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Nucleic Acids Research 2006 34(2):647-658; doi:10.1093/nar/gkj448
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Published online 25 January 2006

© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Article

Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple genomes

Ikuo Uchiyama*

National Institute for Basic Biology, National Institutes of Natural Sciences Nishigonaka 38, Myodaiji, Okazaki, Aichi 444-8585 Japan

*Tel: +81 564 55 7629; Fax: +81 564 55 7625; Email: uchiyama{at}nibb.ac.jp

Received November 13, 2005. Revised December 22, 2005. Accepted January 3, 2006.

Ortholog identification is a crucial first step in comparative genomics. Here, we present a rapid method of ortholog grouping which is effective enough to allow the comparison of many genomes simultaneously. The method takes as input all-against-all similarity data and classifies genes based on the traditional hierarchical clustering algorithm UPGMA. In the course of clustering, the method detects domain fusion or fission events, and splits clusters into domains if required. The subsequent procedure splits the resulting trees such that intra-species paralogous genes are divided into different groups so as to create plausible orthologous groups. As a result, the procedure can split genes into the domains minimally required for ortholog grouping. The procedure, named DomClust, was tested using the COG database as a reference. When comparing several clustering algorithms combined with the conventional bidirectional best-hit (BBH) criterion, we found that our method generally showed better agreement with the COG classification. By comparing the clustering results generated from datasets of different releases, we also found that our method showed relatively good stability in comparison to the BBH-based methods.


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I. Uchiyama
MBGD: a platform for microbial comparative genomics based on the automated construction of orthologous groups
Nucleic Acids Res., January 12, 2007; 35(suppl_1): D343 - D346.
[Abstract] [Full Text] [PDF]


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