An integrative approach to ortholog prediction for disease-focused and other functional studies
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Hu-2011-An integrative approach to ortholog prediction.pdf
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Author(s)
Hu, Yanhui
Flockhart, Ian
Vinayagam, Arunachalam
Bergwitz, Clemens
Perrimon, Norbert
Mohr, Stephanie E.
Berger, Bonnie
Date Issued
August 2011
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Citation
Hu, 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.
Version
Final published version
Abstract
Background
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1186/1471-2105-12-357
