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dc.contributor.authorHu, Yanhui
dc.contributor.authorFlockhart, Ian
dc.contributor.authorVinayagam, Arunachalam
dc.contributor.authorBergwitz, Clemens
dc.contributor.authorPerrimon, Norbert
dc.contributor.authorMohr, Stephanie E.
dc.contributor.authorBerger, Bonnie
dc.date.accessioned2012-03-09T19:11:40Z
dc.date.available2012-03-09T19:11:40Z
dc.date.issued2011-08
dc.date.submitted2011-05
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/1721.1/69633
dc.description.abstractBackground Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt webcite), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist webcite). Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.en_US
dc.description.sponsorshipHarvard Catalysten_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH R01 GM067761)en_US
dc.description.sponsorshipNational Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (5K08DK78361)en_US
dc.description.sponsorshipDana-Farber/Harvard Cancer Centeren_US
dc.language.isoen_US
dc.publisherSpringer (Biomed Central Ltd.)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/1471-2105-12-357en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.sourceBioMed Centralen_US
dc.titleAn integrative approach to ortholog prediction for disease-focused and other functional studiesen_US
dc.typeArticleen_US
dc.identifier.citationHu, Yanhui et al. “An Integrative Approach to Ortholog Prediction for Disease-focused and Other Functional Studies.” BMC Bioinformatics 12.1 (2011): 357. Web. 9 Mar. 2012.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.contributor.approverBerger Leighton, Bonnie
dc.contributor.mitauthorBerger, Bonnie
dc.relation.journalBMC Bioinformaticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsHu, Yanhui; Flockhart, Ian; Vinayagam, Arunachalam; Bergwitz, Clemens; Berger, Bonnie; Perrimon, Norbert; Mohr, Stephanie Een
dc.identifier.orcidhttps://orcid.org/0000-0002-2724-7228
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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