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

By Yanhui Hu, I. Flockhart, Arunachalam Vinayagam, Clemens Bergwitz, Bonnie Berger, Norbert Perrimon, S. Mohr. BMC Bioinformatics, 2011

748 citations recorded by the source corpus.

BackgroundMapping 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.ResultsWe report a simple but effective tool, the Drosophila RNAi Screening Center I ntegrative O rtholog P rediction T ool (DIOPT; http://www.flyrnai.org/diopt), 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).ConclusionsDIOPT 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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/244da9e5d44d4f1b521fa79e9fe89df6f56b4d88

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Citations in the corpus

Alliance of Genome Resources Portal: unified model organism research platform (2019, methodology): “The ortholog inferences from the different methods have been integrated using the DRSC Integrative Ortholog Prediction Tool (DIOPT) (12).”
FlyBase: updates to the Drosophila genes and genomes database (2024, neutral): “Additionally, DIOPT 9.1 has expanded its species coverage to include new predictions for both Anopheles gambiae (African malaria mosquito) and Escherichia coli (enterobacterium).”
FlyBase: updates to the Drosophila melanogaster knowledge base (2020, methodology): “The new pipeline combines three sets of data: (i) D. melanogaster-tohuman orthology relationships from DIOPT (6); (ii) human gene-to-phenotype relationships from OMIM (14); and (iii) OMIM phenotype cross-references within the Disease Ontology (DO) (15).”
FlyBase: a guided tour of highlighted features (2022, methodology): “Homologs Supports searches of orthologs to human, Drosophila melanogaster, and 8 other model organism genes, and paralogs to Drosophila genes using DIOPT (Hu et al. 2011) which integrates multiple orthology prediction algorithms.”
FlyBase at 25: looking to the future (2016, methodology): “A similar presentation is used to show DIOPT and OrthoDB-derived orthology data within the ‘Orthologs’ section of D. melanogaster Gene Reports (not shown).”