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Ortholog Detection Using the Reciprocal Smallest Distance Algorithm

Protocol
Part of the Methods In Molecular Biology™ book series (MIMB, volume 396)

Summary

All protein coding genes have a phylogenetic history that when understood can lead to deep insights into the diversification or conservation of function, the evolution of developmental complexity, and the molecular basis of disease. One important part to reconstructing the relationships among genes in different organisms is an accurate method to find orthologs as well as an accurate measure of evolutionary diversification. The present chapter details such a method, called the reciprocal smallest distance algorithm (RSD). This approach improves upon the common procedure of taking reciprocal best Basic Local Alignment Search Tool hits (RBH) in the identification of orthologs by using global sequence alignment and maximum likelihood estimation of evolutionary distances to detect orthologs between two genomes. RSD finds many putative orthologs missed by RBH because it is less likely to be misled by the presence of close paralogs in genomes. The package offers a tremendous amount of flexibility in investigating parameter settings allowing the user to search for increasingly distant orthologs between highly divergent species, among other advantages. The flexibility of this tool makes it a unique and powerful addition to other available approaches for ortholog detection.

Keywords

Orthologs orthology reciprocal smallest distance reciprocal BLAST hit maximum likelihood molecular phylogenetics phylogeny 
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Notes

Acknowledgments

Many thanks to past and present members of the Computational Biology Initiative who provided advice and expertise, I-Hsien Wu, Tom Monaghan, Jian Pu, Saurav Singh, and Leon Peshkin. This material is based upon work supported by the National Science Foundation under Grant No. DBI 0543480.

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Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  1. 1.Department of Systems BiologyHarvard Medical SchoolHarvard
  2. 2.Department of Systems BiologyHarvard Medical SchoolUSA

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