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Jennifer I. Clark

Bio: Jennifer I. Clark is an academic researcher from Wellcome Trust. The author has contributed to research in topics: Controlled vocabulary & Open Biomedical Ontologies. The author has an hindex of 4, co-authored 4 publications receiving 3683 citations. Previous affiliations of Jennifer I. Clark include European Bioinformatics Institute.

Papers
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Journal ArticleDOI
Midori A. Harris, Jennifer I. Clark1, Ireland A1, Jane Lomax1, Michael Ashburner1, Michael Ashburner2, R. Foulger2, R. Foulger1, Karen Eilbeck3, Karen Eilbeck1, Suzanna E. Lewis1, Suzanna E. Lewis3, B. Marshall1, B. Marshall3, Christopher J. Mungall3, Christopher J. Mungall1, J. Richter3, J. Richter1, Gerald M. Rubin1, Gerald M. Rubin3, Judith A. Blake1, Carol J. Bult1, Dolan M1, Drabkin H1, Janan T. Eppig1, Hill Dp1, L. Ni1, Ringwald M1, Rama Balakrishnan1, Rama Balakrishnan4, J. M. Cherry4, J. M. Cherry1, Karen R. Christie1, Karen R. Christie4, Maria C. Costanzo1, Maria C. Costanzo4, Selina S. Dwight4, Selina S. Dwight1, Stacia R. Engel4, Stacia R. Engel1, Dianna G. Fisk1, Dianna G. Fisk4, Jodi E. Hirschman4, Jodi E. Hirschman1, Eurie L. Hong1, Eurie L. Hong4, Robert S. Nash4, Robert S. Nash1, Anand Sethuraman1, Anand Sethuraman4, Chandra L. Theesfeld4, Chandra L. Theesfeld1, David Botstein5, David Botstein1, Kara Dolinski5, Kara Dolinski1, Becket Feierbach5, Becket Feierbach1, Tanya Z. Berardini6, Tanya Z. Berardini1, S. Mundodi1, S. Mundodi6, Seung Y. Rhee6, Seung Y. Rhee1, Rolf Apweiler1, Daniel Barrell1, Camon E1, E. Dimmer1, Lee1, Rex L. Chisholm, Pascale Gaudet1, Pascale Gaudet7, Warren A. Kibbe7, Warren A. Kibbe1, Ranjana Kishore1, Ranjana Kishore8, Erich M. Schwarz1, Erich M. Schwarz8, Paul W. Sternberg8, Paul W. Sternberg1, M. Gwinn1, Hannick L1, Wortman J1, Matthew Berriman1, Matthew Berriman9, Wood9, Wood1, de la Cruz N1, de la Cruz N10, Peter J. Tonellato10, Peter J. Tonellato1, Pankaj Jaiswal11, Pankaj Jaiswal1, Seigfried T12, Seigfried T1, White R1, White R13 
TL;DR: The Gene Ontology (GO) project as discussed by the authors provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.
Abstract: The Gene Ontology (GO) project (http://www.geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

3,565 citations

01 Jan 2004
TL;DR: The Gene Ontology (GO) project provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.
Abstract: The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

559 citations

Book ChapterDOI
15 Apr 2005
TL;DR: The main goal of the Gene Ontology (GO) project is to support the construction and use of structured, controlled vocabularies to address the growing need for meaningful annotation of genes and their products in different organisms.
Abstract: The main goal of the Gene Ontology (GO) project is to support the construction and use of structured, controlled vocabularies to address the growing need for meaningful annotation of genes and their products in different organisms. There are three key aspects of the GO project: the development of dynamic, controlled vocabularies that can be applied to all organisms even as knowledge of the roles of gene products is accumulating and changing; the application of GO terms in annotating genes or gene products; and the development and maintenance of databases and software for querying, displaying, and manipulating ontologies and associated annotation sets. The GO vocabularies are four nonoverlapping, structured networks of terms that describe key aspects of biology. Molecular function describes the activities or tasks performed by individual gene products at the molecular level; biological process describes broad biological goals that are accomplished by ordered assemblies of molecular functions; cellular component encompasses subcellular structures, locations, and macromolecular complexes; sequence ontology includes genome feature terms. The GO project's resources are available to the public at http://www.geneontology.org. Keywords: ontology; annotation; database; model organism; function; process; component; controlled vocabulary

179 citations

Journal ArticleDOI
TL;DR: The Gene Ontology facilitates the exchange of information between groups of scientists studying similar processes in different model organisms, and so provides a broad range of opportunities for plant scientists.
Abstract: The Gene Ontology project (http://www.geneontology.org/) produces structured, controlled vocabularies and gene product annotations. Gene products are classified according to the cellular locations and biological process in which they act, and the molecular functions that they carry out. We annotate gene products from a broad range of model species and provide support for those groups that wish to contribute annotation of further model species. The Gene Ontology facilitates the exchange of information between groups of scientists studying similar processes in different model organisms, and so provides a broad range of opportunities for plant scientists.

36 citations


Cited by
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Journal ArticleDOI
TL;DR: BioGRID is a freely accessible database of physical and genetic interactions that includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens.
Abstract: Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.

3,794 citations

Journal ArticleDOI
TL;DR: WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO annotation results, designed to deal with the directed acyclic graph structure of GO to facilitate histogram creation of Go annotation results.
Abstract: Unified, structured vocabularies and classifications freely provided by the Gene Ontology (GO) Consortium are widely accepted in most of the large scale gene annotation projects. Consequently, many tools have been created for use with the GO ontologies. WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO annotation results. Different from other commercial software for creating chart, WEGO is designed to deal with the directed acyclic graph structure of GO to facilitate histogram creation of GO annotation results. WEGO has been used widely in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO, along with the two other tools, namely External to GO Query and GO Archive Query, are freely available for all users at http://wego.genomics.org.cn. There are two available mirror sites at http://wego2.genomics.org.cn and http://wego.genomics.com.cn. Any suggestions are welcome at wego@genomics.org.cn.

2,460 citations

Journal ArticleDOI
TL;DR: Oncomine, a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community, provides an update on the initiative, describes the database and analysis modules, and highlight several notable observations.

1,905 citations

Journal ArticleDOI
21 Oct 2004-Nature
TL;DR: Genome analysis provides a greatly improved fish gene catalogue, including identifying key genes previously thought to be absent in fish, and reconstructs much of the evolutionary history of ancient and recent chromosome rearrangements leading to the modern human karyotype.
Abstract: Tetraodon nigroviridis is a freshwater puffer fish with the smallest known vertebrate genome. Here, we report a draft genome sequence with long-range linkage and substantial anchoring to the 21 Tetraodon chromosomes. Genome analysis provides a greatly improved fish gene catalogue, including identifying key genes previously thought to be absent in fish. Comparison with other vertebrates and a urochordate indicates that fish proteins have diverged markedly faster than their mammalian homologues. Comparison with the human genome suggests ∼900 previously unannotated human genes. Analysis of the Tetraodon and human genomes shows that whole-genome duplication occurred in the teleost fish lineage, subsequent to its divergence from mammals. The analysis also makes it possible to infer the basic structure of the ancestral bony vertebrate genome, which was composed of 12 chromosomes, and to reconstruct much of the evolutionary history of ancient and recent chromosome rearrangements leading to the modern human karyotype.

1,889 citations

Journal ArticleDOI
TL;DR: Two novel algorithms that improve GO group scoring using the underlying GO graph topology are presented and it is shown that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms.
Abstract: Motivation: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. Results: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods. Availability: topgo.bioinf.mpi-inf.mpg.de Contact: alexa@mpi-sb.mpg.de Supplementary Information: Supplementary data are available at Bioinformatics online.

1,843 citations