DIOPT Version 9

Website: Version 9

• Version 9 information

The identification of orthologs is commonly used for bioinformatics activities such as data mining and establishing models for human diseases. Moreover, our group notes that researchers analyzing the results of screens performed at the Drosophila RNAi Screening Center (DRSC) frequently wish to identify mammalian orthologs of the fly genes that were "hits" (positive results) in their screens.

In helping DRSC screeners to identify orthologs using existing tools and algorithms, we recognized a need for a user-friendly approach to viewing and comparing ortholog predictions obtained using different tools and algorithms. This was our motivation in developing DIOPT. To facilitate identification of orthologs specifically of human disease-associated genes, we further developed DIOPT-DIST. Information about our approaches to development of both tools is summarized below.

Many tools have emerged to meet the need to identify orthologs. However, low coverage and heterogeneity of these tools present an obstacle to scientists who want to identify a one or a few highest-confidence orthologs for a given gene of interest or conversely, want to cast a wide net and follow up on all possible orthologs of a gene.

Our goal is to provide an easy-to-use resource that facilitates summary, comparison and access to various sources of ortholog predictions. DIOPT integrates human, mouse, fly, worm, zebrafish and yeast ortholog predictions made by Ensembl Compara, HomoloGene, Inparanoid, Isobase, OMA, orthoMCL, Phylome, RoundUp, and TreeFam. DIOPT lets users find ortholog pairs for a specified gene or genes identified by one, many or all of these published approaches. This provides a streamlined method for integration, comparison and access to orthology predictions originating from algorithms based on sequence homology, phylogenetic trees, and functional similarity. DIOPT calculates a simple score indicating the number of tools that support a given orthologous gene-pair relationship, as well as a weighted score based on functional assessment using high quality GO molecular function annotation of all fly-human orthologous pairs predicted by each tool. Differences in the algorithms used by each tool to predict orthologous relationship is one source of difference in the set of predictions made by one tool versus another. However, we also note that some of these differences might be attributable to use of different genome annotation releases used by some tools versus others, and that not all tools cover all of the species that we include in the DIOPT tool (see Tables 1,2 and 3).

DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. These should help you to identify the most appropriate matches among multiple possible orthologs.

The following summary figures and tables help to explain our approach and summarize the tools and algorithms included in DIOPT.

Table 1: Summary Information and Publications for the Tools Integrated in DIOPT

Table 2A: Genome Release Information for the Tools Integrated in DIOPT (Version 9)

DIOPT weights were calculated based on the mean semantic similarity of high quality GO molecular function annotation of all fly-human orthologous pairs predicted by each tool from DIOPT vs1. As such, they do not predict overall performance of an algorithm. Please go to the DIOPT publication for detailed information

(https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-357).

You can access diopt data through an API call. For details see:

https://www.flyrnai.org/tools/diopt/web/api