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Open Access Research article

Predicting genome-wide redundancy using machine learning

Huang-Wen Chen1, Sunayan Bandyopadhyay12, Dennis E Shasha1 and Kenneth D Birnbaum3*

Author Affiliations

1 Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10003 USA

2 Dept of Computer Science & Engineering, University of Minnesota - Twin Cities, 200 Union St SE, Minneapolis MN 55455 USA

3 Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003 USA

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BMC Evolutionary Biology 2010, 10:357  doi:10.1186/1471-2148-10-357

Published: 18 November 2010

Abstract

Background

Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here.

Results

Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1), suggesting that redundancy is stable over long evolutionary periods.

Conclusions

Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.