing pipelines and is available at: http://www.bioconductor.org/packages/release/bioc/html/CompGO.html packages/release/bioc/html/CompGO.html.

Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature

PubMed Central

Müller, Hans-Michael; Kenny, Eimear E

2004-01-01

We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., biological process, etc.). Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism

Gene Ontology annotation of the rice blast fungus, Magnaporthe oryzae

PubMed Central

Meng, Shaowu; Brown, Douglas E; Ebbole, Daniel J; Torto-Alalibo, Trudy; Oh, Yeon Yee; Deng, Jixin; Mitchell, Thomas K; Dean, Ralph A

2009-01-01

Background Magnaporthe oryzae, the causal agent of blast disease of rice, is the most destructive disease of rice worldwide. The genome of this fungal pathogen has been sequenced and an automated annotation has recently been updated to Version 6 . However, a comprehensive manual curation remains to be performed. Gene Ontology (GO) annotation is a valuable means of assigning functional information using standardized vocabulary. We report an overview of the GO annotation for Version 5 of M. oryzae genome assembly. Methods A similarity-based (i.e., computational) GO annotation with manual review was conducted, which was then integrated with a literature-based GO annotation with computational assistance. For similarity-based GO annotation a stringent reciprocal best hits method was used to identify similarity between predicted proteins of M. oryzae and GO proteins from multiple organisms with published associations to GO terms. Significant alignment pairs were manually reviewed. Functional assignments were further cross-validated with manually reviewed data, conserved domains, or data determined by wet lab experiments. Additionally, biological appropriateness of the functional assignments was manually checked. Results In total, 6,286 proteins received GO term assignment via the homology-based annotation, including 2,870 hypothetical proteins. Literature-based experimental evidence, such as microarray, MPSS, T-DNA insertion mutation, or gene knockout mutation, resulted in 2,810 proteins being annotated with GO terms. Of these, 1,673 proteins were annotated with new terms developed for Plant-Associated Microbe Gene Ontology (PAMGO). In addition, 67 experiment-determined secreted proteins were annotated with PAMGO terms. Integration of the two data sets resulted in 7,412 proteins (57%) being annotated with 1,957 distinct and specific GO terms. Unannotated proteins were assigned to the 3 root terms. The Version 5 GO annotation is publically queryable via the GO site

Novel gene sets improve set-level classification of prokaryotic gene expression data.

PubMed

Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

PubMed

Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

2015-01-01

Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

Applications of Ontology Design Patterns in Biomedical Ontologies

PubMed Central

Mortensen, Jonathan M.; Horridge, Matthew; Musen, Mark A.; Noy, Natalya F.

2012-01-01

Ontology design patterns (ODPs) are a proposed solution to facilitate ontology development, and to help users avoid some of the most frequent modeling mistakes. ODPs originate from similar approaches in software engineering, where software design patterns have become a critical aspect of software development. There is little empirical evidence for ODP prevalence or effectiveness thus far. In this work, we determine the use and applicability of ODPs in a case study of biomedical ontologies. We encoded ontology design patterns from two ODP catalogs. We then searched for these patterns in a set of eight ontologies. We found five patterns of the 69 patterns. Two of the eight ontologies contained these patterns. While ontology design patterns provide a vehicle for capturing formally reoccurring models and best practices in ontology design, we show that today their use in a case study of widely used biomedical ontologies is limited. PMID:23304337

Transcriptome analysis of woodland strawberry (Fragaria vesca) response to the infection by Strawberry vein banding virus (SVBV).

PubMed

Chen, Jing; Zhang, Hanping; Feng, Mingfeng; Zuo, Dengpan; Hu, Yahui; Jiang, Tong

2016-07-13

Woodland strawberry (Fragaria vesca) infected with Strawberry vein banding virus (SVBV) exhibits chlorotic symptoms along the leaf veins. However, little is known about the molecular mechanism of strawberry disease caused by SVBV. We performed the next-generation sequencing (RNA-Seq) study to identify gene expression changes induced by SVBV in woodland strawberry using mock-inoculated plants as a control. Using RNA-Seq, we have identified 36,850 unigenes, of which 517 were differentially expressed in the virus-infected plants (DEGs). The unigenes were annotated and classified with Gene Ontology (GO), Clusters of Orthologous Group (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The KEGG pathway analysis of these genes suggested that strawberry disease caused by SVBV may affect multiple processes including pigment metabolism, photosynthesis and plant-pathogen interactions. Our research provides comprehensive transcriptome information regarding SVBV infection in strawberry.

Margin based ontology sparse vector learning algorithm and applied in biology science.

PubMed

Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

2017-01-01

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data.

PubMed

Tang, Hongwei; Wei, Peng; Duell, Eric J; Risch, Harvey A; Olson, Sara H; Bueno-de-Mesquita, H Bas; Gallinger, Steven; Holly, Elizabeth A; Petersen, Gloria; Bracci, Paige M; McWilliams, Robert R; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolph; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

2014-05-01

Cigarette smoking is the best established modifiable risk factor for pancreatic cancer. Genetic factors that underlie smoking-related pancreatic cancer have previously not been examined at the genome-wide level. Taking advantage of the existing Genome-wide association study (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study in 2028 cases and 2109 controls to examine gene-smoking interactions at pathway/gene/single nucleotide polymorphism (SNP) level. Using the likelihood ratio test nested in logistic regression models and ingenuity pathway analysis (IPA), we examined 172 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 manually curated gene sets, 3 nicotine dependency gene ontology pathways, 17 912 genes and 468 114 SNPs. None of the individual pathway/gene/SNP showed significant interaction with smoking after adjusting for multiple comparisons. Six KEGG pathways showed nominal interactions (P < 0.05) with smoking, and the top two are the pancreatic secretion and salivary secretion pathways (major contributing genes: RAB8A, PLCB and CTRB1). Nine genes, i.e. ZBED2, EXO1, PSG2, SLC36A1, CLSTN1, MTHFSD, FAT2, IL10RB and ATXN2 had P interaction < 0.0005. Five intergenic region SNPs and two SNPs of the EVC and KCNIP4 genes had P interaction < 0.00003. In IPA analysis of genes with nominal interactions with smoking, axonal guidance signaling $$\\left(P=2.12\\times 1{0}^{-7}\\right)$$ and α-adrenergic signaling $$\\left(P=2.52\\times 1{0}^{-5}\\right)$$ genes were significantly overrepresented canonical pathways. Genes contributing to the axon guidance signaling pathway included the SLIT/ROBO signaling genes that were frequently altered in pancreatic cancer. These observations need to be confirmed in additional data set. Once confirmed, it will open a new avenue to unveiling the etiology of smoking-associated pancreatic cancer.

Identification of protein features encoded by alternative exons using Exon Ontology.

PubMed

Tranchevent, Léon-Charles; Aubé, Fabien; Dulaurier, Louis; Benoit-Pilven, Clara; Rey, Amandine; Poret, Arnaud; Chautard, Emilie; Mortada, Hussein; Desmet, François-Olivier; Chakrama, Fatima Zahra; Moreno-Garcia, Maira Alejandra; Goillot, Evelyne; Janczarski, Stéphane; Mortreux, Franck; Bourgeois, Cyril F; Auboeuf, Didier

2017-06-01

Transcriptomic genome-wide analyses demonstrate massive variation of alternative splicing in many physiological and pathological situations. One major challenge is now to establish the biological contribution of alternative splicing variation in physiological- or pathological-associated cellular phenotypes. Toward this end, we developed a computational approach, named "Exon Ontology," based on terms corresponding to well-characterized protein features organized in an ontology tree. Exon Ontology is conceptually similar to Gene Ontology-based approaches but focuses on exon-encoded protein features instead of gene level functional annotations. Exon Ontology describes the protein features encoded by a selected list of exons and looks for potential Exon Ontology term enrichment. By applying this strategy to exons that are differentially spliced between epithelial and mesenchymal cells and after extensive experimental validation, we demonstrate that Exon Ontology provides support to discover specific protein features regulated by alternative splicing. We also show that Exon Ontology helps to unravel biological processes that depend on suites of coregulated alternative exons, as we uncovered a role of epithelial cell-enriched splicing factors in the AKT signaling pathway and of mesenchymal cell-enriched splicing factors in driving splicing events impacting on autophagy. Freely available on the web, Exon Ontology is the first computational resource that allows getting a quick insight into the protein features encoded by alternative exons and investigating whether coregulated exons contain the same biological information. © 2017 Tranchevent et al.; Published by Cold Spring Harbor Laboratory Press.

Identifying key genes, pathways and screening therapeutic agents for manganese-induced Alzheimer disease using bioinformatics analysis.

PubMed

Ling, JunJun; Yang, Shengyou; Huang, Yi; Wei, Dongfeng; Cheng, Weidong

2018-06-01

Alzheimer disease (AD) is a progressive neurodegenerative disease, the etiology of which remains largely unknown. Accumulating evidence indicates that elevated manganese (Mn) in brain exerts toxic effects on neurons and contributes to AD development. Thus, we aimed to explore the gene and pathway variations through analysis of high through-put data in this process.To screen the differentially expressed genes (DEGs) that may play critical roles in Mn-induced AD, public microarray data regarding Mn-treated neurocytes versus controls (GSE70845), and AD versus controls (GSE48350), were downloaded and the DEGs were screened out, respectively. The intersection of the DEGs of each datasets was obtained by using Venn analysis. Then, gene ontology (GO) function analysis and KEGG pathway analysis were carried out. For screening hub genes, protein-protein interaction network was constructed. At last, DEGs were analyzed in Connectivity Map (CMAP) for identification of small molecules that overcome Mn-induced neurotoxicity or AD development.The intersection of the DEGs obtained 140 upregulated and 267 downregulated genes. The top 5 items of biological processes of GO analysis were taxis, chemotaxis, cell-cell signaling, regulation of cellular physiological process, and response to wounding. The top 5 items of KEGG pathway analysis were cytokine-cytokine receptor interaction, apoptosis, oxidative phosphorylation, Toll-like receptor signaling pathway, and insulin signaling pathway. Afterwards, several hub genes such as INSR, VEGFA, PRKACB, DLG4, and BCL2 that might play key roles in Mn-induced AD were further screened out. Interestingly, tyrphostin AG-825, an inhibitor of tyrosine phosphorylation, was predicted to be a potential agent for overcoming Mn-induced neurotoxicity or AD development.The present study provided a novel insight into the molecular mechanisms of Mn-induced neurotoxicity or AD development and screened out several small molecular candidates that might be

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

PubMed

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2012-01-01

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

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Identification of the Key Genes and Pathways in Esophageal Carcinoma.

PubMed

Su, Peng; Wen, Shiwang; Zhang, Yuefeng; Li, Yong; Xu, Yanzhao; Zhu, Yonggang; Lv, Huilai; Zhang, Fan; Wang, Mingbo; Tian, Ziqiang

2016-01-01

Objective . Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it. Methods . 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC. Results . A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis. Conclusion . The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.

Identification of Regulatory Genes Implicated in Continuous Flowering of Longan (Dimocarpus longan L.)

PubMed Central

Jia, Tianqi; Wei, Danfeng; Meng, Shan; Allan, Andrew C.; Zeng, Lihui

2014-01-01

Longan (Dimocarpus longan L.) is a tropical/subtropical fruit tree of significant economic importance in Southeast Asia. However, a lack of transcriptomic and genomic information hinders research on longan traits, such as the control of flowering. In this study, high-throughput RNA sequencing (RNA-Seq) was used to investigate differentially expressed genes between a unique longan cultivar ‘Sijimi’(S) which flowers throughout the year and a more typical cultivar ‘Lidongben’(L) which flowers only once in the season, with the aim of identifying candidate genes associated with continuous flowering. 36,527 and 40,982 unigenes were obtained by de novo assembly of the clean reads from cDNA libraries of L and S cultivars. Additionally 40,513 unigenes were assembled from combined reads of these libraries. A total of 32,475 unigenes were annotated by BLAST search to NCBI non-redundant protein (NR), Swiss-Prot, Clusters of Orthologous Groups (COGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Of these, almost fifteen thousand unigenes were identified as significantly differentially expressed genes (DEGs) by using Reads Per kb per Million reads (RPKM) method. A total of 6,415 DEGs were mapped to 128 KEGG pathways, and 8,743 DEGs were assigned to 54 Gene Ontology categories. After blasting the DEGs to public sequence databases, 539 potential flowering-related DEGs were identified. In addition, 107 flowering-time genes were identified in longan, their expression levels between two longan samples were compared by RPKM method, of which the expression levels of 15 were confirmed by real-time quantitative PCR. Our results suggest longan homologues of SHORT VEGETATIVE PHASE (SVP), GIGANTEA (GI), F-BOX 1 (FKF1) and EARLY FLOWERING 4 (ELF4) may be involved this flowering trait and ELF4 may be a key gene. The identification of candidate genes related to continuous flowering will provide new insight into the molecular process of regulating flowering time in woody

PDON: Parkinson's disease ontology for representation and modeling of the Parkinson's disease knowledge domain.

PubMed

Younesi, Erfan; Malhotra, Ashutosh; Gündel, Michaela; Scordis, Phil; Kodamullil, Alpha Tom; Page, Matt; Müller, Bernd; Springstubbe, Stephan; Wüllner, Ullrich; Scheller, Dieter; Hofmann-Apitius, Martin

2015-09-22

Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology. The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1

Semantic similarity between ontologies at different scales

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

Zhang, Qingpeng; Haglin, David J.

In the past decade, existing and new knowledge and datasets has been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea viamore » studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three Gene Ontology slims (Plant, Yeast, and Candida, among which the latter two belong to the same kingdom—Fungi) using four popular measures commonly applied to biomedical ontologies (Resnik, Lin, Jiang-Conrath, and SimRel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performance of Jiang-Conrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by (a) consistently showing that Yeast and Candida are more similar (as compared to Plant) at different scales, and (b) small deviations of the similarity values after excluding a majority of nodes from several lower scales. This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.« less

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

Transcriptome analysis of nitric oxide-responsive genes in upland cotton (Gossypium hirsutum).

PubMed

Huang, Juan; Wei, Hengling; Li, Libei; Yu, Shuxun

2018-01-01

Nitric oxide (NO) is an important signaling molecule with diverse physiological functions in plants. It is therefore important to characterize the downstream genes and signal transduction networks modulated by NO. Here, we identified 1,932 differentially expressed genes (DEGs) responding to NO in upland cotton using high throughput tag sequencing. The results of quantitative real-time polymerase chain reaction (qRT-PCR) analysis of 25 DEGs showed good consistency. Gene Ontology (GO) and KEGG pathway were analyzed to gain a better understanding of these DEGs. We identified 157 DEGs belonging to 36 transcription factor (TF) families and 72 DEGs related to eight plant hormones, among which several TF families and hormones were involved in stress responses. Hydrogen peroxide and malondialdehyde (MDA) contents were increased, as well related genes after treatment with sodium nitroprusside (SNP) (an NO donor), suggesting a role for NO in the plant stress response. Finally, we compared of the current and previous data indicating a massive number of NO-responsive genes at the large-scale transcriptome level. This study evaluated the landscape of NO-responsive genes in cotton and identified the involvement of NO in the stress response. Some of the identified DEGs represent good candidates for further functional analysis in cotton.

The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database.

PubMed

Hayman, G Thomas; Laulederkind, Stanley J F; Smith, Jennifer R; Wang, Shur-Jen; Petri, Victoria; Nigam, Rajni; Tutaj, Marek; De Pons, Jeff; Dwinell, Melinda R; Shimoyama, Mary

2016-01-01

The Rat Genome Database (RGD;http://rgd.mcw.edu/) provides critical datasets and software tools to a diverse community of rat and non-rat researchers worldwide. To meet the needs of the many users whose research is disease oriented, RGD has created a series of Disease Portals and has prioritized its curation efforts on the datasets important to understanding the mechanisms of various diseases. Gene-disease relationships for three species, rat, human and mouse, are annotated to capture biomarkers, genetic associations, molecular mechanisms and therapeutic targets. To generate gene-disease annotations more effectively and in greater detail, RGD initially adopted the MEDIC disease vocabulary from the Comparative Toxicogenomics Database and adapted it for use by expanding this framework with the addition of over 1000 terms to create the RGD Disease Ontology (RDO). The RDO provides the foundation for, at present, 10 comprehensive disease area-related dataset and analysis platforms at RGD, the Disease Portals. Two major disease areas are the focus of data acquisition and curation efforts each year, leading to the release of the related Disease Portals. Collaborative efforts to realize a more robust disease ontology are underway. Database URL:http://rgd.mcw.edu. © The Author(s) 2016. Published by Oxford University Press.

Evaluation of genome-wide association study results through development of ontology fingerprints

PubMed Central

Tsoi, Lam C.; Boehnke, Michael; Klein, Richard L.; Zheng, W. Jim

2009-01-01

Motivation: Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important. Results: We describe an approach to characterize genes or biological concepts (phenotypes, pathways, diseases, etc.) by ontology fingerprint—the set of Gene Ontology (GO) terms that are overrepresented among the PubMed abstracts discussing the gene or biological concept together with the enrichment p-value of these terms generated from a hypergeometric enrichment test. We then quantify the relevance of genes to the trait from a GWA study by calculating similarity scores between their ontology fingerprints using enrichment p-values. We validate this approach by correctly identifying corresponding genes for biological pathways with a 90% average area under the ROC curve (AUC). We applied this approach to rank genes identified through a GWA study that are associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, lipoprotein lipase (LPL) and cholesterol ester transfer protein, plasma for high-density lipoprotein; low-density lipoprotein receptor, APOE and APOB for low-density lipoprotein; and LPL, APOA1 and APOB for triglyceride. In addition, we identified genes relevant to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation. Contact: zhengw@musc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19349285

A UML profile for the OBO relation ontology

PubMed Central

2012-01-01

Background Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain. Results We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO). Conclusions The use of an established and well-known graphical language in the development of biomedical ontologies provides a more

Defining functional distance using manifold embeddings of gene ontology annotations

PubMed Central

Lerman, Gilad; Shakhnovich, Boris E.

2007-01-01

Although rigorous measures of similarity for sequence and structure are now well established, the problem of defining functional relationships has been particularly daunting. Here, we present several manifold embedding techniques to compute distances between Gene Ontology (GO) functional annotations and consequently estimate functional distances between protein domains. To evaluate accuracy, we correlate the functional distance to the well established measures of sequence, structural, and phylogenetic similarities. Finally, we show that manual classification of structures into folds and superfamilies is mirrored by proximity in the newly defined function space. We show how functional distances place structure–function relationships in biological context resulting in insight into divergent and convergent evolution. The methods and results in this paper can be readily generalized and applied to a wide array of biologically relevant investigations, such as accuracy of annotation transference, the relationship between sequence, structure, and function, or coherence of expression modules. PMID:17595300

Comparative Digital Gene Expression Analysis of Tissue-Cultured Plantlets of Highly Resistant and Susceptible Banana Cultivars in Response to Fusarium oxysporum

PubMed Central

Niu, Yuqing; Hu, Bei; Li, Xiaoquan; Chen, Houbin; Šamaj, Jozef; Xu, Chunxiang

2018-01-01

Banana Fusarium wilt caused by Fusarium oxysporum f. sp. cubense (Foc) is one of the most destructive soil-borne diseases. In this study, young tissue-cultured plantlets of banana (Musa spp. AAA) cultivars differing in Foc susceptibility were used to reveal their differential responses to this pathogen using digital gene expression (DGE). Data were evaluated by various bioinformatic tools (Venn diagrams, gene ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses) and immunofluorescence labelling method to support the identification of gene candidates determining the resistance of banana against Foc. Interestingly, we have identified MaWRKY50 as an important gene involved in both constitutive and induced resistance. We also identified new genes involved in the resistance of banana to Foc, including several other transcription factors (TFs), pathogenesis-related (PR) genes and some genes related to the plant cell wall biosynthesis or degradation (e.g., pectinesterases, β-glucosidases, xyloglucan endotransglucosylase/hydrolase and endoglucanase). The resistant banana cultivar shows activation of PR-3 and PR-4 genes as well as formation of different constitutive cell barriers to restrict spreading of the pathogen. These data suggest new mechanisms of banana resistance to Foc. PMID:29364855

OntologyWidget - a reusable, embeddable widget for easily locating ontology terms.

PubMed

Beauheim, Catherine C; Wymore, Farrell; Nitzberg, Michael; Zachariah, Zachariah K; Jin, Heng; Skene, J H Pate; Ball, Catherine A; Sherlock, Gavin

2007-09-13

Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form. We have produced a tool, OntologyWidget, which allows users to rapidly search for and browse ontology terms. OntologyWidget can easily be embedded in other web-based applications. OntologyWidget is written using AJAX (Asynchronous JavaScript and XML) and has two related elements. The first is a dynamic auto-complete ontology search feature. As a user enters characters into the search box, the appropriate ontology is queried remotely for terms that match the typed-in text, and the query results populate a drop-down list with all potential matches. Upon selection of a term from the list, the user can locate this term within a generic and dynamic ontology browser, which comprises the second element of the tool. The ontology browser shows the paths from a selected term to the root as well as parent/child tree hierarchies. We have implemented web services at the Stanford Microarray Database (SMD), which provide the OntologyWidget with access to over 40 ontologies from the Open Biological Ontology (OBO) website 1. Each ontology is updated weekly. Adopters of the OntologyWidget can either use SMD's web services, or elect to rely on their own. Deploying the OntologyWidget can be accomplished in three simple steps: (1) install Apache Tomcat 2 on one's web server, (2) download and install the OntologyWidget servlet stub that provides access to the SMD ontology web services, and (3) create an html (HyperText Markup Language) file that refers to the OntologyWidget using a simple, well-defined format. We have developed OntologyWidget, an easy-to-use ontology search and display

The Plant Ontology as a Tool for Comparative Plant Anatomy and Genomic Analyses

PubMed Central

Cooper, Laurel; Walls, Ramona L.; Elser, Justin; Gandolfo, Maria A.; Stevenson, Dennis W.; Smith, Barry; Preece, Justin; Athreya, Balaji; Mungall, Christopher J.; Rensing, Stefan; Hiss, Manuel; Lang, Daniel; Reski, Ralf; Berardini, Tanya Z.; Li, Donghui; Huala, Eva; Schaeffer, Mary; Menda, Naama; Arnaud, Elizabeth; Shrestha, Rosemary; Yamazaki, Yukiko; Jaiswal, Pankaj

2013-01-01

The Plant Ontology (PO; http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary (‘ontology’) of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to >110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs. PMID:23220694

Proceedings of a Sickle Cell Disease Ontology workshop - Towards the first comprehensive ontology for Sickle Cell Disease.

PubMed

Mulder, Nicola; Nembaware, Victoria; Adekile, Adekunle; Anie, Kofi A; Inusa, Baba; Brown, Biobele; Campbell, Andrew; Chinenere, Furahini; Chunda-Liyoka, Catherine; Derebail, Vimal K; Geard, Amy; Ghedira, Kais; Hamilton, Carol M; Hanchard, Neil A; Haendel, Melissa; Huggins, Wayne; Ibrahim, Muntaser; Jupp, Simon; Kamga, Karen Kengne; Knight-Madden, Jennifer; Lopez-Sall, Philomène; Mbiyavanga, Mamana; Munube, Deogratias; Nirenberg, Damian; Nnodu, Obiageli; Ofori-Acquah, Solomon Fiifi; Ohene-Frempong, Kwaku; Opap, Kenneth Babu; Panji, Sumir; Park, Miriam; Pule, Gift; Royal, Charmaine; Sangeda, Raphael; Tayo, Bamidele; Treadwell, Marsha; Tshilolo, Léon; Wonkam, Ambroise

2016-06-01

Sickle cell disease (SCD) is a debilitating single gene disorder caused by a single point mutation that results in physical deformation (i.e. sickling) of erythrocytes at reduced oxygen tensions. Up to 75% of SCD in newborns world-wide occurs in sub-Saharan Africa, where neonatal and childhood mortality from sickle cell related complications is high. While SCD research across the globe is tackling the disease on multiple fronts, advances have yet to significantly impact on the health and quality of life of SCD patients, due to lack of coordination of these disparate efforts. Ensuring data across studies is directly comparable through standardization is a necessary step towards realizing this goal. Such a standardization requires the development and implementation of a disease-specific ontology for SCD that is applicable globally. Ontology development is best achieved by bringing together experts in the domain to contribute their knowledge. The SCD community and H3ABioNet members joined forces at a recent SCD Ontology workshop to develop an ontology covering aspects of SCD under the classes: phenotype, diagnostics, therapeutics, quality of life, disease modifiers and disease stage. The aim of the workshop was for participants to contribute their expertise to development of the structure and contents of the SCD ontology. Here we describe the proceedings of the Sickle Cell Disease Ontology Workshop held in Cape Town South Africa in February 2016 and its outcomes. The objective of the workshop was to bring together experts in SCD from around the world to contribute their expertise to the development of various aspects of the SCD ontology.

Mining Rare Associations between Biological Ontologies

PubMed Central

Benites, Fernando; Simon, Svenja; Sapozhnikova, Elena

2014-01-01

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations. PMID:24404165

Mining rare associations between biological ontologies.

PubMed

Benites, Fernando; Simon, Svenja; Sapozhnikova, Elena

2014-01-01

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations.

KEGGParser: parsing and editing KEGG pathway maps in Matlab.

PubMed

Arakelyan, Arsen; Nersisyan, Lilit

2013-02-15

KEGG pathway database is a collection of manually drawn pathway maps accompanied with KGML format files intended for use in automatic analysis. KGML files, however, do not contain the required information for complete reproduction of all the events indicated in the static image of a pathway map. Several parsers and editors of KEGG pathways exist for processing KGML files. We introduce KEGGParser-a MATLAB based tool for KEGG pathway parsing, semiautomatic fixing, editing, visualization and analysis in MATLAB environment. It also works with Scilab. The source code is available at http://www.mathworks.com/matlabcentral/fileexchange/37561.

Toward Understanding the Genetic Basis of Yak Ovary Reproduction: A Characterization and Comparative Analyses of Estrus Ovary Transcriptiome in Yak and Cattle.

PubMed

Lan, Daoliang; Xiong, Xianrong; Huang, Cai; Mipam, Tserang Donko; Li, Jian

2016-01-01

Yaks (Bos grunniens) are endemic species that can adapt well to thin air, cold temperatures, and high altitude. These species can survive in harsh plateau environments and are major source of animal production for local residents, being an important breed in the Qinghai-Tibet Plateau. However, compared with ordinary cattle that live in the plains, yaks generally have lower fertility. Investigating the basic physiological molecular features of yak ovary and identifying the biological events underlying the differences between the ovaries of yak and plain cattle is necessary to understand the specificity of yak reproduction. Therefore, RNA-seq technology was applied to analyze transcriptome data comparatively between the yak and plain cattle estrous ovaries. After deep sequencing, 3,653,032 clean reads with a total of 4,828,772,880 base pairs were obtained from yak ovary library. Alignment analysis showed that 16992 yak genes mapped to the yak genome, among which, 12,731 and 14,631 genes were assigned to Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, comparison of yak and cattle ovary transcriptome data revealed that 1307 genes were significantly and differentially expressed between the two libraries, wherein 661 genes were upregulated and 646 genes were downregulated in yak ovary. Functional analysis showed that the differentially expressed genes were involved in various Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO annotations indicated that the genes related to "cell adhesion," "hormonal" biological processes, and "calcium ion binding," "cation transmembrane transport" molecular events were significantly active. KEGG pathway analysis showed that the "complement and coagulation cascade" pathway was the most enriched in yak ovary transcriptome data, followed by the "cytochrome P450" related and "ECM-receptor interaction" pathways. Moreover, several novel pathways

An Ontology-Based GIS for Genomic Data Management of Rumen Microbes.

PubMed

Jelokhani-Niaraki, Saber; Tahmoorespur, Mojtaba; Minuchehr, Zarrin; Nassiri, Mohammad Reza

2015-03-01

During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and Protégé as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data.

[Study on action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis based on techniques of gene expression profile and molecular fingerprint].

PubMed

Zhou, Wei; Song, Xiang-gang; Chen, Chao; Wang, Shu-mei; Liang, Sheng-wang

2015-08-01

Action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis were discussed based on gene expression profile and molecular fingerprint in this paper. First, gene expression profiles of atherosclerotic carotid artery tissues and histologically normal tissues in human body were collected, and were screened using significance analysis of microarray (SAM) to screen out differential gene expressions; then differential genes were analyzed by Gene Ontology (GO) analysis and KEGG pathway analysis; to avoid some genes with non-outstanding differential expression but biologically importance, Gene Set Enrichment Analysis (GSEA) were performed, and 7 chemical ingredients with higher negative enrichment score were obtained by Cmap method, implying that they could reversely regulate the gene expression profiles of pathological tissues; and last, based on the hypotheses that similar structures have similar activities, 336 ingredients of compound Danshen dripping pills were compared with 7 drug molecules in 2D molecular fingerprints method. The results showed that 147 differential genes including 60 up-regulated genes and 87 down regulated genes were screened out by SAM. And in GO analysis, Biological Process ( BP) is mainly concerned with biological adhesion, response to wounding and inflammatory response; Cellular Component (CC) is mainly concerned with extracellular region, extracellular space and plasma membrane; while Molecular Function (MF) is mainly concerned with antigen binding, metalloendopeptidase activity and peptide binding. KEGG pathway analysis is mainly concerned with JAK-STAT, RIG-I like receptor and PPAR signaling pathway. There were 10 compounds, such as hexadecane, with Tanimoto coefficients greater than 0.85, which implied that they may be the active ingredients (AIs) of compound Danshen dripping pills in treatment of carotid atherosclerosis (CAs). The present method can be applied to the research on material

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.

A high-resolution anatomical ontology of the developing murine genitourinary tract

PubMed Central

Little, Melissa H.; Brennan, Jane; Georgas, Kylie; Davies, Jamie A.; Davidson, Duncan R.; Baldock, Richard A.; Beverdam, Annemiek; Bertram, John F.; Capel, Blanche; Chiu, Han Sheng; Clements, Dave; Cullen-McEwen, Luise; Fleming, Jean; Gilbert, Thierry; Houghton, Derek; Kaufman, Matt H.; Kleymenova, Elena; Koopman, Peter A.; Lewis, Alfor G.; McMahon, Andrew P.; Mendelsohn, Cathy L.; Mitchell, Eleanor K.; Rumballe, Bree A.; Sweeney, Derina E.; Valerius, M. Todd; Yamada, Gen; Yang, Yiya; Yu., Jing

2007-01-01

Cataloguing gene expression during development of the genitourinary tract will increase our understanding not only of this process but also of congenital defects and disease affecting this organ system. We have developed a high-resolution ontology with which to describe the subcompartments of the developing murine genitourinary tract. This ontology incorporates what can be defined histologically and begins to encompass other structures and cell types already identified at the molecular level. The ontology is being used to annotate in situ hybridisation data generated as part of the Genitourinary Development Molecular Anatomy Project (GUDMAP), a publicly available data resource on gene and protein expression during genitourinary development. The GUDMAP ontology encompasses Theiler stage (TS) 17 to 27 of development as well as the sexually mature adult. It has been written as a partonomic, text-based, hierarchical ontology that, for the embryological stages, has been developed as a high-resolution expansion of the existing Edinburgh Mouse Atlas Project (EMAP) ontology. It also includes group terms for well-characterised structural and/or functional units comprising several sub-structures, such as the nephron and juxtaglomerular complex. Each term has been assigned a unique identification number. Synonyms have been used to improve the success of query searching and maintain wherever possible existing EMAP terms relating to this organ system. We describe here the principles and structure of the ontology and provide representative diagrammatic, histological, and whole mount and section RNA in situ hybridisation images to clarify the terms used within the ontology. Visual examples of how terms appear in different specimen types are also provided. PMID:17452023

Multiomics in Grape Berry Skin Revealed Specific Induction of the Stilbene Synthetic Pathway by Ultraviolet-C Irradiation1

PubMed Central

Suzuki, Mami; Nakabayashi, Ryo; Ogata, Yoshiyuki; Sakurai, Nozomu; Tokimatsu, Toshiaki; Goto, Susumu; Suzuki, Makoto; Jasinski, Michal; Martinoia, Enrico; Otagaki, Shungo; Matsumoto, Shogo; Saito, Kazuki; Shiratake, Katsuhiro

2015-01-01

Grape (Vitis vinifera) accumulates various polyphenolic compounds, which protect against environmental stresses, including ultraviolet-C (UV-C) light and pathogens. In this study, we looked at the transcriptome and metabolome in grape berry skin after UV-C irradiation, which demonstrated the effectiveness of omics approaches to clarify important traits of grape. We performed transcriptome analysis using a genome-wide microarray, which revealed 238 genes up-regulated more than 5-fold by UV-C light. Enrichment analysis of Gene Ontology terms showed that genes encoding stilbene synthase, a key enzyme for resveratrol synthesis, were enriched in the up-regulated genes. We performed metabolome analysis using liquid chromatography-quadrupole time-of-flight mass spectrometry, and 2,012 metabolite peaks, including unidentified peaks, were detected. Principal component analysis using the peaks showed that only one metabolite peak, identified as resveratrol, was highly induced by UV-C light. We updated the metabolic pathway map of grape in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and in the KaPPA-View 4 KEGG system, then projected the transcriptome and metabolome data on a metabolic pathway map. The map showed specific induction of the resveratrol synthetic pathway by UV-C light. Our results showed that multiomics is a powerful tool to elucidate the accumulation mechanisms of secondary metabolites, and updated systems, such as KEGG and KaPPA-View 4 KEGG for grape, can support such studies. PMID:25761715

Multiomics in grape berry skin revealed specific induction of the stilbene synthetic pathway by ultraviolet-C irradiation.

PubMed

Suzuki, Mami; Nakabayashi, Ryo; Ogata, Yoshiyuki; Sakurai, Nozomu; Tokimatsu, Toshiaki; Goto, Susumu; Suzuki, Makoto; Jasinski, Michal; Martinoia, Enrico; Otagaki, Shungo; Matsumoto, Shogo; Saito, Kazuki; Shiratake, Katsuhiro

2015-05-01

Grape (Vitis vinifera) accumulates various polyphenolic compounds, which protect against environmental stresses, including ultraviolet-C (UV-C) light and pathogens. In this study, we looked at the transcriptome and metabolome in grape berry skin after UV-C irradiation, which demonstrated the effectiveness of omics approaches to clarify important traits of grape. We performed transcriptome analysis using a genome-wide microarray, which revealed 238 genes up-regulated more than 5-fold by UV-C light. Enrichment analysis of Gene Ontology terms showed that genes encoding stilbene synthase, a key enzyme for resveratrol synthesis, were enriched in the up-regulated genes. We performed metabolome analysis using liquid chromatography-quadrupole time-of-flight mass spectrometry, and 2,012 metabolite peaks, including unidentified peaks, were detected. Principal component analysis using the peaks showed that only one metabolite peak, identified as resveratrol, was highly induced by UV-C light. We updated the metabolic pathway map of grape in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and in the KaPPA-View 4 KEGG system, then projected the transcriptome and metabolome data on a metabolic pathway map. The map showed specific induction of the resveratrol synthetic pathway by UV-C light. Our results showed that multiomics is a powerful tool to elucidate the accumulation mechanisms of secondary metabolites, and updated systems, such as KEGG and KaPPA-View 4 KEGG for grape, can support such studies. © 2015 American Society of Plant Biologists. All Rights Reserved.

OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data.

PubMed

Huang, Jingshan; Gutierrez, Fernando; Strachan, Harrison J; Dou, Dejing; Huang, Weili; Smith, Barry; Blake, Judith A; Eilbeck, Karen; Natale, Darren A; Lin, Yu; Wu, Bin; Silva, Nisansa de; Wang, Xiaowei; Liu, Zixing; Borchert, Glen M; Tan, Ming; Ruttenberg, Alan

2016-01-01

As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is therefore critical to identify, analyze, and cross-reference miRNA-target interactions to better explore and delineate miRNA functions. Semantic technologies can help in this regard. We previously developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), whose goal was to serve as a foundation for semantic annotation, data integration, and semantic search in the miRNA field. In this paper we describe our continuing effort to develop the OMIT, and demonstrate its use within a semantic search system, OmniSearch, designed to facilitate knowledge capture of miRNA-target interaction data. Important changes in the current version OMIT are summarized as: (1) following a modularized ontology design (with 2559 terms imported from the NCRO ontology); (2) encoding all 1884 human miRNAs (vs. 300 in previous versions); and (3) setting up a GitHub project site along with an issue tracker for more effective community collaboration on the ontology development. The OMIT ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/omit.owl. The OmniSearch system is also free and open to all users, accessible at: http://omnisearch.soc.southalabama.edu/index.php/Software.

Age distribution patterns of human gene families: divergent for Gene Ontology categories and concordant between different subcellular localizations.

PubMed

Liu, Gangbiao; Zou, Yangyun; Cheng, Qiqun; Zeng, Yanwu; Gu, Xun; Su, Zhixi

2014-04-01

The age distribution of gene duplication events within the human genome exhibits two waves of duplications along with an ancient component. However, because of functional constraint differences, genes in different functional categories might show dissimilar retention patterns after duplication. It is known that genes in some functional categories are highly duplicated in the early stage of vertebrate evolution. However, the correlations of the age distribution pattern of gene duplication between the different functional categories are still unknown. To investigate this issue, we developed a robust pipeline to date the gene duplication events in the human genome. We successfully estimated about three-quarters of the duplication events within the human genome, along with the age distribution pattern in each Gene Ontology (GO) slim category. We found that some GO slim categories show different distribution patterns when compared to the whole genome. Further hierarchical clustering of the GO slim functional categories enabled grouping into two main clusters. We found that human genes located in the duplicated copy number variant regions, whose duplicate genes have not been fixed in the human population, were mainly enriched in the groups with a high proportion of recently duplicated genes. Moreover, we used a phylogenetic tree-based method to date the age of duplications in three signaling-related gene superfamilies: transcription factors, protein kinases and G-protein coupled receptors. These superfamilies were expressed in different subcellular localizations. They showed a similar age distribution as the signaling-related GO slim categories. We also compared the differences between the age distributions of gene duplications in multiple subcellular localizations. We found that the distribution patterns of the major subcellular localizations were similar to that of the whole genome. This study revealed the whole picture of the evolution patterns of gene functional

PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.

PubMed

Kahanda, Indika; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa

2015-01-01

The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

GOClonto: an ontological clustering approach for conceptualizing PubMed abstracts.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Kim, Hong-Gee

2010-02-01

Concurrent with progress in biomedical sciences, an overwhelming of textual knowledge is accumulating in the biomedical literature. PubMed is the most comprehensive database collecting and managing biomedical literature. To help researchers easily understand collections of PubMed abstracts, numerous clustering methods have been proposed to group similar abstracts based on their shared features. However, most of these methods do not explore the semantic relationships among groupings of documents, which could help better illuminate the groupings of PubMed abstracts. To address this issue, we proposed an ontological clustering method called GOClonto for conceptualizing PubMed abstracts. GOClonto uses latent semantic analysis (LSA) and gene ontology (GO) to identify key gene-related concepts and their relationships as well as allocate PubMed abstracts based on these key gene-related concepts. Based on two PubMed abstract collections, the experimental results show that GOClonto is able to identify key gene-related concepts and outperforms the STC (suffix tree clustering) algorithm, the Lingo algorithm, the Fuzzy Ants algorithm, and the clustering based TRS (tolerance rough set) algorithm. Moreover, the two ontologies generated by GOClonto show significant informative conceptual structures.

Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy

PubMed Central

Alexopoulou, Dimitra; Andreopoulos, Bill; Dietze, Heiko; Doms, Andreas; Gandon, Fabien; Hakenberg, Jörg; Khelif, Khaled; Schroeder, Michael; Wächter, Thomas

2009-01-01

Background Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively. Results The 'Closest Sense' method assumes that the ontology defines multiple senses of the term. It computes the shortest path of co-occurring terms in the document to one of these senses. The 'Term Cooc' method defines a log-odds ratio for co-occurring terms including co-occurrences inferred from the ontology structure. The 'MetaData' approach trains a classifier on metadata. It does not require any ontology, but requires training data, which the other methods do not. To evaluate these approaches we defined a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The 'MetaData' approach performed best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The 'Term Cooc' approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The 'Closest Sense' approach achieves on average 80% success rate. Conclusion Metadata is valuable for disambiguation, but requires high quality training data. Closest Sense requires no training, but a large, consistently modelled ontology, which are two opposing conditions. Term Cooc achieves greater 90% success given a consistently

KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats.

PubMed

Wrzodek, Clemens; Dräger, Andreas; Zell, Andreas

2011-08-15

The KEGG PATHWAY database provides a widely used service for metabolic and nonmetabolic pathways. It contains manually drawn pathway maps with information about the genes, reactions and relations contained therein. To store these pathways, KEGG uses KGML, a proprietary XML-format. Parsers and translators are needed to process the pathway maps for usage in other applications and algorithms. We have developed KEGGtranslator, an easy-to-use stand-alone application that can visualize and convert KGML formatted XML-files into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g. MIRIAM annotations) beyond the scope of the KGML document, and amends missing components to fragmentary reactions within the pathway to allow simulations on those. KEGGtranslator is freely available as a Java(™) Web Start application and for download at http://www.cogsys.cs.uni-tuebingen.de/software/KEGGtranslator/. KGML files can be downloaded from within the application. clemens.wrzodek@uni-tuebingen.de Supplementary data are available at Bioinformatics online.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis.

PubMed

Jeong, Hyeri; Kim, Jongwoon; Kim, Youngjun

2017-09-30

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis

PubMed Central

Kim, Jongwoon

2017-01-01

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer. PMID:28973975

OntologyWidget – a reusable, embeddable widget for easily locating ontology terms

PubMed Central

Beauheim, Catherine C; Wymore, Farrell; Nitzberg, Michael; Zachariah, Zachariah K; Jin, Heng; Skene, JH Pate; Ball, Catherine A; Sherlock, Gavin

2007-01-01

Background Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form. Results We have produced a tool, OntologyWidget, which allows users to rapidly search for and browse ontology terms. OntologyWidget can easily be embedded in other web-based applications. OntologyWidget is written using AJAX (Asynchronous JavaScript and XML) and has two related elements. The first is a dynamic auto-complete ontology search feature. As a user enters characters into the search box, the appropriate ontology is queried remotely for terms that match the typed-in text, and the query results populate a drop-down list with all potential matches. Upon selection of a term from the list, the user can locate this term within a generic and dynamic ontology browser, which comprises the second element of the tool. The ontology browser shows the paths from a selected term to the root as well as parent/child tree hierarchies. We have implemented web services at the Stanford Microarray Database (SMD), which provide the OntologyWidget with access to over 40 ontologies from the Open Biological Ontology (OBO) website [1]. Each ontology is updated weekly. Adopters of the OntologyWidget can either use SMD's web services, or elect to rely on their own. Deploying the OntologyWidget can be accomplished in three simple steps: (1) install Apache Tomcat [2] on one's web server, (2) download and install the OntologyWidget servlet stub that provides access to the SMD ontology web services, and (3) create an html (HyperText Markup Language) file that refers to the OntologyWidget using a simple, well-defined format. Conclusion We have developed OntologyWidget, an easy

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

PubMed

Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

2017-03-20

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

Differential gene expression in Schistosoma japonicum schistosomula from Wistar rats and BALB/c mice

PubMed Central

2011-01-01

Background More than 46 species of mammals can be naturally infected with Schistosoma japonicum in the mainland of China. Mice are permissive and may act as the definitive host of the life cycle. In contrast, rats are less susceptible to S. japonicum infection, and are considered to provide an unsuitable micro-environment for parasite growth and development. Since little is known of what effects this micro-environment has on the parasite itself, we have in the present study utilised a S. japonicum oligonucleotide microarray to compare the gene expression differences of 10-day-old schistosomula maintained in Wistar rats with those maintained in BALB/c mice. Results In total 3,468 schistosome genes were found to be differentially expressed, of which the majority (3,335) were down-regulated (≤ 2 fold) and 133 were up-regulated (≥ 2 fold) in schistosomula from Wistar rats compared with those from BALB/c mice. Gene ontology (GO) analysis revealed that of the differentially expressed genes with already established functions or close homology to well characterized genes in another organisms, many are related to important biological functions or molecular processes. Among the genes that were down-regulated in schistosomula from Wistar rats, some were associated with metabolism, signal transduction and development. Of these genes related to metabolic processes, areas including translation, protein and amino acid phosphorylation, proteolysis, oxidoreductase activities, catalytic activities and hydrolase activities, were represented. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis of differential expressed genes indicated that of the 328 genes that had a specific KEGG pathway annotation, 324 were down-regulated and were mainly associated with metabolism, growth, redox pathway, oxidative phosphorylation, the cell cycle, ubiquitin-mediated proteolysis, protein export and the MAPK (mitogen-activated protein kinases) signaling pathway. Conclusions This work

Where to Publish and Find Ontologies? A Survey of Ontology Libraries

PubMed Central

d'Aquin, Mathieu; Noy, Natalya F.

2011-01-01

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries a ects their features, content, and potential exploitation in applications. From reviewing eleven ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address. PMID:22408576

An Ontology-Based GIS for Genomic Data Management of Rumen Microbes

PubMed Central

Jelokhani-Niaraki, Saber; Minuchehr, Zarrin; Nassiri, Mohammad Reza

2015-01-01

During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and Protégé as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data. PMID:25873847

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

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

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

PubMed

Martínez-Romero, Marcos; Jonquet, Clement; O'Connor, Martin J; Graybeal, John; Pazos, Alejandro; Musen, Mark A

2017-06-07

Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability

ontologyX: a suite of R packages for working with ontological data.

PubMed

Greene, Daniel; Richardson, Sylvia; Turro, Ernest

2017-04-01

Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines. ontologyIndex , ontologyPlot and ontologySimilarity are all available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packages/ . Daniel Greene dg333@cam.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

A unified anatomy ontology of the vertebrate skeletal system.

PubMed

Dahdul, Wasila M; Balhoff, James P; Blackburn, David C; Diehl, Alexander D; Haendel, Melissa A; Hall, Brian K; Lapp, Hilmar; Lundberg, John G; Mungall, Christopher J; Ringwald, Martin; Segerdell, Erik; Van Slyke, Ceri E; Vickaryous, Matthew K; Westerfield, Monte; Mabee, Paula M

2012-01-01

The skeleton is of fundamental importance in research in comparative vertebrate morphology, paleontology, biomechanics, developmental biology, and systematics. Motivated by research questions that require computational access to and comparative reasoning across the diverse skeletal phenotypes of vertebrates, we developed a module of anatomical concepts for the skeletal system, the Vertebrate Skeletal Anatomy Ontology (VSAO), to accommodate and unify the existing skeletal terminologies for the species-specific (mouse, the frog Xenopus, zebrafish) and multispecies (teleost, amphibian) vertebrate anatomy ontologies. Previous differences between these terminologies prevented even simple queries across databases pertaining to vertebrate morphology. This module of upper-level and specific skeletal terms currently includes 223 defined terms and 179 synonyms that integrate skeletal cells, tissues, biological processes, organs (skeletal elements such as bones and cartilages), and subdivisions of the skeletal system. The VSAO is designed to integrate with other ontologies, including the Common Anatomy Reference Ontology (CARO), Gene Ontology (GO), Uberon, and Cell Ontology (CL), and it is freely available to the community to be updated with additional terms required for research. Its structure accommodates anatomical variation among vertebrate species in development, structure, and composition. Annotation of diverse vertebrate phenotypes with this ontology will enable novel inquiries across the full spectrum of phenotypic diversity.

A Unified Anatomy Ontology of the Vertebrate Skeletal System

PubMed Central

Dahdul, Wasila M.; Balhoff, James P.; Blackburn, David C.; Diehl, Alexander D.; Haendel, Melissa A.; Hall, Brian K.; Lapp, Hilmar; Lundberg, John G.; Mungall, Christopher J.; Ringwald, Martin; Segerdell, Erik; Van Slyke, Ceri E.; Vickaryous, Matthew K.; Westerfield, Monte; Mabee, Paula M.

2012-01-01

The skeleton is of fundamental importance in research in comparative vertebrate morphology, paleontology, biomechanics, developmental biology, and systematics. Motivated by research questions that require computational access to and comparative reasoning across the diverse skeletal phenotypes of vertebrates, we developed a module of anatomical concepts for the skeletal system, the Vertebrate Skeletal Anatomy Ontology (VSAO), to accommodate and unify the existing skeletal terminologies for the species-specific (mouse, the frog Xenopus, zebrafish) and multispecies (teleost, amphibian) vertebrate anatomy ontologies. Previous differences between these terminologies prevented even simple queries across databases pertaining to vertebrate morphology. This module of upper-level and specific skeletal terms currently includes 223 defined terms and 179 synonyms that integrate skeletal cells, tissues, biological processes, organs (skeletal elements such as bones and cartilages), and subdivisions of the skeletal system. The VSAO is designed to integrate with other ontologies, including the Common Anatomy Reference Ontology (CARO), Gene Ontology (GO), Uberon, and Cell Ontology (CL), and it is freely available to the community to be updated with additional terms required for research. Its structure accommodates anatomical variation among vertebrate species in development, structure, and composition. Annotation of diverse vertebrate phenotypes with this ontology will enable novel inquiries across the full spectrum of phenotypic diversity. PMID:23251424

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

PubMed Central

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

2015-01-01

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

Ontology-based representation and analysis of host-Brucella interactions.

PubMed

Lin, Yu; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Biomedical ontologies are representations of classes of entities in the biomedical domain and how these classes are related in computer- and human-interpretable formats. Ontologies support data standardization and exchange and provide a basis for computer-assisted automated reasoning. IDOBRU is an ontology in the domain of Brucella and brucellosis. Brucella is a Gram-negative intracellular bacterium that causes brucellosis, the most common zoonotic disease in the world. In this study, IDOBRU is used as a platform to model and analyze how the hosts, especially host macrophages, interact with virulent Brucella strains or live attenuated Brucella vaccine strains. Such a study allows us to better integrate and understand intricate Brucella pathogenesis and host immunity mechanisms. Different levels of host-Brucella interactions based on different host cell types and Brucella strains were first defined ontologically. Three important processes of virulent Brucella interacting with host macrophages were represented: Brucella entry into macrophage, intracellular trafficking, and intracellular replication. Two Brucella pathogenesis mechanisms were ontologically represented: Brucella Type IV secretion system that supports intracellular trafficking and replication, and Brucella erythritol metabolism that participates in Brucella intracellular survival and pathogenesis. The host cell death pathway is critical to the outcome of host-Brucella interactions. For better survival and replication, virulent Brucella prevents macrophage cell death. However, live attenuated B. abortus vaccine strain RB51 induces caspase-2-mediated proinflammatory cell death. Brucella-associated cell death processes are represented in IDOBRU. The gene and protein information of 432 manually annotated Brucella virulence factors were represented using the Ontology of Genes and Genomes (OGG) and Protein Ontology (PRO), respectively. Seven inference rules were defined to capture the knowledge of host

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease.

PubMed

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K

2016-11-30

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer's disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer's disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer's disease brains. The biological pathways associated with Alzheimer's disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.

GGDonto ontology as a knowledge-base for genetic diseases and disorders of glycan metabolism and their causative genes.

PubMed

Solovieva, Elena; Shikanai, Toshihide; Fujita, Noriaki; Narimatsu, Hisashi

2018-04-18

Inherited mutations in glyco-related genes can affect the biosynthesis and degradation of glycans and result in severe genetic diseases and disorders. The Glyco-Disease Genes Database (GDGDB), which provides information about these diseases and disorders as well as their causative genes, has been developed by the Research Center for Medical Glycoscience (RCMG) and released in April 2010. GDGDB currently provides information on about 80 genetic diseases and disorders caused by single-gene mutations in glyco-related genes. Many biomedical resources provide information about genetic disorders and genes involved in their pathogenesis, but resources focused on genetic disorders known to be related to glycan metabolism are lacking. With the aim of providing more comprehensive knowledge on genetic diseases and disorders of glycan biosynthesis and degradation, we enriched the content of the GDGDB database and improved the methods for data representation. We developed the Genetic Glyco-Diseases Ontology (GGDonto) and a RDF/SPARQL-based user interface using Semantic Web technologies. In particular, we represented the GGDonto content using Semantic Web languages, such as RDF, RDFS, SKOS, and OWL, and created an interactive user interface based on SPARQL queries. This user interface provides features to browse the hierarchy of the ontology, view detailed information on diseases and related genes, and find relevant background information. Moreover, it provides the ability to filter and search information by faceted and keyword searches. Focused on the molecular etiology, pathogenesis, and clinical manifestations of genetic diseases and disorders of glycan metabolism and developed as a knowledge-base for this scientific field, GGDonto provides comprehensive information on various topics, including links to aid the integration with other scientific resources. The availability and accessibility of this knowledge will help users better understand how genetic defects impact the

Identification of upstream transcription factors (TFs) for expression signature genes in breast cancer.

PubMed

Zang, Hongyan; Li, Ning; Pan, Yuling; Hao, Jingguang

2017-03-01

Breast cancer is a common malignancy among women with a rising incidence. Our intention was to detect transcription factors (TFs) for deeper understanding of the underlying mechanisms of breast cancer. Integrated analysis of gene expression datasets of breast cancer was performed. Then, functional annotation of differentially expressed genes (DEGs) was conducted, including Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Furthermore, TFs were identified and a global transcriptional regulatory network was constructed. Seven publically available GEO datasets were obtained, and a set of 1196 DEGs were identified (460 up-regulated and 736 down-regulated). Functional annotation results showed that cell cycle was the most significantly enriched pathway, which was consistent with the fact that cell cycle is closely related to various tumors. Fifty-three differentially expressed TFs were identified, and the regulatory networks consisted of 817 TF-target interactions between 46 TFs and 602 DEGs in the context of breast cancer. Top 10 TFs covering the most downstream DEGs were SOX10, NFATC2, ZNF354C, ARID3A, BRCA1, FOXO3, GATA3, ZEB1, HOXA5 and EGR1. The transcriptional regulatory networks could enable a better understanding of regulatory mechanisms of breast cancer pathology and provide an opportunity for the development of potential therapy.

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

PubMed Central

Smith, Barry; Ashburner, Michael; Rosse, Cornelius; Bard, Jonathan; Bug, William; Ceusters, Werner; Goldberg, Louis J; Eilbeck, Karen; Ireland, Amelia; Mungall, Christopher J; Leontis, Neocles; Rocca-Serra, Philippe; Ruttenberg, Alan; Sansone, Susanna-Assunta; Scheuermann, Richard H; Shah, Nigam; Whetzel, Patricia L; Lewis, Suzanna

2010-01-01

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved. PMID:17989687

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

PubMed Central

Kakati, Tulika; Kashyap, Hirak; Bhattacharyya, Dhruba K.

2016-01-01

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature. PMID:27901073

Differential transcriptome analysis reveals genes related to cold tolerance in seabuckthorn carpenter moth, Eogystia hippophaecolus

PubMed Central

Hu, Ping; Wang, Tao; Tao, Jing; Zong, Shixiang

2017-01-01

Seabuckthorn carpenter moth, Eogystia hippophaecolus (Lepidoptera: Cossidae), is an important pest of sea buckthorn (Hippophae rhamnoides), which is a shrub that has significant ecological and economic value in China. E. hippophaecolus is highly cold tolerant, but limited studies have been conducted to elucidate the molecular mechanisms underlying its cold resistance. Here we sequenced the E. hippophaecolus transcriptome using RNA-Seq technology and performed de novo assembly from the short paired-end reads. We investigated the larval response to cold stress by comparing gene expression profiles between treatments. We obtained 118,034 unigenes, of which 22,161 were annotated with gene descriptions, conserved domains, gene ontology terms, and metabolic pathways. These resulted in 57 GO terms and 193 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. By comparing transcriptome profiles for differential gene expression, we identified many differentially expressed proteins and genes, including heat shock proteins and cuticular proteins which have previously been reported to be involved in cold resistance of insects. This study provides a global transcriptome analysis and an assessment of differential gene expression in E. hippophaecolus under cold stress. We found seven differential expressed genes in common between developmental stages, which were verified with qPCR. Our findings facilitate future genomic studies aimed at improving our understanding of the molecular mechanisms underlying the response of insects to low temperatures. PMID:29131867

Differential transcriptome analysis reveals genes related to cold tolerance in seabuckthorn carpenter moth, Eogystia hippophaecolus.

PubMed

Cui, Mingming; Hu, Ping; Wang, Tao; Tao, Jing; Zong, Shixiang

2017-01-01

Seabuckthorn carpenter moth, Eogystia hippophaecolus (Lepidoptera: Cossidae), is an important pest of sea buckthorn (Hippophae rhamnoides), which is a shrub that has significant ecological and economic value in China. E. hippophaecolus is highly cold tolerant, but limited studies have been conducted to elucidate the molecular mechanisms underlying its cold resistance. Here we sequenced the E. hippophaecolus transcriptome using RNA-Seq technology and performed de novo assembly from the short paired-end reads. We investigated the larval response to cold stress by comparing gene expression profiles between treatments. We obtained 118,034 unigenes, of which 22,161 were annotated with gene descriptions, conserved domains, gene ontology terms, and metabolic pathways. These resulted in 57 GO terms and 193 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. By comparing transcriptome profiles for differential gene expression, we identified many differentially expressed proteins and genes, including heat shock proteins and cuticular proteins which have previously been reported to be involved in cold resistance of insects. This study provides a global transcriptome analysis and an assessment of differential gene expression in E. hippophaecolus under cold stress. We found seven differential expressed genes in common between developmental stages, which were verified with qPCR. Our findings facilitate future genomic studies aimed at improving our understanding of the molecular mechanisms underlying the response of insects to low temperatures.

Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis.

PubMed

Grossmann, Steffen; Bauer, Sebastian; Robinson, Peter N; Vingron, Martin

2007-11-15

High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.

Prediction of protein subcellular localization by weighted gene ontology terms.

PubMed

Chi, Sang-Mun

2010-08-27

We develop a new weighting approach of gene ontology (GO) terms for predicting protein subcellular localization. The weights of individual GO terms, corresponding to their contribution to the prediction algorithm, are determined by the term-weighting methods used in text categorization. We evaluate several term-weighting methods, which are based on inverse document frequency, information gain, gain ratio, odds ratio, and chi-square and its variants. Additionally, we propose a new term-weighting method based on the logarithmic transformation of chi-square. The proposed term-weighting method performs better than other term-weighting methods, and also outperforms state-of-the-art subcellular prediction methods. Our proposed method achieves 98.1%, 99.3%, 98.1%, 98.1%, and 95.9% overall accuracies for the animal BaCelLo independent dataset (IDS), fungal BaCelLo IDS, animal Höglund IDS, fungal Höglund IDS, and PLOC dataset, respectively. Furthermore, the close correlation between high-weighted GO terms and subcellular localizations suggests that our proposed method appropriately weights GO terms according to their relevance to the localizations. Copyright 2010 Elsevier Inc. All rights reserved.

Toxicology ontology perspectives.

PubMed

Hardy, Barry; Apic, Gordana; Carthew, Philip; Clark, Dominic; Cook, David; Dix, Ian; Escher, Sylvia; Hastings, Janna; Heard, David J; Jeliazkova, Nina; Judson, Philip; Matis-Mitchell, Sherri; Mitic, Dragana; Myatt, Glenn; Shah, Imran; Spjuth, Ola; Tcheremenskaia, Olga; Toldo, Luca; Watson, David; White, Andrew; Yang, Chihae

2012-01-01

The field of predictive toxicology requires the development of open, public, computable, standardized toxicology vocabularies and ontologies to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. In this article we review ontology developments based on a set of perspectives showing how ontologies are being used in predictive toxicology initiatives and applications. Perspectives on resources and initiatives reviewed include OpenTox, eTOX, Pistoia Alliance, ToxWiz, Virtual Liver, EU-ADR, BEL, ToxML, and Bioclipse. We also review existing ontology developments in neighboring fields that can contribute to establishing an ontological framework for predictive toxicology. A significant set of resources is already available to provide a foundation for an ontological framework for 21st century mechanistic-based toxicology research. Ontologies such as ToxWiz provide a basis for application to toxicology investigations, whereas other ontologies under development in the biological, chemical, and biomedical communities could be incorporated in an extended future framework. OpenTox has provided a semantic web framework for the implementation of such ontologies into software applications and linked data resources. Bioclipse developers have shown the benefit of interoperability obtained through ontology by being able to link their workbench application with remote OpenTox web services. Although these developments are promising, an increased international coordination of efforts is greatly needed to develop a more unified, standardized, and open toxicology ontology framework.

A flexible ontology for inference of emergent whole cell function from relationships between subcellular processes.

PubMed

Hansen, Jens; Meretzky, David; Woldesenbet, Simeneh; Stolovitzky, Gustavo; Iyengar, Ravi

2017-12-18

Whole cell responses arise from coordinated interactions between diverse human gene products functioning within various pathways underlying sub-cellular processes (SCP). Lower level SCPs interact to form higher level SCPs, often in a context specific manner to give rise to whole cell function. We sought to determine if capturing such relationships enables us to describe the emergence of whole cell functions from interacting SCPs. We developed the Molecular Biology of the Cell Ontology based on standard cell biology and biochemistry textbooks and review articles. Currently, our ontology contains 5,384 genes, 753 SCPs and 19,180 expertly curated gene-SCP associations. Our algorithm to populate the SCPs with genes enables extension of the ontology on demand and the adaption of the ontology to the continuously growing cell biological knowledge. Since whole cell responses most often arise from the coordinated activity of multiple SCPs, we developed a dynamic enrichment algorithm that flexibly predicts SCP-SCP relationships beyond the current taxonomy. This algorithm enables us to identify interactions between SCPs as a basis for higher order function in a context dependent manner, allowing us to provide a detailed description of how SCPs together can give rise to whole cell functions. We conclude that this ontology can, from omics data sets, enable the development of detailed SCP networks for predictive modeling of emergent whole cell functions.

SPONGY (SPam ONtoloGY): Email Classification Using Two-Level Dynamic Ontology

PubMed Central

2014-01-01

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user's background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance. PMID:25254240

SPONGY (SPam ONtoloGY): email classification using two-level dynamic ontology.

PubMed

Youn, Seongwook

2014-01-01

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user's background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance.

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

PubMed

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

2006-03-01

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

Transcriptome analysis in different developmental stages of Batocera horsfieldi (Coleoptera: Cerambycidae) and comparison of candidate olfactory genes

PubMed Central

Yang, Wei; Yang, Chunping; Zhang, Jin; Yang, Yang; Wang, Baoxin; Guan, Fengrong

2018-01-01

The white-striped longhorn beetle Batocera horsfieldi (Coleoptera: Cerambycidae) is a polyphagous wood-boring pest that causes substantial damage to the lumber industry. Moreover olfactory proteins are crucial components to function in related processes, but the B. horsfieldi genome is not readily available for olfactory proteins analysis. In the present study, developmental transcriptomes of larvae from the first instar to the prepupal stage, pupae, and adults (females and males) from emergence to mating were built by RNA sequencing to establish a genetic background that may help understand olfactory genes. Approximately 199 million clean reads were obtained and assembled into 171,664 transcripts, which were classified into 23,380, 26,511, 22,393, 30,270, and 87, 732 unigenes for larvae, pupae, females, males, and combined datasets, respectively. The unigenes were annotated against NCBI’s non-redundant nucleotide and protein sequences, Swiss-Prot, Gene Ontology (GO), Pfam, Clusters of Eukaryotic Orthologous Groups (KOG), and KEGG Orthology (KO) databases. A total of 43,197 unigenes were annotated into 55 sub-categories under the three main GO categories; 25,237 unigenes were classified into 26 functional KOG categories, and 25,814 unigenes were classified into five functional KEGG Pathway categories. RSEM software identified 2,983, 3,097, 870, 2,437, 5,161, and 2,882 genes that were differentially expressed between larvae and males, larvae and pupae, larvae and females, males and females, males and pupae, and females and pupae, respectively. Among them, genes encoding seven candidate odorant binding proteins (OBPs) and three chemosensory proteins (CSPs) were identified. RT-PCR and RT-qPCR analyses showed that BhorOBP3, BhorCSP2, and BhorOBPC1/C3/C4 were highly expressed in the antenna of males, indicating these genes may may play key roles in foraging and host-orientation in B. horsfieldi. Our results provide valuable molecular information about the olfactory

Alteration of gene expression by zinc oxide nanoparticles or zinc sulfate in vivo and comparison with in vitro data: A harmonious case.

PubMed

Zhang, Wei-Dong; Zhao, Yong; Zhang, Hong-Fu; Wang, Shu-Kun; Hao, Zhi-Hui; Liu, Jing; Yuan, Yu-Qing; Zhang, Peng-Fei; Yang, Hong-Di; Shen, Wei; Li, Lan

2016-08-01

Granulosa cells (GCs) are those somatic cells closest to the female germ cell. GCs play a vital role in oocyte growth and development, and the oocyte is necessary for multiplication of a species. Zinc oxide (ZnO) nanoparticles (NPs) readily cross biologic barriers to be absorbed into biologic systems that make them promising candidates as food additives. The objective of the present investigation was to explore the impact of intact NPs on gene expression and the functional classification of altered genes in hen GCs in vivo, to compare the data from in vivo and in vitro studies, and finally to point out the adverse effects of ZnO NPs on the reproductive system. After a 24-week treatment, hen GCs were isolated and gene expression was quantified. Intact NPs were found in the ovary and other organs. Zn levels were similar in ZnO-NP-100 mg/kg- and ZnSO4-100 mg/kg-treated hen ovaries. ZnO-NP-100 mg/kg and ZnSO4-100 mg/kg regulated the expression of the same sets of genes, and they also altered the expression of different sets of genes individually. The number of genes altered by the ZnO-NP-100 mg/kg and ZnSO4-100 mg/kg treatments was different. Gene Ontology (GO) functional analysis reported that different results for the two treatments and, in Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, 12 pathways (out of the top 20 pathways) in each treatment were different. These results suggested that intact NPs and Zn(2+) had different effects on gene expression in GCs in vivo. In our recent publication, we noted that intact NPs and Zn(2+) differentially altered gene expression in GCs in vitro. However, GO functional classification and KEGG pathway enrichment analyses revealed close similarities for the changed genes in vivo and in vitro after ZnO NP treatment. Furthermore, close similarities were observed for the changed genes after ZnSO4 treatments in vivo and in vitro by GO functional classification and KEGG pathway enrichment analyses. Therefore

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

PubMed

Malhotra, Ashutosh; Gündel, Michaela; Rajput, Abdul Mateen; Mevissen, Heinz-Theodor; Saiz, Albert; Pastor, Xavier; Lozano-Rubi, Raimundo; Martinez-Lapiscina, Elena H; Martinez-Lapsicina, Elena H; Zubizarreta, Irati; Mueller, Bernd; Kotelnikova, Ekaterina; Toldo, Luca; Hofmann-Apitius, Martin; Villoslada, Pablo

2015-01-01

In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS). The MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology. Validation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports. The MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.

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

Standardized description of scientific evidence using the Evidence Ontology (ECO)

PubMed Central

Chibucos, Marcus C.; Mungall, Christopher J.; Balakrishnan, Rama; Christie, Karen R.; Huntley, Rachael P.; White, Owen; Blake, Judith A.; Lewis, Suzanna E.; Giglio, Michelle

2014-01-01

The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources. Database URL: Evidence Ontology Web site: http://evidenceontology.org PMID:25052702

Identifying arsenic trioxide (ATO) functions in leukemia cells by using time series gene expression profiles.

PubMed

Yang, Hong; Lin, Shan; Cui, Jingru

2014-02-10

Arsenic trioxide (ATO) is presently the most active single agent in the treatment of acute promyelocytic leukemia (APL). In order to explore the molecular mechanism of ATO in leukemia cells with time series, we adopted bioinformatics strategy to analyze expression changing patterns and changes in transcription regulation modules of time series genes filtered from Gene Expression Omnibus database (GSE24946). We totally screened out 1847 time series genes for subsequent analysis. The KEGG (Kyoto encyclopedia of genes and genomes) pathways enrichment analysis of these genes showed that oxidative phosphorylation and ribosome were the top 2 significantly enriched pathways. STEM software was employed to compare changing patterns of gene expression with assigned 50 expression patterns. We screened out 7 significantly enriched patterns and 4 tendency charts of time series genes. The result of Gene Ontology showed that functions of times series genes mainly distributed in profiles 41, 40, 39 and 38. Seven genes with positive regulation of cell adhesion function were enriched in profile 40, and presented the same first increased model then decreased model as profile 40. The transcription module analysis showed that they mainly involved in oxidative phosphorylation pathway and ribosome pathway. Overall, our data summarized the gene expression changes in ATO treated K562-r cell lines with time and suggested that time series genes mainly regulated cell adhesive. Furthermore, our result may provide theoretical basis of molecular biology in treating acute promyelocytic leukemia. Copyright © 2013 Elsevier B.V. All rights reserved.

Multi-tissue transcriptomics for construction of a comprehensive gene resource for the terrestrial snail Theba pisana.

PubMed

Zhao, M; Wang, T; Adamson, K J; Storey, K B; Cummins, S F

2016-02-08

The land snail Theba pisana is native to the Mediterranean region but has become one of the most abundant invasive species worldwide. Here, we present three transcriptomes of this agriculture pest derived from three tissues: the central nervous system, hepatopancreas (digestive gland), and foot muscle. Sequencing of the three tissues produced 339,479,092 high quality reads and a global de novo assembly generated a total of 250,848 unique transcripts (unigenes). BLAST analysis mapped 52,590 unigenes to NCBI non-redundant protein databases and further functional analysis annotated 21,849 unigenes with gene ontology. We report that T. pisana transcripts have representatives in all functional classes and a comparison of differentially expressed transcripts amongst all three tissues demonstrates enormous differences in their potential metabolic activities. The genes differentially expressed include those with sequence similarity to those genes associated with multiple bacterial diseases and neurological diseases. To provide a valuable resource that will assist functional genomics study, we have implemented a user-friendly web interface, ThebaDB (http://thebadb.bioinfo-minzhao.org/). This online database allows for complex text queries, sequence searches, and data browsing by enriched functional terms and KEGG mapping.

Alignment of ICNP® 2.0 ontology and a proposed INCP® Brazilian ontology.

PubMed

Carvalho, Carina Maris Gaspar; Cubas, Marcia Regina; Malucelli, Andreia; Nóbrega, Maria Miriam Lima da

2014-01-01

to align the International Classification for Nursing Practice (ICNP®) Version 2.0 ontology and a proposed INCP® Brazilian Ontology. document-based, exploratory and descriptive study, the empirical basis of which was provided by the ICNP® 2.0 Ontology and the INCP® Brazilian Ontology. The ontology alignment was performed using a computer tool with algorithms to identify correspondences between concepts, which were organized and analyzed according to their presence or absence, their names, and their sibling, parent, and child classes. there were 2,682 concepts present in the ICNP® 2.0 Ontology that were missing in the Brazilian Ontology; 717 concepts present in the Brazilian Ontology were missing in the ICNP® 2.0 Ontology; and there were 215 pairs of matching concepts. it is believed that the correspondences identified in this study might contribute to the interoperability between the representations of nursing practice elements in ICNP®, thus allowing the standardization of nursing records based on this classification system.

Using a Foundational Ontology for Reengineering a Software Enterprise Ontology

NASA Astrophysics Data System (ADS)

Perini Barcellos, Monalessa; de Almeida Falbo, Ricardo

The knowledge about software organizations is considerably relevant to software engineers. The use of a common vocabulary for representing the useful knowledge about software organizations involved in software projects is important for several reasons, such as to support knowledge reuse and to allow communication and interoperability between tools. Domain ontologies can be used to define a common vocabulary for sharing and reuse of knowledge about some domain. Foundational ontologies can be used for evaluating and re-designing domain ontologies, giving to these real-world semantics. This paper presents an evaluating of a Software Enterprise Ontology that was reengineered using the Unified Foundation Ontology (UFO) as basis.

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.

Analysis of Differentially Expressed Genes and Signaling Pathways Related to Intramuscular Fat Deposition in Skeletal Muscle of Sex-Linked Dwarf Chickens

PubMed Central

Ye, Yaqiong; Lin, Shumao; Mu, Heping; Tang, Xiaohong; Ou, Yangdan; Chen, Jian; Ma, Yongjiang; Li, Yugu

2014-01-01

Intramuscular fat (IMF) plays an important role in meat quality. However, the molecular mechanisms underlying IMF deposition in skeletal muscle have not been addressed for the sex-linked dwarf (SLD) chicken. In this study, potential candidate genes and signaling pathways related to IMF deposition in chicken leg muscle tissue were characterized using gene expression profiling of both 7-week-old SLD and normal chickens. A total of 173 differentially expressed genes (DEGs) were identified between the two breeds. Subsequently, 6 DEGs related to lipid metabolism or muscle development were verified in each breed based on gene ontology (GO) analysis. In addition, KEGG pathway analysis of DEGs indicated that some of them (GHR, SOCS3, and IGF2BP3) participate in adipocytokine and insulin signaling pathways. To investigate the role of the above signaling pathways in IMF deposition, the gene expression of pathway factors and other downstream genes were measured by using qRT-PCR and Western blot analyses. Collectively, the results identified potential candidate genes related to IMF deposition and suggested that IMF deposition in skeletal muscle of SLD chicken is regulated partially by pathways of adipocytokine and insulin and other downstream signaling pathways (TGF-β/SMAD3 and Wnt/catenin-β pathway). PMID:24757673

Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions.

PubMed

Christen, Victor; Hartung, Michael; Groß, Anika

2015-01-01

A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications. Here we present REX (Region Evolution eXplorer) a web-based system for exploring the evolution of ontology parts (regions). REX provides an analysis platform for currently about 1,000 versions of 16 well-known life science ontologies. Interactive workflows allow an explorative analysis of changing ontology regions and can be used to study evolution trends for long-term periods. REX is a web application providing an interactive and user-friendly interface to identify (un)stable regions in large life science ontologies. It is available at http://www.izbi.de/rex.

Toward Understanding the Genetic Basis of Yak Ovary Reproduction: A Characterization and Comparative Analyses of Estrus Ovary Transcriptiome in Yak and Cattle

PubMed Central

Huang, Cai; Mipam, Tserang Donko; Li, Jian

2016-01-01

Background Yaks (Bos grunniens) are endemic species that can adapt well to thin air, cold temperatures, and high altitude. These species can survive in harsh plateau environments and are major source of animal production for local residents, being an important breed in the Qinghai–Tibet Plateau. However, compared with ordinary cattle that live in the plains, yaks generally have lower fertility. Investigating the basic physiological molecular features of yak ovary and identifying the biological events underlying the differences between the ovaries of yak and plain cattle is necessary to understand the specificity of yak reproduction. Therefore, RNA-seq technology was applied to analyze transcriptome data comparatively between the yak and plain cattle estrous ovaries. Results After deep sequencing, 3,653,032 clean reads with a total of 4,828,772,880 base pairs were obtained from yak ovary library. Alignment analysis showed that 16992 yak genes mapped to the yak genome, among which, 12,731 and 14,631 genes were assigned to Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, comparison of yak and cattle ovary transcriptome data revealed that 1307 genes were significantly and differentially expressed between the two libraries, wherein 661 genes were upregulated and 646 genes were downregulated in yak ovary. Functional analysis showed that the differentially expressed genes were involved in various Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO annotations indicated that the genes related to “cell adhesion,” “hormonal” biological processes, and “calcium ion binding,” “cation transmembrane transport” molecular events were significantly active. KEGG pathway analysis showed that the “complement and coagulation cascade” pathway was the most enriched in yak ovary transcriptome data, followed by the “cytochrome P450” related and �

Back to the sea twice: identifying candidate plant genes for molecular evolution to marine life.

PubMed

Wissler, Lothar; Codoñer, Francisco M; Gu, Jenny; Reusch, Thorsten B H; Olsen, Jeanine L; Procaccini, Gabriele; Bornberg-Bauer, Erich

2011-01-12

Seagrasses are a polyphyletic group of monocotyledonous angiosperms that have adapted to a completely submerged lifestyle in marine waters. Here, we exploit two collections of expressed sequence tags (ESTs) of two wide-spread and ecologically important seagrass species, the Mediterranean seagrass Posidonia oceanica (L.) Delile and the eelgrass Zostera marina L., which have independently evolved from aquatic ancestors. This replicated, yet independent evolutionary history facilitates the identification of traits that may have evolved in parallel and are possible instrumental candidates for adaptation to a marine habitat. In our study, we provide the first quantitative perspective on molecular adaptations in two seagrass species. By constructing orthologous gene clusters shared between two seagrasses (Z. marina and P. oceanica) and eight distantly related terrestrial angiosperm species, 51 genes could be identified with detection of positive selection along the seagrass branches of the phylogenetic tree. Characterization of these positively selected genes using KEGG pathways and the Gene Ontology uncovered that these genes are mostly involved in translation, metabolism, and photosynthesis. These results provide first insights into which seagrass genes have diverged from their terrestrial counterparts via an initial aquatic stage characteristic of the order and to the derived fully-marine stage characteristic of seagrasses. We discuss how adaptive changes in these processes may have contributed to the evolution towards an aquatic and marine existence.

Ontology Research and Development. Part 2 - A Review of Ontology Mapping and Evolving.

ERIC Educational Resources Information Center

Ding, Ying; Foo, Schubert

2002-01-01

Reviews ontology research and development, specifically ontology mapping and evolving. Highlights include an overview of ontology mapping projects; maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired; and ontology's role and the future of the World Wide Web, or Semantic Web. (Contains 55…

The MGED Ontology: a resource for semantics-based description of microarray experiments.

PubMed

Whetzel, Patricia L; Parkinson, Helen; Causton, Helen C; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Game, Laurence; Heiskanen, Mervi; Morrison, Norman; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Taylor, Chris; White, Joseph; Stoeckert, Christian J

2006-04-01

The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). Stoeckrt@pcbi.upenn.edu Supplementary data are available at Bioinformatics online.

Transcriptome and Gene Ontology (GO) Enrichment Analysis Reveals Genes Involved in Biotin Metabolism That Affect L-Lysine Production in Corynebacterium glutamicum.

PubMed

Kim, Hong-Il; Kim, Jong-Hyeon; Park, Young-Jin

2016-03-09

Corynebacterium glutamicum is widely used for amino acid production. In the present study, 543 genes showed a significant change in their mRNA expression levels in L-lysine-producing C. glutamicum ATCC21300 than that in the wild-type C. glutamicum ATCC13032. Among these 543 differentially expressed genes (DEGs), 28 genes were up- or downregulated. In addition, 454 DEGs were functionally enriched and categorized based on BLAST sequence homologies and gene ontology (GO) annotations using the Blast2GO software. Interestingly, NCgl0071 (bioB, encoding biotin synthase) was expressed at levels ~20-fold higher in the L-lysine-producing ATCC21300 strain than that in the wild-type ATCC13032 strain. Five other genes involved in biotin metabolism or transport--NCgl2515 (bioA, encoding adenosylmethionine-8-amino-7-oxononanoate aminotransferase), NCgl2516 (bioD, encoding dithiobiotin synthetase), NCgl1883, NCgl1884, and NCgl1885--were also expressed at significantly higher levels in the L-lysine-producing ATCC21300 strain than that in the wild-type ATCC13032 strain, which we determined using both next-generation RNA sequencing and quantitative real-time PCR analysis. When we disrupted the bioB gene in C. glutamicum ATCC21300, L-lysine production decreased by approximately 76%, and the three genes involved in biotin transport (NCgl1883, NCgl1884, and NCgl1885) were significantly downregulated. These results will be helpful to improve our understanding of C. glutamicum for industrial amino acid production.

The MGED ontology: a framework for describing functional genomics experiments.

PubMed

Stoeckert, Christian J; Parkinson, Helen

2003-01-01

The Microarray Gene Expression Data (MGED) society was formed with an initial focus on experiments involving microarray technology. Despite the diversity of applications, there are common concepts used and a common need to capture experimental information in a standardized manner. In building the MGED ontology, it was recognized that it would be impractical to cover all the different types of experiments on all the different types of organisms by listing and defining all the types of organisms and their properties. Our solution was to create a framework for describing microarray experiments with an initial focus on the biological sample and its manipulation. For concepts that are common for many species, we could provide a manageable listing of controlled terms. For concepts that are species-specific or whose values cannot be readily listed, we created an 'OntologyEntry' concept that referenced an external resource. The MGED ontology is a work in progress that needs additional instances and particularly needs constraints to be added. The ontology currently covers the experimental sample and design, and we have begun capturing aspects of the microarrays themselves as well. The primary application of the ontology will be to develop forms for entering information into databases, and consequently allowing queries, taking advantage of the structure provided by the ontology. The application of an ontology of experimental conditions extends beyond microarray experiments and, as the scope of MGED includes other aspects of functional genomics, so too will the MGED ontology.

Protein complex prediction in large ontology attributed protein-protein interaction networks.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.

PubMed

Cui, Licong

Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

PubMed Central

Bodenreider, O.

2008-01-01

Summary Objectives To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed. PMID:18660879

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability.

PubMed

He, Yongqun; Xiang, Zuoshuang; Zheng, Jie; Lin, Yu; Overton, James A; Ong, Edison

2018-01-12

Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an "eXtensible Ontology Development" (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).

A robust data-driven approach for gene ontology annotation.

PubMed

Li, Yanpeng; Yu, Hong

2014-01-01

Gene ontology (GO) and GO annotation are important resources for biological information management and knowledge discovery, but the speed of manual annotation became a major bottleneck of database curation. BioCreative IV GO annotation task aims to evaluate the performance of system that automatically assigns GO terms to genes based on the narrative sentences in biomedical literature. This article presents our work in this task as well as the experimental results after the competition. For the evidence sentence extraction subtask, we built a binary classifier to identify evidence sentences using reference distance estimator (RDE), a recently proposed semi-supervised learning method that learns new features from around 10 million unlabeled sentences, achieving an F1 of 19.3% in exact match and 32.5% in relaxed match. In the post-submission experiment, we obtained 22.1% and 35.7% F1 performance by incorporating bigram features in RDE learning. In both development and test sets, RDE-based method achieved over 20% relative improvement on F1 and AUC performance against classical supervised learning methods, e.g. support vector machine and logistic regression. For the GO term prediction subtask, we developed an information retrieval-based method to retrieve the GO term most relevant to each evidence sentence using a ranking function that combined cosine similarity and the frequency of GO terms in documents, and a filtering method based on high-level GO classes. The best performance of our submitted runs was 7.8% F1 and 22.2% hierarchy F1. We found that the incorporation of frequency information and hierarchy filtering substantially improved the performance. In the post-submission evaluation, we obtained a 10.6% F1 using a simpler setting. Overall, the experimental analysis showed our approaches were robust in both the two tasks. © The Author(s) 2014. Published by Oxford University Press.

Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies

PubMed Central

Nilsson, Björn; Håkansson, Petra; Johansson, Mikael; Nelander, Sven; Fioretos, Thoas

2007-01-01

Ontological analysis facilitates the interpretation of microarray data. Here we describe new ontological analysis methods which, unlike existing approaches, are threshold-free and statistically powerful. We perform extensive evaluations and introduce a new concept, detection spectra, to characterize methods. We show that different ontological analysis methods exhibit distinct detection spectra, and that it is critical to account for this diversity. Our results argue strongly against the continued use of existing methods, and provide directions towards an enhanced approach. PMID:17488501

TGF-beta signaling proteins and the Protein Ontology.

PubMed

Arighi, Cecilia N; Liu, Hongfang; Natale, Darren A; Barker, Winona C; Drabkin, Harold; Blake, Judith A; Smith, Barry; Wu, Cathy H

2009-05-06

The Protein Ontology (PRO) is designed as a formal and principled Open Biomedical Ontologies (OBO) Foundry ontology for proteins. The components of PRO extend from a classification of proteins on the basis of evolutionary relationships at the homeomorphic level to the representation of the multiple protein forms of a gene, including those resulting from alternative splicing, cleavage and/or post-translational modifications. Focusing specifically on the TGF-beta signaling proteins, we describe the building, curation, usage and dissemination of PRO. PRO is manually curated on the basis of PrePRO, an automatically generated file with content derived from standard protein data sources. Manual curation ensures that the treatment of the protein classes and the internal and external relationships conform to the PRO framework. The current release of PRO is based upon experimental data from mouse and human proteins wherein equivalent protein forms are represented by single terms. In addition to the PRO ontology, the annotation of PRO terms is released as a separate PRO association file, which contains, for each given PRO term, an annotation from the experimentally characterized sub-types as well as the corresponding database identifiers and sequence coordinates. The annotations are added in the form of relationship to other ontologies. Whenever possible, equivalent forms in other species are listed to facilitate cross-species comparison. Splice and allelic variants, gene fusion products and modified protein forms are all represented as entities in the ontology. Therefore, PRO provides for the representation of protein entities and a resource for describing the associated data. This makes PRO useful both for proteomics studies where isoforms and modified forms must be differentiated, and for studies of biological pathways, where representations need to take account of the different ways in which the cascade of events may depend on specific protein modifications. PRO provides

Differential mantle transcriptomics and characterization of growth-related genes in the diploid and triploid pearl oyster Pinctada fucata.

PubMed

Guan, Yunyan; He, Maoxian; Wu, Houbo

2017-06-01

To explore the molecular mechanism of triploidy effect in the pearl oyster Pinctada fucata, two RNA-seq libraries were constructed from the mantle tissue of diploids and triploids by Roche-454 massive parallel pyrosequencing. The identification of differential expressed genes (DEGs) between diploid and triploid may reveal the molecular mechanism of triploidy effect. In this study, 230 down-regulated and 259 up-regulated DEGs were obtained by comparison between diploid and triploid libraries. The gene ontology and KEGG pathway analysis revealed more functional activation in triploids and it may due to the duplicated gene expression in transcriptional level during whole genome duplication (WGD). To confirm the sequencing data, a set of 11 up-regulated genes related to growth and development control and regulation were analyzed by RT-qPCR in independent experiment. According to the validation and annotation of these genes, it is hypothesized that the set of up-regulated expressed genes had the correlated expression pattern involved in shell building or other interactive probable functions during triploidization. The up- regulation of growth-related genes may support the classic hypotheses of 'energy redistribution' from early research. The results provide valuable resources to understand the molecular mechanism of triploidy effect in both shell building and producing high-quality seawater pearls. Copyright © 2017 Elsevier B.V. All rights reserved.

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

PubMed Central

2011-01-01

Background Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data. Results We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA. Conclusions GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches. PMID:21914205

DOSE RESPONSE FROM HIGH THROUGHPUT GENE EXPRESSION STUDIES AND THE INFLUENCE OF TIME AND CELL LINE ON INFERRED MODE OF ACTION BY ONTOLOGIC ENRICHMENT (SOT)

EPA Science Inventory

Gene expression with ontologic enrichment and connectivity mapping tools is widely used to infer modes of action (MOA) for therapeutic drugs. Despite progress in high-throughput (HT) genomic systems, strategies suitable to identify industrial chemical MOA are needed. The L1000 is...

TermGenie - a web-application for pattern-based ontology class generation.

PubMed

Dietze, Heiko; Berardini, Tanya Z; Foulger, Rebecca E; Hill, David P; Lomax, Jane; Osumi-Sutherland, David; Roncaglia, Paola; Mungall, Christopher J

2014-01-01

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 new classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.

Digital Gene Expression Analysis Provides Insight into the Transcript Profile of the Genes Involved in Aporphine Alkaloid Biosynthesis in Lotus (Nelumbo nucifera)

PubMed Central

Yang, Mei; Zhu, Lingping; Li, Ling; Li, Juanjuan; Xu, Liming; Feng, Ji; Liu, Yanling

2017-01-01

The predominant alkaloids in lotus leaves are aporphine alkaloids. These are the most important active components and have many pharmacological properties, but little is known about their biosynthesis. We used digital gene expression (DGE) technology to identify differentially-expressed genes (DEGs) between two lotus cultivars with different alkaloid contents at four leaf development stages. We also predicted potential genes involved in aporphine alkaloid biosynthesis by weighted gene co-expression network analysis (WGCNA). Approximately 335 billion nucleotides were generated; and 94% of which were aligned against the reference genome. Of 22 thousand expressed genes, 19,000 were differentially expressed between the two cultivars at the four stages. Gene Ontology (GO) enrichment analysis revealed that catalytic activity and oxidoreductase activity were enriched significantly in most pairwise comparisons. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, dozens of DEGs were assigned to the categories of biosynthesis of secondary metabolites, isoquinoline alkaloid biosynthesis, and flavonoid biosynthesis. The genes encoding norcoclaurine synthase (NCS), norcoclaurine 6-O-methyltransferase (6OMT), coclaurine N-methyltransferase (CNMT), N-methylcoclaurine 3′-hydroxylase (NMCH), and 3′-hydroxy-N-methylcoclaurine 4′-O-methyltransferase (4′OMT) in the common pathways of benzylisoquinoline alkaloid biosynthesis and the ones encoding corytuberine synthase (CTS) in aporphine alkaloid biosynthetic pathway, which have been characterized in other plants, were identified in lotus. These genes had positive effects on alkaloid content, albeit with phenotypic lag. The WGCNA of DEGs revealed that one network module was associated with the dynamic change of alkaloid content. Eleven genes encoding proteins with methyltransferase, oxidoreductase and CYP450 activities were identified. These were surmised to be genes involved in aporphine alkaloid biosynthesis. This

Gene expression profiles of fin regeneration in loach (Paramisgurnus dabryanu).

PubMed

Li, Li; He, Jingya; Wang, Linlin; Chen, Weihua; Chang, Zhongjie

2017-11-01

Teleost fins can regenerate accurate position-matched structure and function after amputation. However, we still lack systematic transcriptional profiling and methodologies to understand the molecular basis of fin regeneration. After histological analysis, we established a suppression subtraction hybridization library containing 418 distinct sequences expressed differentially during the process of blastema formation and differentiation in caudal fin regeneration. Genome ontology and comparative analysis of differential distribution of our data and the reference zebrafish genome showed notable subcategories, including multi-organism processes, response to stimuli, extracellular matrix, antioxidant activity, and cell junction function. KEGG pathway analysis allowed the effective identification of relevant genes in those pathways involved in tissue morphogenesis and regeneration, including tight junction, cell adhesion molecules, mTOR and Jak-STAT signaling pathway. From relevant function subcategories and signaling pathways, 78 clones were examined for further Southern-blot hybridization. Then, 17 genes were chosen and characterized using semi-quantitative PCR. Then 4 candidate genes were identified, including F11r, Mmp9, Agr2 and one without a match to any database. After real-time quantitative PCR, the results showed obvious expression changes in different periods of caudal fin regeneration. We can assume that the 4 candidates, likely valuable genes associated with fin regeneration, deserve additional attention. Thus, our study demonstrated how to investigate the transcript profiles with an emphasis on bioinformatics intervention and how to identify potential genes related to fin regeneration processes. The results also provide a foundation or knowledge for further research into genes and molecular mechanisms of fin regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Ontology Research and Development. Part 1-A Review of Ontology Generation.

ERIC Educational Resources Information Center

Ding, Ying; Foo, Schubert

2002-01-01

Discusses the role of ontology in knowledge representation, including enabling content-based access, interoperability, communications, and new levels of service on the Semantic Web; reviews current ontology generation studies and projects as well as problems facing such research; and discusses ontology mapping, information extraction, natural…

Simple Ontology Format (SOFT)

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

Sorokine, Alexandre

2011-10-01

Simple Ontology Format (SOFT) library and file format specification provides a set of simple tools for developing and maintaining ontologies. The library, implemented as a perl module, supports parsing and verification of the files in SOFt format, operations with ontologies (adding, removing, or filtering of entities), and converting of ontologies into other formats. SOFT allows users to quickly create ontologies using only a basic text editor, verify it, and portray it in a graph layout system using customized styles.

Overview of the gene ontology task at BioCreative IV.

PubMed

Mao, Yuqing; Van Auken, Kimberly; Li, Donghui; Arighi, Cecilia N; McQuilton, Peter; Hayman, G Thomas; Tweedie, Susan; Schaeffer, Mary L; Laulederkind, Stanley J F; Wang, Shur-Jen; Gobeill, Julien; Ruch, Patrick; Luu, Anh Tuan; Kim, Jung-Jae; Chiang, Jung-Hsien; Chen, Yu-De; Yang, Chia-Jung; Liu, Hongfang; Zhu, Dongqing; Li, Yanpeng; Yu, Hong; Emadzadeh, Ehsan; Gonzalez, Graciela; Chen, Jian-Ming; Dai, Hong-Jie; Lu, Zhiyong

2014-01-01

Gene ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO

The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations.

PubMed

Chibucos, Marcus C; Siegele, Deborah A; Hu, James C; Giglio, Michelle

2017-01-01

The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

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

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Rebholz-Schuhmann, Dietrich; Schofield, Paul N; Gkoutos, Georgios V

2011-01-01

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.

Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.

PubMed

Ochs, Christopher; He, Zhe; Zheng, Ling; Geller, James; Perl, Yehoshua; Hripcsak, George; Musen, Mark A

2016-06-01

An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one ontology at a time. To improve the efficiency of the Abstraction-Network-based QA methodology, we introduced a QA framework that uses uniform Abstraction Network derivation techniques and QA methodologies that are applicable to whole families of structurally similar ontologies. For the family-based framework to be successful, it is necessary to develop a method for classifying ontologies into structurally similar families. We now describe a structural meta-ontology that classifies ontologies according to certain structural features that are commonly used in the modeling of ontologies (e.g., object properties) and that are important for Abstraction Network derivation. Each class of the structural meta-ontology represents a family of ontologies with identical structural features, indicating which types of Abstraction Networks and QA methodologies are potentially applicable to all of the ontologies in the family. We derive a collection of 81 families, corresponding to classes of the structural meta-ontology, that enable a flexible, streamlined family-based QA methodology, offering multiple choices for classifying an ontology. The structure of 373 ontologies from the NCBO BioPortal is analyzed and each ontology is classified into multiple families modeled by the structural meta-ontology. Copyright © 2016 Elsevier Inc. All rights reserved.

De novo analysis of the Nilaparvata lugens (Stål) antenna transcriptome and expression patterns of olfactory genes.

PubMed

Zhou, Shuang-Shuang; Sun, Ze; Ma, Weihua; Chen, Wei; Wang, Man-Qun

2014-03-01

We sequenced the antenna transcriptome of the brown planthopper (BPH), Nilaparvata lugens (Stål), a global rice pest, and performed transcriptome analysis on BPH antenna. We obtained about 40million 90bp reads that were assembled into 75,874 unigenes with a mean size of 456bp. Among the antenna transcripts, 32,856 (43%) showed significant similarity (E-value <1e(-5)) to known proteins in the NCBI database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to classify functions of BPH antenna genes. We identified 10 odorant-binding proteins (OBPs), including 7 previously unidentified, and 11 chemosensory proteins (CSPs), including two new members. The expression profiles of 4 OBPs and 2 CSPs were determined by q-PCR for antenna, abdomen, leg and wing of insects of different age, gender, and mating status including two BPH adult wing-morphology types. NlugCSP10 and 4 OBPs appeared to be antenna-specific because they were highly and differentially expressed in male and female antennae. NlugCSP11 was expressed ubiquitously, with particularly high expression in wings. The transcript levels of several olfactory genes depended on adult wing form, age, gender, and mating status, although no clear expression patterns were determined. Copyright © 2013 Elsevier Inc. All rights reserved.

Gene discovery using next-generation pyrosequencing to develop ESTs for Phalaenopsis orchids

PubMed Central

2011-01-01

Background Orchids are one of the most diversified angiosperms, but few genomic resources are available for these non-model plants. In addition to the ecological significance, Phalaenopsis has been considered as an economically important floriculture industry worldwide. We aimed to use massively parallel 454 pyrosequencing for a global characterization of the Phalaenopsis transcriptome. Results To maximize sequence diversity, we pooled RNA from 10 samples of different tissues, various developmental stages, and biotic- or abiotic-stressed plants. We obtained 206,960 expressed sequence tags (ESTs) with an average read length of 228 bp. These reads were assembled into 8,233 contigs and 34,630 singletons. The unigenes were searched against the NCBI non-redundant (NR) protein database. Based on sequence similarity with known proteins, these analyses identified 22,234 different genes (E-value cutoff, e-7). Assembled sequences were annotated with Gene Ontology, Gene Family and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Among these annotations, over 780 unigenes encoding putative transcription factors were identified. Conclusion Pyrosequencing was effective in identifying a large set of unigenes from Phalaenopsis. The informative EST dataset we developed constitutes a much-needed resource for discovery of genes involved in various biological processes in Phalaenopsis and other orchid species. These transcribed sequences will narrow the gap between study of model organisms with many genomic resources and species that are important for ecological and evolutionary studies. PMID:21749684

Evaluation of differentially expressed immune-related genes in intestine of Pelodiscus sinensis after intragastric challenge with lipopolysaccharide based on transcriptome analysis.

PubMed

Xu, Jiehao; Zhao, Jing; Li, Yiqun; Zou, Yiyi; Lu, Binjie; Chen, Yuyin; Ma, Youzhi; Xu, Haisheng

2016-09-01

Pelodiscus sinensis is the most common turtle species that has been raised in East and Southeast Asia. However, there are still limited studies about the immune defense mechanisms in its small intestine until now. In the present research, histological analysis and transcriptome analysis was performed on the small intestine of P. sinensis after intragastric challenge with LPS to explore its mechanisms of immune responses to pathogens. The result showed the number of intraepithelial lymphocytes (IELs) and goblet cells (GCs) in its intestine increased significantly at 48 h post-challenge with LPS by intragastrical route, indicating clearly the intestinal immune response was induced. Compared with the control, a total of 748 differentially expressed genes (DEGs) were identified, including 361 up-regulated genes and 387 down-regulated genes. Based on the Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG), 48 immune-related DEGs were identified, which were classified into 82 GO terms and 14 pathways. Finally, 18 DEGs, which were randomly selected, were confirmed by quantitative real-time PCR (qRT-PCR). Our results provide valuable information for further analysis of the immune defense mechanisms against pathogens in the small intestine of P. sinensis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automatic annotation of protein motif function with Gene Ontology terms.

PubMed

Lu, Xinghua; Zhai, Chengxiang; Gopalakrishnan, Vanathi; Buchanan, Bruce G

2004-09-02

Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects.

PubMed

Strohmaier, Markus; Walk, Simon; Pöschko, Jan; Lamprecht, Daniel; Tudorache, Tania; Nyulas, Csongor; Musen, Mark A; Noy, Natalya F

2013-05-01

Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product . In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to its construction is critical not only in understanding but consequently also in evaluating the ontology. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.

How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects

PubMed Central

Strohmaier, Markus; Walk, Simon; Pöschko, Jan; Lamprecht, Daniel; Tudorache, Tania; Nyulas, Csongor; Musen, Mark A.; Noy, Natalya F.

2013-01-01

Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to its construction is critical not only in understanding but consequently also in evaluating the ontology. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches. PMID:24311994

Biomedical ontologies: toward scientific debate.

PubMed

Maojo, V; Crespo, J; García-Remesal, M; de la Iglesia, D; Perez-Rey, D; Kulikowski, C

2011-01-01

Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective. We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies. We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies. Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.

The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data.

PubMed

Zhang, Chaoyang; Peng, Li; Zhang, Yaqin; Liu, Zhaoyang; Li, Wenling; Chen, Shilian; Li, Guancheng

2017-06-01

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein-protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM-receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched

Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes.

PubMed

Feuermann, Marc; Gaudet, Pascale; Mi, Huaiyu; Lewis, Suzanna E; Thomas, Paul D

2016-01-01

We previously reported a paradigm for large-scale phylogenomic analysis of gene families that takes advantage of the large corpus of experimentally supported Gene Ontology (GO) annotations. This 'GO Phylogenetic Annotation' approach integrates GO annotations from evolutionarily related genes across ∼100 different organisms in the context of a gene family tree, in which curators build an explicit model of the evolution of gene functions. GO Phylogenetic Annotation models the gain and loss of functions in a gene family tree, which is used to infer the functions of uncharacterized (or incompletely characterized) gene products, even for human proteins that are relatively well studied. Here, we report our results from applying this paradigm to two well-characterized cellular processes, apoptosis and autophagy. This revealed several important observations with respect to GO annotations and how they can be used for function inference. Notably, we applied only a small fraction of the experimentally supported GO annotations to infer function in other family members. The majority of other annotations describe indirect effects, phenotypes or results from high throughput experiments. In addition, we show here how feedback from phylogenetic annotation leads to significant improvements in the PANTHER trees, the GO annotations and GO itself. Thus GO phylogenetic annotation both increases the quantity and improves the accuracy of the GO annotations provided to the research community. We expect these phylogenetically based annotations to be of broad use in gene enrichment analysis as well as other applications of GO annotations.Database URL: http://amigo.geneontology.org/amigo. © The Author(s) 2016. Published by Oxford University Press.

Ontology-Driven Business Modelling: Improving the Conceptual Representation of the REA Ontology

NASA Astrophysics Data System (ADS)

Gailly, Frederik; Poels, Geert

Business modelling research is increasingly interested in exploring how domain ontologies can be used as reference models for business models. The Resource Event Agent (REA) ontology is a primary candidate for ontology-driven modelling of business processes because the REA point of view on business reality is close to the conceptual modelling perspective on business models. In this paper Ontology Engineering principles are employed to reengineer REA in order to make it more suitable for ontology-driven business modelling. The new conceptual representation of REA that we propose uses a single representation formalism, includes a more complete domain axiomatizat-ion (containing definitions of concepts, concept relations and ontological axioms), and is proposed as a generic model that can be instantiated to create valid business models. The effects of these proposed improvements on REA-driven business modelling are demonstrated using a business modelling example.

GoGene: gene annotation in the fast lane.

PubMed

Plake, Conrad; Royer, Loic; Winnenburg, Rainer; Hakenberg, Jörg; Schroeder, Michael

2009-07-01

High-throughput screens such as microarrays and RNAi screens produce huge amounts of data. They typically result in hundreds of genes, which are often further explored and clustered via enriched GeneOntology terms. The strength of such analyses is that they build on high-quality manual annotations provided with the GeneOntology. However, the weakness is that annotations are restricted to process, function and location and that they do not cover all known genes in model organisms. GoGene addresses this weakness by complementing high-quality manual annotation with high-throughput text mining extracting co-occurrences of genes and ontology terms from literature. GoGene contains over 4,000,000 associations between genes and gene-related terms for 10 model organisms extracted from more than 18,000,000 PubMed entries. It does not cover only process, function and location of genes, but also biomedical categories such as diseases, compounds, techniques and mutations. By bringing it all together, GoGene provides the most recent and most complete facts about genes and can rank them according to novelty and importance. GoGene accepts keywords, gene lists, gene sequences and protein sequences as input and supports search for genes in PubMed, EntrezGene and via BLAST. Since all associations of genes to terms are supported by evidence in the literature, the results are transparent and can be verified by the user. GoGene is available at http://gopubmed.org/gogene.

Towards refactoring the Molecular Function Ontology with a UML profile for function modeling.

PubMed

Burek, Patryk; Loebe, Frank; Herre, Heinrich

2017-10-04

Gene Ontology (GO) is the largest resource for cataloging gene products. This resource grows steadily and, naturally, this growth raises issues regarding the structure of the ontology. Moreover, modeling and refactoring large ontologies such as GO is generally far from being simple, as a whole as well as when focusing on certain aspects or fragments. It seems that human-friendly graphical modeling languages such as the Unified Modeling Language (UML) could be helpful in connection with these tasks. We investigate the use of UML for making the structural organization of the Molecular Function Ontology (MFO), a sub-ontology of GO, more explicit. More precisely, we present a UML dialect, called the Function Modeling Language (FueL), which is suited for capturing functions in an ontologically founded way. FueL is equipped, among other features, with language elements that arise from studying patterns of subsumption between functions. We show how to use this UML dialect for capturing the structure of molecular functions. Furthermore, we propose and discuss some refactoring options concerning fragments of MFO. FueL enables the systematic, graphical representation of functions and their interrelations, including making information explicit that is currently either implicit in MFO or is mainly captured in textual descriptions. Moreover, the considered subsumption patterns lend themselves to the methodical analysis of refactoring options with respect to MFO. On this basis we argue that the approach can increase the comprehensibility of the structure of MFO for humans and can support communication, for example, during revision and further development.

A top-level ontology of functions and its application in the Open Biomedical Ontologies.

PubMed

Burek, Patryk; Hoehndorf, Robert; Loebe, Frank; Visagie, Johann; Herre, Heinrich; Kelso, Janet

2006-07-15

A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration

PubMed Central

Ong, Edison; Xiang, Zuoshuang; Zhao, Bin; Liu, Yue; Lin, Yu; Zheng, Jie; Mungall, Chris; Courtot, Mélanie; Ruttenberg, Alan; He, Yongqun

2017-01-01

Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies. PMID:27733503

The ontology life cycle: Integrated tools for editing, publishing, peer review, and evolution of ontologies

PubMed Central

Noy, Natalya; Tudorache, Tania; Nyulas, Csongor; Musen, Mark

2010-01-01

Ontologies have become a critical component of many applications in biomedical informatics. However, the landscape of the ontology tools today is largely fragmented, with independent tools for ontology editing, publishing, and peer review: users develop an ontology in an ontology editor, such as Protégé; and publish it on a Web server or in an ontology library, such as BioPortal, in order to share it with the community; they use the tools provided by the library or mailing lists and bug trackers to collect feedback from users. In this paper, we present a set of tools that bring the ontology editing and publishing closer together, in an integrated platform for the entire ontology lifecycle. This integration streamlines the workflow for collaborative development and increases integration between the ontologies themselves through the reuse of terms. PMID:21347039

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

PubMed Central

Liu, Chuan-He; Fan, Chao

2016-01-01

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

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

Transcriptome profiling and digital gene expression analysis of sweet potato for the identification of putative genes involved in the defense response against Fusarium oxysporum f. sp. batatas.

PubMed

Lin, Yuli; Zou, Weikun; Lin, Shiqiang; Onofua, Dennis; Yang, Zhijian; Chen, Haizhou; Wang, Songliang; Chen, Xuanyang

2017-01-01

Sweet potato production is constrained by Fusarium wilt, which is caused by Fusarium oxysporum f. sp. batatas (Fob). The identification of genes related to disease resistance and the underlying mechanisms will contribute to improving disease resistance via sweet potato breeding programs. In the present study, we performed de novo transcriptome assembly and digital gene expression (DGE) profiling of sweet potato challenged with Fob using Illumina HiSeq technology. In total, 89,944,188 clean reads were generated from 12 samples and assembled into 101,988 unigenes with an average length of 666 bp; of these unigenes, 62,605 (61.38%) were functionally annotated in the NCBI non-redundant protein database by BLASTX with a cutoff E-value of 10-5. Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were examined to explore the unigenes' functions. We constructed four DGE libraries for the sweet potato cultivars JinShan57 (JS57, highly resistant) and XinZhongHua (XZH, highly susceptible), which were challenged with pathogenic Fob. Genes that were differentially expressed in the four libraries were identified by comparing the transcriptomes. Various genes that were differentially expressed during defense, including chitin elicitor receptor kinase 1 (CERK), mitogen-activated protein kinase (MAPK), WRKY, NAC, MYB, and ethylene-responsive transcription factor (ERF), as well as resistance genes, pathogenesis-related genes, and genes involved in salicylic acid (SA) and jasmonic acid (JA) signaling pathways, were identified. These data represent a sequence resource for genetic and genomic studies of sweet potato that will enhance the understanding of the mechanism of disease resistance.

Target gene screening and evaluation of prognostic values in non-small cell lung cancers by bioinformatics analysis.

PubMed

Piao, Junjie; Sun, Jie; Yang, Yang; Jin, Tiefeng; Chen, Liyan; Lin, Zhenhua

2018-03-20

Non-small cell lung cancer (NSCLC) is the major leading cause of cancer-related deaths worldwide. This study aims to explore molecular mechanism of NSCLC. Microarray dataset was obtained from the Gene Expression Omnibus (GEO) database, and analyzed by using GEO2R. Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, STRING, Cytoscape and MCODE were applied to construct the Protein-protein interaction (PPI) network and screen hub genes. Following, overall survival (OS) analysis of hub genes was performed by using the Kaplan-Meier plotter online tool. Moreover, miRecords was also applied to predict the targets of the differentially expressed microRNAs (DEMs). A total of 228 DEGs were identified, and they were mainly enriched in the terms of cell adhesion molecules, leukocyte transendothelial migration and ECM-receptor interaction. A PPI network was constructed, and 16 hub genes were identified, including TEK, ANGPT1, MMP9, VWF, CDH5, EDN1, ESAM, CCNE1, CDC45, PRC1, CCNB2, AURKA, MELK, CDC20, TOP2A and PTTG1. Among the genes, expressions of 14 hub genes were associated with prognosis of NSCLC patients. Additionally, a total of 11 DEMs were also identified. Our results provide some potential underlying biomarkers for NSCLC. Further studies are required to elucidate the pathogenesis of NSCLC. Copyright © 2018 Elsevier B.V. All rights reserved.

Identification of key genes associated with the effect of estrogen on ovarian cancer using microarray analysis.

PubMed

Zhang, Shi-tao; Zuo, Chao; Li, Wan-nan; Fu, Xue-qi; Xing, Shu; Zhang, Xiao-ping

2016-02-01

To identify key genes related to the effect of estrogen on ovarian cancer. Microarray data (GSE22600) were downloaded from Gene Expression Omnibus. Eight estrogen and seven placebo treatment samples were obtained using a 2 × 2 factorial designs, which contained 2 cell lines (PEO4 and 2008) and 2 treatments (estrogen and placebo). Differentially expressed genes were identified by Bayesian methods, and the genes with P < 0.05 and |log2FC (fold change)| ≥0.5 were chosen as cut-off criterion. Differentially co-expressed genes (DCGs) and differentially regulated genes (DRGs) were, respectively, identified by DCe function and DRsort function in DCGL package. Topological structure analysis was performed on the important transcriptional factors (TFs) and genes in transcriptional regulatory network using tYNA. Functional enrichment analysis was, respectively, performed for DEGs and the important genes using Gene Ontology and KEGG databases. In total, 465 DEGs were identified. Functional enrichment analysis of DEGs indicated that ACVR2B, LTBP1, BMP7 and MYC involved in TGF-beta signaling pathway. The 2285 DCG pairs and 357 DRGs were identified. Topological structure analysis showed that 52 important TFs and 65 important genes were identified. Functional enrichment analysis of the important genes showed that TP53 and MLH1 participated in DNA damage response and the genes (ACVR2B, LTBP1, BMP7 and MYC) involved in TGF-beta signaling pathway. TP53, MLH1, ACVR2B, LTBP1 and BMP7 might participate in the pathogenesis of ovarian cancer.

Extending the evaluation of Genia Event task toward knowledge base construction and comparison to Gene Regulation Ontology task

PubMed Central

2015-01-01

Background The third edition of the BioNLP Shared Task was held with the grand theme "knowledge base construction (KB)". The Genia Event (GE) task was re-designed and implemented in light of this theme. For its final report, the participating systems were evaluated from a perspective of annotation. To further explore the grand theme, we extended the evaluation from a perspective of KB construction. Also, the Gene Regulation Ontology (GRO) task was newly introduced in the third edition. The final evaluation of the participating systems resulted in relatively low performance. The reason was attributed to the large size and complex semantic representation of the ontology. To investigate potential benefits of resource exchange between the presumably similar tasks, we measured the overlap between the datasets of the two tasks, and tested whether the dataset for one task can be used to enhance performance on the other. Results We report an extended evaluation on all the participating systems in the GE task, incoporating a KB perspective. For the evaluation, the final submission of each participant was converted to RDF statements, and evaluated using 8 queries that were formulated in SPARQL. The results suggest that the evaluation may be concluded differently between the two different perspectives, annotation vs. KB. We also provide a comparison of the GE and GRO tasks by converting their datasets into each other's format. More than 90% of the GE data could be converted into the GRO task format, while only half of the GRO data could be mapped to the GE task format. The imbalance in conversion indicates that the GRO is a comprehensive extension of the GE task ontology. We further used the converted GRO data as additional training data for the GE task, which helped improve GE task participant system performance. However, the converted GE data did not help GRO task participants, due to overfitting and the ontology gap. PMID:26202680

Advancing data reuse in phyloinformatics using an ontology-driven Semantic Web approach.

PubMed

Panahiazar, Maryam; Sheth, Amit P; Ranabahu, Ajith; Vos, Rutger A; Leebens-Mack, Jim

2013-01-01

Phylogenetic analyses can resolve historical relationships among genes, organisms or higher taxa. Understanding such relationships can elucidate a wide range of biological phenomena, including, for example, the importance of gene and genome duplications in the evolution of gene function, the role of adaptation as a driver of diversification, or the evolutionary consequences of biogeographic shifts. Phyloinformaticists are developing data standards, databases and communication protocols (e.g. Application Programming Interfaces, APIs) to extend the accessibility of gene trees, species trees, and the metadata necessary to interpret these trees, thus enabling researchers across the life sciences to reuse phylogenetic knowledge. Specifically, Semantic Web technologies are being developed to make phylogenetic knowledge interpretable by web agents, thereby enabling intelligently automated, high-throughput reuse of results generated by phylogenetic research. This manuscript describes an ontology-driven, semantic problem-solving environment for phylogenetic analyses and introduces artefacts that can promote phyloinformatic efforts to promote accessibility of trees and underlying metadata. PhylOnt is an extensible ontology with concepts describing tree types and tree building methodologies including estimation methods, models and programs. In addition we present the PhylAnt platform for annotating scientific articles and NeXML files with PhylOnt concepts. The novelty of this work is the annotation of NeXML files and phylogenetic related documents with PhylOnt Ontology. This approach advances data reuse in phyloinformatics.

De novo assembly of the Japanese lawngrass (Zoysia japonica Steud.) root transcriptome and identification of candidate unigenes related to early responses under salt stress

PubMed Central

Xie, Qi; Niu, Jun; Xu, Xilin; Xu, Lixin; Zhang, Yinbing; Fan, Bo; Liang, Xiaohong; Zhang, Lijuan; Yin, Shuxia; Han, Liebao

2015-01-01

Japanese lawngrass (Zoysia japonica Steud.) is an important warm-season turfgrass that is able to survive in a range of soils, from infertile sands to clays, and to grow well under saline conditions. However, little is known about the molecular mechanisms involved in its resistance to salt stress. Here, we used high-throughput RNA sequencing (RNA-seq) to investigate the changes in gene expression of Zoysia grass at high NaCl concentrations. We first constructed two sequencing libraries, including control and NaCl-treated samples, and sequenced them using the Illumina HiSeq™ 2000 platform. Approximately 157.20 million paired-end reads with a total length of 68.68 Mb were obtained. Subsequently, 32,849 unigenes with an N50 length of 1781 bp were assembled using Trinity. Furthermore, three public databases, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-prot, and Clusters of Orthologous Groups (COGs), were used for gene function analysis and enrichment. The annotated genes included 57 Gene Ontology (GO) terms, 120 KEGG pathways, and 24 COGs. Compared with the control, 1455 genes were significantly different (false discovery rate ≤0.01, |log2Ratio |≥1) in the NaCl-treated samples. These genes were enriched in 10 KEGG pathways and 73 GO terms, and subjected to 25 COG categories. Using high-throughput next-generation sequencing, we built a database as a global transcript resource for Z. japonica Steud. roots. The results of this study will advance our understanding of the early salt response in Japanese lawngrass roots. PMID:26347751

Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS.

PubMed

Kwon, Ji-Sun; Kim, Jihye; Nam, Dougu; Kim, Sangsoo

2012-06-01

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.

Matching disease and phenotype ontologies in the ontology alignment evaluation initiative.

PubMed

Harrow, Ian; Jiménez-Ruiz, Ernesto; Splendiani, Andrea; Romacker, Martin; Woollard, Peter; Markel, Scott; Alam-Faruque, Yasmin; Koch, Martin; Malone, James; Waaler, Arild

2017-12-02

The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.

The Porifera Ontology (PORO): enhancing sponge systematics with an anatomy ontology.

PubMed

Thacker, Robert W; Díaz, Maria Cristina; Kerner, Adeline; Vignes-Lebbe, Régine; Segerdell, Erik; Haendel, Melissa A; Mungall, Christopher J

2014-01-01

Porifera (sponges) are ancient basal metazoans that lack organs. They provide insight into key evolutionary transitions, such as the emergence of multicellularity and the nervous system. In addition, their ability to synthesize unusual compounds offers potential biotechnical applications. However, much of the knowledge of these organisms has not previously been codified in a machine-readable way using modern web standards. The Porifera Ontology is intended as a standardized coding system for sponge anatomical features currently used in systematics. The ontology is available from http://purl.obolibrary.org/obo/poro.owl, or from the project homepage http://porifera-ontology.googlecode.com/. The version referred to in this manuscript is permanently available from http://purl.obolibrary.org/obo/poro/releases/2014-03-06/. By standardizing character representations, we hope to facilitate more rapid description and identification of sponge taxa, to allow integration with other evolutionary database systems, and to perform character mapping across the major clades of sponges to better understand the evolution of morphological features. Future applications of the ontology will focus on creating (1) ontology-based species descriptions; (2) taxonomic keys that use the nested terms of the ontology to more quickly facilitate species identifications; and (3) methods to map anatomical characters onto molecular phylogenies of sponges. In addition to modern taxa, the ontology is being extended to include features of fossil taxa.

Identification of Key Pathways and Genes in L4 Dorsal Root Ganglion (DRG) After Sciatic Nerve Injury via Microarray Analysis.

PubMed

Zhao, He; Duan, Li-Jun; Sun, Qing-Ling; Gao, Yu-Shan; Yang, Yong-Dong; Tang, Xiang-Sheng; Zhao, Ding-Yan; Xiong, Yang; Hu, Zhen-Guo; Li, Chuan-Hong; Chen, Si-Xue; Liu, Tao; Yu, Xing

2018-04-19

Peripheral nerve injury (PNI) has devastating consequences. Dorsal root ganglion as a pivotal locus participates in the process of neuropathic pain and nerve regeneration. In recent years, gene sequencing technology has seen rapid rise in the biomedicine field. So, we attempt to gain insight into in the mechanism of neuropathic pain and nerve regeneration in the transcriptional level and to explore novel genes through bioinformatics analysis. The gene expression profiles of GSE96051 were downloaded from GEO database. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. Our results showed that both IL-6 and Jun genes and the signaling pathway of MAPK, apoptosis, P53 present their vital modulatory role in nerve regeneration and neuropathic pain. Noteworthy, 13 hub genes associated with neuropathic pain and nerve regeneration, including Ccl12, Ppp1r15a, Cdkn1a, Atf3, Nts, Dusp1, Ccl7, Csf, Gadd45a, Serpine1, Timp1 were rarely reported in PubMed database, these genes may provide us the new orientation in experimental research and clinical study. Our results may provide more deep insight into the mechanism and a promising therapeutic target. The next step is to put our emphasis on an experiment level and to verify the novel genes from 13 hub genes.

Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration.

PubMed

Ong, Edison; Xiang, Zuoshuang; Zhao, Bin; Liu, Yue; Lin, Yu; Zheng, Jie; Mungall, Chris; Courtot, Mélanie; Ruttenberg, Alan; He, Yongqun

2017-01-04

Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

PubMed Central

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

2014-01-01

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

Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns.

PubMed

Xiang, Zuoshuang; Zheng, Jie; Lin, Yu; He, Yongqun

2015-01-01

It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format. Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development. With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

PubMed

Quesada-Martínez, M; Fernández-Breis, J T; Stevens, R; Mikroyannidi, E

2015-01-01

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

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

HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2014-01-01

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

Intrauterine growth restriction and placental gene expression in severe preeclampsia, comparing early-onset and late-onset forms.

PubMed

Nevalainen, Jaana; Skarp, Sini; Savolainen, Eeva-Riitta; Ryynänen, Markku; Järvenpää, Jouko

2017-10-26

To evaluate placental gene expression in severe early- or late-onset preeclampsia with intrauterine growth restriction compared to controls. Chorionic villus sampling was conducted after cesarean section from the placentas of five women with early- or late-onset severe preeclampsia and five controls for each preeclampsia group. Microarray analysis was performed to identify gene expression differences between the groups. Pathway analysis showed over-representation of gene ontology (GO) biological process terms related to inflammatory and immune response pathways, platelet development, vascular development, female pregnancy and reproduction in early-onset preeclampsia. Pathways related to immunity, complement and coagulation cascade were overrepresented in the hypergeometric test for the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Ten genes (ABI3BP, C7, HLA-G, IL2RB, KRBOX1, LRRC15, METTL7B, MPP5, RFLNB and SLC20A) had a ≥±1 fold expression difference in severe early-onset preeclampsia group compared to early controls. There were 362 genes that had a ≥±1 fold expression difference in severe early-onset preeclampsia group compared to late-onset preeclampsia group including ABI3BP, C7, HLA-G and IL2RB. There are significant differences in placental gene expression between severe early- and late-onset preeclampsia when both are associated with intrauterine growth restriction. ABI3BP, C7, HLA-G and IL2RB might contribute to the development of early form of severe preeclampsia.

Integrating transcriptome and genome re-sequencing data to identify key genes and mutations affecting chicken eggshell qualities.

PubMed

Zhang, Quan; Zhu, Feng; Liu, Long; Zheng, Chuan Wei; Wang, De He; Hou, Zhuo Cheng; Ning, Zhong Hua

2015-01-01

Eggshell damages lead to economic losses in the egg production industry and are a threat to human health. We examined 49-wk-old Rhode Island White hens (Gallus gallus) that laid eggs having shells with significantly different strengths and thicknesses. We used HiSeq 2000 (Illumina) sequencing to characterize the chicken transcriptome and whole genome to identify the key genes and genetic mutations associated with eggshell calcification. We identified a total of 14,234 genes expressed in the chicken uterus, representing 89% of all annotated chicken genes. A total of 889 differentially expressed genes were identified by comparing low eggshell strength (LES) and normal eggshell strength (NES) genomes. The DEGs are enriched in calcification-related processes, including calcium ion transport and calcium signaling pathways as revealed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Some important matrix proteins, such as OC-116, LTF and SPP1, were also expressed differentially between two groups. A total of 3,671,919 single-nucleotide polymorphisms (SNPs) and 508,035 Indels were detected in protein coding genes by whole-genome re-sequencing, including 1775 non-synonymous variations and 19 frame-shift Indels in DEGs. SNPs and Indels found in this study could be further investigated for eggshell traits. This is the first report to integrate the transcriptome and genome re-sequencing to target the genetic variations which decreased the eggshell qualities. These findings further advance our understanding of eggshell calcification in the chicken uterus.

Ontology-based, Tissue MicroArray oriented, image centered tissue bank

PubMed Central

Viti, Federica; Merelli, Ivan; Caprera, Andrea; Lazzari, Barbara; Stella, Alessandra; Milanesi, Luciano

2008-01-01

Background Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information. Results In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data. Conclusions Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes. PMID:18460177

Sexually Dimorphic Gene Expression Associated with Growth and Reproduction of Tongue Sole (Cynoglossus semilaevis) Revealed by Brain Transcriptome Analysis.

PubMed

Wang, Pingping; Zheng, Min; Liu, Jian; Liu, Yongzhuang; Lu, Jianguo; Sun, Xiaowen

2016-08-26

In this study, we performed a comprehensive analysis of the transcriptome of one- and two-year-old male and female brains of Cynoglossus semilaevis by high-throughput Illumina sequencing. A total of 77,066 transcripts, corresponding to 21,475 unigenes, were obtained with a N50 value of 4349 bp. Of these unigenes, 33 genes were found to have significant differential expression and potentially associated with growth, from which 18 genes were down-regulated and 12 genes were up-regulated in two-year-old males, most of these genes had no significant differences in expression among one-year-old males and females and two-year-old females. A similar analysis was conducted to look for genes associated with reproduction; 25 genes were identified, among them, five genes were found to be down regulated and 20 genes up regulated in two-year-old males, again, most of the genes had no significant expression differences among the other three. The performance of up regulated genes in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was significantly different between two-year-old males and females. Males had a high gene expression in genetic information processing, while female's highly expressed genes were mainly enriched on organismal systems. Our work identified a set of sex-biased genes potentially associated with growth and reproduction that might be the candidate factors affecting sexual dimorphism of tongue sole, laying the foundation to understand the complex process of sex determination of this economic valuable species.

Ontology for Structural Geology

NASA Astrophysics Data System (ADS)

Zhong, J.; McGuinness, D. L.; Antonellini, M.; Aydin, A.

2005-12-01

We present our comprehensive process-based ontology for Structural Geology. This ontology covers major domain concepts, especially those related to geological structure type, properties of these structures, their deformation mechanisms, and the factors that control which deformation mechanisms may operate under certain conditions. The structure class in our ontology extends the planetary structure class of the SWEET ontology by providing additional information required for use in the structural geology domain. The classification followed the architectures of structures, such as structure element, set, zone, and pattern. Our deformation mechanism class does not have a corresponding class in SWEET. In our ontology, it has two subclasses, Macro- and Micro- mechanisms. The property class and the factor class are both subclasses of the physical property class of SWEET. Relationships among those concepts are also included in our ontology. For example, the class structure element has properties associated with the deformation mechanisms, descriptive properties such as geometry and morphology, and physical properties of rocks such as strength, compressibility, seismic velocity, porosity, and permeability. The subject matter expertise was provided by domain experts. Additionally, we surveyed text books and journal articles with the goal of evaluating the completeness and correctness of the domain terms and we used logical reasoners and validators to eliminate logical problems. We propose that our ontology provides a reusable extension to the SWEET ontology that may be of value to scientists and lay people interested in structural geology issues. We have also implemented prototype services that utilize this ontology for search.

An Ontology of Therapies

NASA Astrophysics Data System (ADS)

Eccher, Claudio; Ferro, Antonella; Pisanelli, Domenico M.

Ontologies are the essential glue to build interoperable systems and the talk of the day in the medical community. In this paper we present the ontology of medical therapies developed in the course of the Oncocure project, aimed at building a guideline based decision support integrated with a legacy Electronic Patient Record (EPR). The therapy ontology is based upon the DOLCE top level ontology. It is our opinion that our ontology, besides constituting a model capturing the precise meaning of therapy-related concepts, can serve for several practical purposes: interfacing automatic support systems with a legacy EPR, allowing the automatic data analysis, and controlling possible medical errors made during EPR data input.

Monitoring the evolutionary aspect of the Gene Ontology to enhance predictability and usability.

PubMed

Park, Jong C; Kim, Tak-eun; Park, Jinah

2008-04-11

Much effort is currently made to develop the Gene Ontology (GO). Due to the dynamic nature of information it addresses, GO undergoes constant updates whose results are released at regular intervals as separate versions. Although there are a large number of computational tools to aid the development of GO, they are operating on a particular version of GO, making it difficult for GO curators to anticipate the full impact of particular changes along the time axis on a larger scale. We present a method for tapping into such an evolutionary aspect of GO, by making it possible to keep track of important temporal changes to any of the terms and relations of GO and by consequently making it possible to recognize associated trends. We have developed visualization methods for viewing the changes between two different versions of GO by constructing a colour-coded layered graph. The graph shows both versions of GO with highlights to those GO terms that are added, removed and modified between the two versions. Focusing on a specific GO term or terms of interest over a period, we demonstrate the utility of our system that can be used to make useful hypotheses about the cause of the evolution and to provide new insights into more complex changes. GO undergoes fast evolutionary changes. A snapshot of GO, as presented by each version of GO alone, overlooks such evolutionary aspects, and consequently limits the utilities of GO. The method that highlights the differences of consecutive versions or two different versions of an evolving ontology with colour-coding enhances the utility of GO for users as well as for developers. To the best of our knowledge, this is the first proposal to visualize the evolutionary aspect of GO.

Lentiviral gene ontology (LeGO) vectors equipped with novel drug-selectable fluorescent proteins: new building blocks for cell marking and multi-gene analysis.

PubMed

Weber, K; Mock, U; Petrowitz, B; Bartsch, U; Fehse, B

2010-04-01

Vector-encoded fluorescent proteins (FPs) facilitate unambiguous identification or sorting of gene-modified cells by fluorescence-activated cell sorting (FACS). Exploiting this feature, we have recently developed lentiviral gene ontology (LeGO) vectors (www.LentiGO-Vectors.de) for multi-gene analysis in different target cells. In this study, we extend the LeGO principle by introducing 10 different drug-selectable FPs created by fusing one of the five selection marker (protecting against blasticidin, hygromycin, neomycin, puromycin and zeocin) and one of the five FP genes (Cerulean, eGFP, Venus, dTomato and mCherry). All tested fusion proteins allowed both fluorescence-mediated detection and drug-mediated selection of LeGO-transduced cells. Newly generated codon-optimized hygromycin- and neomycin-resistance genes showed improved expression as compared with their ancestors. New LeGO constructs were produced at titers >10(6) per ml (for non-concentrated supernatants). We show efficient combinatorial marking and selection of various cells, including mesenchymal stem cells, simultaneously transduced with different LeGO constructs. Inclusion of the cytomegalovirus early enhancer/chicken beta-actin promoter into LeGO vectors facilitated robust transgene expression in and selection of neural stem cells and their differentiated progeny. We suppose that the new drug-selectable markers combining advantages of FACS and drug selection are well suited for numerous applications and vector systems. Their inclusion into LeGO vectors opens new possibilities for (stem) cell tracking and functional multi-gene analysis.

OMIT: dynamic, semi-automated ontology development for the microRNA domain.

PubMed

Huang, Jingshan; Dang, Jiangbo; Borchert, Glen M; Eilbeck, Karen; Zhang, He; Xiong, Min; Jiang, Weijian; Wu, Hao; Blake, Judith A; Natale, Darren A; Tan, Ming

2014-01-01

As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology.

OMIT: Dynamic, Semi-Automated Ontology Development for the microRNA Domain

PubMed Central

Huang, Jingshan; Dang, Jiangbo; Borchert, Glen M.; Eilbeck, Karen; Zhang, He; Xiong, Min; Jiang, Weijian; Wu, Hao; Blake, Judith A.; Natale, Darren A.; Tan, Ming

2014-01-01

As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology. PMID:25025130

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

Genome-wide screening of indicator genes for assessing the potential carcinogenic risk of Nanjing city drinking water.

PubMed

Zhang, Rui; Cheng, Shupei; Li, Aimin; Sun, Jie; Zhang, Yan; Zhang, Xuxiang

2011-07-01

Effects of all pollutants existing in the Nanjing city drinking water (DWNC) on mouse gene transcription levels were measured to assess the DWNC carcinogenic risks and to identify candidate indicator genes for assessing and early warning the cancer risks. Transcriptional expression levels of 14,000 hepatic genes for the treatment group mice (Mus musculus, ICR) fed with DWNC for 90 days were detected using the GeneChip(®) Mouse Genome 430A 2.0 array. The analysis indicated that the transcriptional levels of 294 genes were up-regulated and 542 ones were down-regulated. Of these genes, 12 ones identified to be involved in at least five different types of cancers were further analyzed. An interrogation by Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that three (including ITGAV, CCND1 and SMAD2) of the 12 genes were mapped to pathway in cancer. Gene Ontology (GO) function annotation also showed that they were associated with the functional categories of cell cycle regulation, adhesion, apoptosis, signal transduction and so on which are closely implicated in tumorigenesis and progression. The correlations between the aberrant expressions of them and the genesis and progression of cancers have been further documented by a number of scientific researches. These results might demonstrate that the potential toxicity and carcinogenic risks were associated with DWNC. Moreover, ITGAV, CCND1 and SMAD2 were identified as the most likely candidate indicator genes for the assessment of the combined carcinogenic risk of all pollutants existing in DWNC.

Towards Agile Ontology Maintenance

NASA Astrophysics Data System (ADS)

Luczak-Rösch, Markus

Ontologies are an appropriate means to represent knowledge on the Web. Research on ontology engineering reached practices for an integrative lifecycle support. However, a broader success of ontologies in Web-based information systems remains unreached while the more lightweight semantic approaches are rather successful. We assume, paired with the emerging trend of services and microservices on the Web, new dynamic scenarios gain momentum in which a shared knowledge base is made available to several dynamically changing services with disparate requirements. Our work envisions a step towards such a dynamic scenario in which an ontology adapts to the requirements of the accessing services and applications as well as the user's needs in an agile way and reduces the experts' involvement in ontology maintenance processes.