Skip Navigation



Bioinformatics Advance Access published online on October 26, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl537
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/24/2996    most recent
btl537v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Mendes, N. D.
Right arrow Articles by Freitas, A. T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Mendes, N. D.
Right arrow Articles by Freitas, A. T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 26, 2006
Revised September 29, 2006
Accepted October 13, 2006

Article

MUSA: a parameter free algorithm for the identification of biologically significant motifs

Nuno D. Mendes 1, Ana C. Casimiro 1, Pedro M. Santos 2, Isabel Sá-Correia 2, Arlindo L. Oliveira 1, and Ana T. Freitas 1 *

1 INESC-ID, Instituto Superior Técnico, Rua Alves Redol, 9 1000-029 Lisboa, Portugal
2 Biological Sciences Research Group, Centro de Engenharia Biológica e Qúymica, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

* To whom correspondence should be addressed.
Ana T. Freitas, E-mail: atf{at}inesc-id.pt


  Abstract

Motivation: The ability to identify complex motifs, i.e., non-contiguous nucleotide sequences, is a key feature of modern motif finders. Addressing this problem is extremely important, not only because these motifs can accurately model biological phenomena but because its extraction is highly dependent upon the appropriate selection of numerous search parameters.

Currently available combinatorial algorithms have proved to be highly efficient in exhaustively enumerating motifs (including complex motifs) which fulfill certain extraction criteria. However, one major problem with these methods is the large number of parameters that need to be specified.

Results: We propose a new algorithm, MUSA (Motif finding using an UnSupervised Approach), that can be used either to autonomously find over-represented complex motifs or to estimate search parameters for modern motif finders.

This method relies on a biclustering algorithm that operates on a matrix of co-occurrences of small motifs. The performance of this method is independent of the composite structure of the motifs being sought, making few assumptions about their characteristics.

The MUSA algorithm was applied to two data sets involving the bacterium Pseudomonas putida KT2440. The first one was composed of {sigma}54-dependent promoter sequences and the second data set included 54 promoter sequences of up-regulated genes in response to phenol, as suggested by quantitative proteomics. The results obtained indicate that this approach is very effective at identifying complex motifs of biological significance.

Availability: The MUSA algorithm is available upon request from the authors, and will be made available via a Web based interface.

Supplementary information: An appendix is available at http://algos.inesc-id.pt/~atf under ‘Papers on-line’.

Associate Editor: Golan Yona
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nucleic Acids ResHome page
P. T. Monteiro, N. D. Mendes, M. C. Teixeira, S. d'Orey, S. Tenreiro, N. P. Mira, H. Pais, A. P. Francisco, A. M. Carvalho, A. B. Lourenco, et al.
YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae
Nucleic Acids Res., January 11, 2008; 36(suppl_1): D132 - D136.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
L. Li, Y. Liang, and R. L. Bass
GAPWM: a genetic algorithm method for optimizing a position weight matrix
Bioinformatics, May 15, 2007; 23(10): 1188 - 1194.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.