Journal Article

T-REKS: identification of Tandem REpeats in sequences with a K-meanS based algorithm

Julien Jorda and Andrey V. Kajava

in Bioinformatics

Volume 25, issue 20, pages 2632-2638
Published in print October 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp482
T-REKS: identification of Tandem REpeats in sequences with a K-meanS based algorithm

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Motivation: Over the last years a number of evidences have been accumulated about high incidence of tandem repeats in proteins carrying fundamental biological functions and being related to a number of human diseases. At the same time, frequently, protein repeats are strongly degenerated during evolution and, therefore, cannot be easily identified. To solve this problem, several computer programs which were based on different algorithms have been developed. Nevertheless, our tests showed that there is still room for improvement of methods for accurate and rapid detection of tandem repeats in proteins.

Results: We developed a new program called T-REKS for ab initio identification of the tandem repeats. It is based on clustering of lengths between identical short strings by using a K-means algorithm. Benchmark of the existing programs and T-REKS on several sequence datasets is presented. Our program being linked to the Protein Repeat DataBase opens the way for large-scale analysis of protein tandem repeats. T-REKS can also be applied to the nucleotide sequences.

Availability: The algorithm has been implemented in JAVA, the program is available upon request at http://bioinfo.montp.cnrs.fr/?r=t-reks. Protein Repeat DataBase generated by using T-REKS is accessible at http://bioinfo.montp.cnrs.fr/?r=repeatDB.

Contact: julien.jorda@crbm.cnrs.fr; andrey.kajava@crbm.cnrs.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5059 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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