EGGSlicer: predicting biologically meaningful gene sets from gene clusters using gene ontology information

Authors:
Heejoon Chae;Kwangmin Choi;Sun Kim;Haleh Ashki
Affiliations:
Indiana University, Bloomington, Indiana;Indiana University, Bloomington, Indiana;Indiana University, Bloomington, Indiana;Florida State University, Tallahassee, Florida
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

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Abstract

Predicting conserved gene sets in genome is a very important problem and there are a number of algorithms developed for the task. Unfortunately, conserved gene cluster prediction results depend largely on the phylogenetic distance of genomes. In particular, the sizes of clusters for closely related genomes are large and the functions of these genes in each cluster are diverse. Due to the vast diversity in the functions of these genes, it is difficult to define the precise biological meaning of those genes. To address this problem, we have developed a comparative genomics approach to splitting large conserved gene clusters into functionally related sub-clusters using gene ontology information.