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Bioinformatics Advance Access published online on May 8, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn210
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Microbial Genotype-Phenotype Mapping by Class Association Rule Mining

Makio Tamura a,* and Patrik D'haeseleer a

aLawrence Livermore National Laboratory, Computing Applications and Research Department / Chemistry, Materials, Earth, and Life Sciences Department, Microbial Systems Biology Group, Livermore, CA 94550

*To whom correspondence should be addressed. Dr. Makio Tamura, E-mail: makio323{at}gmail.com


  Abstract

Motivation: Microbial phenotypes are typically due to the concerted action of multiple gene functions, yet the presence of each gene may have only a weak correlation with the observed phenotype. Hence, it may be more appropriate to examine co-occurrence between sets of genes and a phenotype (multiple-to-one) instead of pairwise relations between a single gene and the phenotype. Here, we propose an efficient Class Association Rule mining algorithm, NETCAR, in order to extract sets of COGs (Clusters of Orthologous Groups of proteins) associated with a phenotype from COG phylogenetic profiles and a phenotype profile. NETCAR takes into account the phylogenetic cooccurrence graph between COGs to restrict hypothesis space, and uses mutual information to evaluate the biconditional relation.

Results: We examined the mining capability of pairwise and multiple-toone association by using NETCAR to extract COGs relevant to six microbial phenotypes (aerobic, anaerobic, facultative, endospore, motility, and Gram negative) from 11,969 unique COG profiles across 155 prokaryotic organisms. With the same level of False Discovery Rate (FDR), multiple-to-one association can extract about 10 times more relevant COGs than one-to-one association. We also reveal various topologies of association networks among COGs (modules) from extracted multiple-to-one correlation rules relevant with the six phenotypes; including a well-connected network for motility, a startshaped network for aerobic, and intermediate topologies for the other phenotypes. NETCAR outperforms a standard Class Association Rule mining algorithm, CARAPRIORI, while requiring several orders of magnitude less computational time for extracting 3-COG sets.

Availability: Source code of the Java implementation is available as Supplementary material at the Bioinformatics online website, or upon request to the author.

Contact: makio323{at}gmail.com

Associate Editor: Dr. Jonathan Wren

Received on October 8, 2007; revised on February 28, 2008; accepted on April 26, 2008

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