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Nucleic Acids Research Advance Access published online on February 20, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn064
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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.

Computational Biology

Empirical comparison of ab initio repeat finding programs

Surya Saha1,2,3, Susan Bridges1,3, Zenaida V. Magbanua2,3,4 and Daniel G. Peterson2,3,4,*

1Department of Computer Science and Engineering, 2Mississippi Genome Exploration Laboratory, 3Institute for Digital Biology and 4Department of Plant & Soil Sciences, Mississippi State University, Mississippi State, MS 39762, USA

*To whom correspondence should be addressed. Tel: +1 662 325 2747; Fax: +1 662 325 8742; Email: dpeterson{at}pss.msstate.edu

Received November 5, 2007. Revised January 30, 2008. Accepted January 31, 2008.

Identification of dispersed repetitive elements can be difficult, especially when elements share little or no homology with previously described repeats. Consequently, a growing number of computational tools have been designed to identify repetitive elements in an ab initio manner, i.e. without using prior sequence data. Here we present the results of side-by-side evaluations of six of the most widely used ab initio repeat finding programs. Using sequence from rice chromosome 12, tools were compared with regard to time requirements, ability to find known repeats, utility in identifying potential novel repeats, number and types of repeat elements recognized and compactness of family descriptions. The study reveals profound differences in the utility of the tools with some identifying virtually their entire substrate as repetitive, others making reasonable estimates of repetition, and some missing almost all repeats. Of note, even when tools recognized similar numbers of repeats they often showed marked differences in the nature and number of repeat families identified. Within the context of this comparative study, ReAS and RepeatScout showed the most promise in analysis of sequence reads and assembled genomic regions, respectively. Our results should help biologists identify the program(s), if any, that is best suited for their needs.


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