Open Access Highly Accessed Research article

An integrative approach to ortholog prediction for disease-focused and other functional studies

Yanhui Hu1, Ian Flockhart1, Arunachalam Vinayagam1, Clemens Bergwitz2, Bonnie Berger3, Norbert Perrimon14 and Stephanie E Mohr1*

Author Affiliations

1 Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

2 Endocrine Unit, Massachusetts General Hospital, 50 Blossom Street, Boston MA 02114, USA

3 Math Department and Computer Science and Artificial Intelligence Laboratory, MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USA

4 Howard Hughes Medical Institute, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

For all author emails, please log on.

BMC Bioinformatics 2011, 12:357  doi:10.1186/1471-2105-12-357

Published: 31 August 2011

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

    D
rosophila RNAi Screening Center
    I
ntegrative
    O
rtholog
    P
rediction
    T
ool (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.