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      B2G-FAR, a species-centered GO annotation repository

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          Abstract

          Motivation: Functional genomics research has expanded enormously in the last decade thanks to the cost reduction in high-throughput technologies and the development of computational tools that generate, standardize and share information on gene and protein function such as the Gene Ontology (GO). Nevertheless, many biologists, especially working with non-model organisms, still suffer from non-existing or low-coverage functional annotation, or simply struggle retrieving, summarizing and querying these data.

          Results: The Blast2GO Functional Annotation Repository (B2G-FAR) is a bioinformatics resource envisaged to provide functional information for otherwise uncharacterized sequence data and offers data mining tools to analyze a larger repertoire of species than currently available. This new annotation resource has been created by applying the Blast2GO functional annotation engine in a strongly high-throughput manner to the entire space of public available sequences. The resulting repository contains GO term predictions for over 13.2 million non-redundant protein sequences based on BLAST search alignments from the SIMAP database. We generated GO annotation for approximately 150 000 different taxa making available 2000 species with the highest coverage through B2G-FAR. A second section within B2G-FAR holds functional annotations for 17 non-model organism Affymetrix GeneChips.

          Conclusions: B2G-FAR provides easy access to exhaustive functional annotation for 2000 species offering a good balance between quality and quantity, thereby supporting functional genomics research especially in the case of non-model organisms.

          Availability: The annotation resource is available at http://www.b2gfar.org.

          Contact: aconesa@ 123456cipf.es ; sgoetz@ 123456cipf.es

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references18

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes.

            We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.
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              The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

              The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                1 April 2011
                18 February 2011
                18 February 2011
                : 27
                : 7
                : 919-924
                Affiliations
                1Bioinformatics and Genomics Department, Centro de Investigaciones Príncipe Felipe (CIPF), Valencia, Spain, 2Department of Genome Oriented Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany, 3CIBER de Enfermedades Raras (CIBERER), 4Functional Genomics Node (National Institute for Bioinformatics, INB), Centro de Investigaciones Príncipe Felipe (CIPF), Valencia, Spain and 5Department of Computational Systems Biology, Ecology Centre, University of Vienna, 1090 Vienna, Austria
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: John Quackenbush

                Article
                btr059
                10.1093/bioinformatics/btr059
                3065692
                21335611
                54e64991-05dc-43ee-a472-65b122e471d0
                © The Author(s) 2011. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 September 2010
                : 4 January 2011
                : 1 February 2011
                Categories
                Original Papers
                Genome Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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