http://precedings.nature.com/tags/gene%20ontology Recently posted documents tagged with 'gene ontology' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3639.1 AbstractPhenotypic data are routinely used to elucidate gene and protein function in most organisms amenable to experimental manipulation. However, although phenotype ontologies exist for many eukaryotic model organisms, no standardized system exists for the capture of phenotypic information in bacteria. We propose to build an Ontology of Microbial Phenotypes and use it to annotate the prokaryotic model organism Escherichia coli.IntroductionPhenotypes are the observable characteristics of an organism that result from the combination of a particular genotype and a particular environment, and thus are a basic and fundamental aspect of the biology of all organisms. The awesome power of genetics is founded on how the phenotypes of mutant genes, alone and in combination, contribute to understanding the biology of affected systems. To fully exploit the power of phenotypes for functional and comparative genomics, the ability to make comparisons across datasets and systems is vital. Making these comparisons either manually or computationally is hindered by the fact that phenotypes are not described consistently for bacteria. Our project aims to develop annotation infrastructure to improve the ability of microbiologists and bioinformaticians to use both existing and new phenotype information and to capture it in a consistent and standardized manner. This will require two key components: 1) an Ontology of Microbial Phenotypes (OMP) that captures phenotype descriptions in a controlled vocabulary, and 2) a set of evidence codes based on extension of the existing Evidence Code Ontology,1 with links to a database of papers and other resources describing the assays used to “measure” these phenotypes.Results We have explored two parallel approaches to building the OMP. Both are pre-coordinated approaches that rely on using the terms in the Phenotypic Quality Ontology (PATO) as a basis for building up phenotype terms.2 In the first approach we read 100 papers and identified 40 phenotypes described in those papers. We organized the 40 phenotypes into a controlled vocabulary using OBO-Edit.3 While this effort was not comprehensive, we were able to classify the 40 phenotypes into five superclasses and assign PATO entities and qualities. In addition, various assays (biochemical, morphological, and physiological) were collected from the papers that were curated to generate phenotype terms. In the second approach we generated a cross product between a selection of PATO terms and two GO nodes relevant to microbial phenotypes, “GO:0044262 : cellular carbohydrate metabolic process” and “GO:0006520 : cellular amino acid metabolic process.” We found the cross product generation method to be quite effective in generating large numbers of relevant terms quickly.ConclusionThe manual and cross product efforts were undertaken independently and in parallel by separate members of the group to see what, if any, consistency would be achieved. We found that although the concepts captured were similar, the different researchers chose different PATO quality terms to represent the same concepts. The manual curator chose “abnormal,” while the person working on cross products chose “abolished” and “disrupted.” The results of this exercise illustrate one reason why the pre-coordinated approach has advantages over the post-coordinated approach. In the post-coordinated approach separate annotators creating phenotype annotations at different points in time may choose different ways of expressing the same concept and thus create inconsistency. In the pre-coordinated approach, one controlled set of PATO terms will be used for term generation, and the fact of storing all the terms in one controlled vocabulary will enforce consistency and uniformity.Future DirectionsIf our project is funded, we plan to expand our cross product generation by targeting relevant nodes in the GO and other ontologies. We will extend ECO to include terms that capture the assays used in phenotype analysis. We will apply the OMP and extended ECO to the annotation of Eschericia coli and make the data available using EcoliWiki and other resources.References1. http://www.obofoundry.org/cgi-bin/detail.cgi?id=evidence_code2. http://obofoundry.org/wiki/index.php/PATO:Main_Page3. Day-Richter J, Harris MA, Haendel M, The Gene Ontology OBO-Edit Working Group, and Lewis S. OBO-Edit—an ontology editor for biologists. Bioinformatics. 2007;23(16):2198-2200. http://dx.doi.org/10.1038/npre.2009.3639.1 Wed, 19 Aug 2009 09:20:04 UTC Development of an Ontology of Microbial Phenotypes (OMP) doi:10.1038/npre.2009.3639.1 2009-08-19 Marcus Chibucos Nature Precedings 2009-08-19T09:20:04Z Poster Microbiology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3615/version/1 We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes 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, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking 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. http://precedings.nature.com/documents/3615/version/1 Fri, 14 Aug 2009 20:52:16 UTC Using Ontology Fingerprints to evaluate genome-wide association study results hdl:10101/npre.2009.3615.1 2009-08-14 W. Jim Zheng Nature Precedings 2009-08-14T20:52:16Z Manuscript Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3556.1 We are creating a gold-standard corpus of manually annotated full-text biomedical journal articles toward natural-language-processing applications. Central to this is our use of entire ontologies of the Open Biomedical Ontologies initiative as well as other terminologies as term sources, in contrast to most other such annotation projects, which have used small, ad hoc schemas. In addition to the standard difficulties in such annotation projects, each of the terminologies we have used has idiosyncrasies and ambiguities that present further challenges to consistent, high-quality annotation of these articles. In this paper we present and discuss the most salient of these with regard to the Gene Ontology that we have encountered and addressed in our annotation guidelines and training. The utility of these guidelines can be seen in the high and still-increasing interannotator-agreement statistics that we continually monitor. http://dx.doi.org/10.1038/npre.2009.3556.1 Thu, 06 Aug 2009 14:33:25 UTC Using the Gene Ontology to Annotate Biomedical Journal Articles doi:10.1038/npre.2009.3556.1 2009-08-06 Michael Bada Nature Precedings 2009-08-06T14:33:25Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3511.1 We developed a Bayesian hierarchical model to identify gene networks based on the similarity score generated from comparing the gene ontology fingerprints of gene pairs. Genes in this network were assumed to have similar biological functions that can be indicated by their ontology fingerprints. Our results indicate that different pathways show consistent score threshold that allow us to distinguish biological relevant gene—gene connections in the network. http://dx.doi.org/10.1038/npre.2009.3511.1 Wed, 29 Jul 2009 19:54:22 UTC A Bayesian Hierarchical Model to Derive Novel Gene Networks from Gene Ontology Fingerprints doi:10.1038/npre.2009.3511.1 2009-07-29 Jim Zheng Nature Precedings 2009-07-29T19:54:22Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3513.1 We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes 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, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking 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. http://dx.doi.org/10.1038/npre.2009.3513.1 Wed, 29 Jul 2009 19:53:06 UTC Using Ontology Fingerprints to Evaluate Genome-wide Association Results doi:10.1038/npre.2009.3513.1 2009-07-29 Jim Zheng Nature Precedings 2009-07-29T19:53:06Z Presentation Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3496/version/1 The Gene Ontology is being normalized and extended to include computable logical definitions. These definitions are partitioned into mutually exclusive cross-product sets, many of which reference other OBO Foundry ontologies. The results can be used to reason over the ontology, and to make cross-ontology queries. http://precedings.nature.com/documents/3496/version/1 Wed, 29 Jul 2009 02:04:15 UTC Cross-Product Extensions of the Gene Ontology hdl:10101/npre.2009.3496.1 2009-07-29 Christopher J. Mungall Nature Precedings 2009-07-29T02:04:15Z Manuscript Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3262.1 A major challenge for the development of resources for functional and comparative genomics is the extraction of data from the biomedical literature. Although text retrieval and extraction for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases.In September 2008, Mouse Genome Informatics (MGI) at The Jackson Lab initiated a search for dictionary-based text mining tools that we could integrate into our curation workflow. MGI has rigorous document triage and annotation procedures designed to identify articles about mouse genome biology and determine whether those articles should be curated. We currently screens approximately 1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we don’t foresee that human curation tasks can be fully automated in the near future, we are eager to implement entity name recognition and gene tagging tools that can help streamline our curation workflow and simplify gene indexing tasks in the MGI system. In this presentation, we discuss our search process and the steps we took to identify a short list of potential tools for further evaluation. We present our performance metrics and success criteria, and pilot projects in progress. The primary applications under current review are Fraunhofer SCAI’s ProMiner and NCBO’s Open-Biomedical Annotator. http://dx.doi.org/10.1038/npre.2009.3262.1 Wed, 20 May 2009 21:16:19 UTC Integrating Text Mining into the MGI Biocuration Workflow doi:10.1038/npre.2009.3262.1 2009-05-20 Karen G. Dowell Nature Precedings 2009-05-20T21:16:19Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3214.1 Within the UniProt consortium, the UniProtKB/Swiss-Prot knowledge base provides the international community with a stable, comprehensive, fully classified, richly and accurately annotated protein sequence database that is fully operable with other databases. Annotation relates to function(s) of the protein (their catalytic activity and the corresponding metabolic pathway(s) in which the protein may be involved), their cellular location, their interactions with other cellular components, etc. It is challenging to unify the way we annotate proteins, to ensure consistency and to describe data unambiguously. It is also highly valuable both for querying the database and for analyzing high-throughput data (expression data for instance). Because it is of fundamental importance to use standardized nomenclatures, annotations in UniProtKB/Swiss-Prot are progressively moving towards controlled vocabularies (CVs) and ontologies. Controlled vocabulary – or terminology – provides a list of concepts and text descriptions of their meaning. Concepts in a CV are often organized in a hierarchy. Ontology provides a formal representation of knowledge with definitions of concepts, their attributes and relations between them.As an illustration, we will describe the processes used to produce SUBCELLULAR and PATHWAY annotation sections in UniProtKB/Swiss-Prot. The CVs used in these two sections are based on in-house resources, UniProt subcell1 and UniPathway2 respectively. The links between these resources and other existing resources will be presented too, with a specific focus on Gene Ontology3 as we envisage using it extensively in order to describe protein functions or other biological processes. http://dx.doi.org/10.1038/npre.2009.3214.1 Fri, 08 May 2009 14:29:59 UTC Standardization in UniProtKB/Swiss-Prot doi:10.1038/npre.2009.3214.1 2009-05-08 Serenella Ferro Rojas Nature Precedings 2009-05-08T14:29:59Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3176.1 OBJECTIVES: We report on a experiment to functionally annotate PubChem assays using the Gene Ontology Categorizer (GOCat). METHODS and MATERIALS: The assays are processed to filter out non functional information, then the textual content is sent to the categorizer, which provide a ranked list of GO descriptors based on a GOA-learned association model. RESULTS: The GO descriptors are regarded as irrelevant for only 18% of the assays. CONCLUSION: Semantic enrichment of PubChem assays with functional genomics categories seems effective at least to navigate PubChem assays. AVAILABILITIES: the database is hosted by the University of Indiana at http://goassay.rguha.net/index.html the GO categorizer interface and service is available at the University of Geneva http://eagl.unige.ch/EAGLi/. http://dx.doi.org/10.1038/npre.2009.3176.1 Tue, 28 Apr 2009 08:47:47 UTC GOAssay: from Gene Ontology to Assays IDentifiers – Towards Automatic Functional Annotation of PubChem BioAssays doi:10.1038/npre.2009.3176.1 2009-04-28 Patrick Ruch Nature Precedings 2009-04-28T08:47:47Z Poster Chemistry Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3186.1 In contrast with the centrally-organised curation of the Gene Ontology, many biological ontologies are developed by loosely-organised groups who develop their ontology remotely. These groups tend to be formed from scientists and bio-informaticians from research groups with a common interest, who want to create a resource that will be useful to the community, rather than being formally mandated. Until recently, technological support for bio-ontology development relied on stand-alone editors running on users’ desk- tops for creating new ontology versions (e.g. OBO-Edit, COBrA and Protégé) and on private email, email lists and perhaps Wikis for the distribution of ontology files and discussions. Clearly, much better use could be made of the storage, versioning and visualisation techniques being developed by the database and e- Science communities. BioSphere is an online ontology editor supporting multiple users and is underpinned by a server that stores versions (in OWL-XML) and provides a discussion portal. http://dx.doi.org/10.1038/npre.2009.3186.1 Mon, 27 Apr 2009 19:42:27 UTC Grid technology for collaborative ontology development doi:10.1038/npre.2009.3186.1 2009-04-27 Stuart Aitken Nature Precedings 2009-04-27T19:42:27Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/