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Bioinformatics Advance Access published online on February 24, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl049
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 26, 2005
Revised December 23, 2005
Accepted February 7, 2006

Article

A novel sensitive method for the detection of user-defined compositional bias in biological sequences

Igor B. Kuznetsov 1 * and Seungwoo Hwang 1

1 Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, State University of New York, One Discovery Drive, Rensselaer, NY 12144, USA

* To whom correspondence should be addressed.
Igor B. Kuznetsov, E-mail: IKuznetsov{at}albany.edu


  Abstract

Motivation: Most biological sequences contain compositionally biased segments in which one or more residue types are significantly over-represented. The function and evolution of these segments are poorly understood. Usually, all types of compositionally biased segments are masked and ignored during sequence analysis. However, it has been shown for a number of proteins that biased segments that contain amino acids with similar chemical properties are involved in a variety of molecular functions and human diseases. A detailed large-scale analysis of the functional implications and evolutionary conservation of different compositionally biased segments requires a sensitive method capable of detecting user-specified types of compositional bias.

Results: We present BIAS, a novel sensitive method for the detection of compositionally biased segments composed of a user-specified set of residue types. BIAS uses the discrete scan statistics that provides a highly accurate correction for multiple tests to compute analytical estimates of the significance of each compositionally-biased segment. The method can take into account global compositional bias when computing analytical estimates of the significance of local clusters. BIAS is benchmarked against SEG, SAPS and CAST programs. We also use BIAS to show that groups of proteins with the same biological function are significantly associated with particular types of compositionally biased segments.

Availability: The software is available from {{http://lcg.rit.albany.edu/bias/}}.

Associate Editor: Christos Ouzounis
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I. B. Kuznetsov
ProBias: a web-server for the identification of user-specified types of compositionally biased segments in protein sequences
Bioinformatics, July 1, 2008; 24(13): 1534 - 1535.
[Abstract] [Full Text] [PDF]


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