title: BBH-LS: an algorithm for computing positional homologs using sequence and gene context similarity.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Computing, National University of Singapore, 13 Computing Drive, Singapore, Republic of Singapore.
authors.forename: Melvin
authors.initials: M
authors.lastname: Zhang
authors.forename: Hon Wai
authors.initials: HW
authors.lastname: Leong
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D004285:Dogs; D023281:Genomics / Q000379:methods*; D006801:Humans; D051379:Mice; D051381:Rats; D012689:Sequence Homology, Nucleic Acid