BBH-LS: an algorithm for computing positional homologs using sequence and gene context similarity

Identifying corresponding genes (orthologs) in different species is an important step in genome-wide comparative analysis. In particular, one-to-one correspondences between genes in different species greatly simplify certain problems such as transfer of function annotation and genome rearrangement studies. Positional homologs are the direct descendants of a single ancestral gene in the most recent common ancestor and by definition form one-to-one correspondence.

In this work, we present a simple yet effective method (BBH-LS) for the identification of positional homologs from the comparative analysis of two genomes. Our BBH-LS method integrates sequence similarity and gene context similarity in order to get more accurate ortholog assignments. Specifically, BBH-LS applies the bidirectional best hit heuristic to a combination of sequence similarity and gene context similarity scores.

We applied our method to the human, mouse, and rat genomes and found that BBH-LS produced the best results when using both sequence and gene context information equally. Compared to the state-of-the-art algorithms, such as MSOAR2, BBH-LS is able to identify more positional homologs with fewer false positives.