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Bioinformatics Advance Access published online on January 21, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp047
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Gclust: trans-kingdom classification of proteins using automatic individual threshold setting

Naoki Sato 1,*

1 Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, Komaba, Meguro-ku, Tokyo, 153-8902, Japan

*To whom correspondence should be addressed. Dr. Naoki Sato, E-mail: naokisat{at}bio.c.u-tokyo.ac.jp


  Abstract

Motivation: Trans-kingdom protein clustering remained difficult because of large sequence divergence between eukaryotes and prokaryotes and the presence of a transit sequence in organellar proteins. A large-scale protein clustering including such divergent organisms needs a heuristic to efficiently select similar proteins by setting a proper threshold for homologs of each protein. Here a method is described using two similarity measures and organism count.

Results: The Gclust software constructs minimal homolog groups using all-against-all BLASTP results by single-linkage clustering. Major points include (1) estimation of domain structure of proteins, (2) exclusion of multi-domain proteins, (3) explicit consideration of transit peptides, and (4) heuristic estimation of a similarity threshold for homologs of each protein by entropy-optimized organism count method. The resultant clusters were evaluated in the light of power law. The software was used to construct protein clusters for up to 95 organisms.

Availability: Software and data are available at http://gclust.c.u-tokyo.ac.jp/Gclust_Download.html.

Contact: naokisat{at}bio.c.u-tokyo.ac.jp

Associate Editor: Prof. Martin Bishop

Received on December 16, 2008; revised on January 16, 2009; accepted on January 16, 2009

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