RBR: library-less repeat detection for ESTs

Abstract

Motivation: Repeat sequences in ESTs are a source of problems, in particular for clustering. ESTs are therefore commonly masked against a library of known repeats. High quality repeat libraries are available for the widely studied organisms, but for most other organisms the lack of such libraries is likely to compromise the quality of EST analysis.

Results: We present a fast, flexible and library-less method for masking repeats in EST sequences, based on match statistics within the EST collection. The method is not linked to a particular clustering algorithm. Extensive testing on datasets using different clustering methods and a genomic mapping as reference shows that this method gives results that are better than or as good as those obtained using RepeatMasker with a repeat library.

Availability: The implementation of RBR is available under the terms of the GPL from http://www.ii.uib.no/~ketil/bioinformatics

Contact: ketil.malde@bccs.uib.no

Supplementary information: Supplementary data are available at Bioinformatics online.