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|Title:||BBH-LS: An algorithm for computing positional homologs using sequence and gene context similarity||Authors:||Zhang, M.
|Issue Date:||16-Jul-2012||Citation:||Zhang, M., Leong, H.W. (2012-07-16). BBH-LS: An algorithm for computing positional homologs using sequence and gene context similarity. BMC Systems Biology 6 (SUPPL.1) : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1752-0509-6-S1-S22||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.Conclusion: 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. © 2012 Zhang and Leong; licensee BioMed Central Ltd.||Source Title:||BMC Systems Biology||URI:||http://scholarbank.nus.edu.sg/handle/10635/77829||ISSN:||17520509||DOI:||10.1186/1752-0509-6-S1-S22|
|Appears in Collections:||Staff Publications|
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