Skip Navigation



Bioinformatics Advance Access published online on March 9, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl088
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/11/1302    most recent
btl088v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
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 Leonardi, F. G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Leonardi, F. G.
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 October 26, 2005
Revised February 19, 2006
Accepted March 6, 2006

Article

A generalization of the PST algorithm: modeling the sparse nature of protein sequences

Florencia G. Leonardi 1 *

1 Instituto de Matemática e Estatística, Universidade de São Paulo, Rua do Matão 1010 CEP 05508-090, São Paulo, Brazil

* To whom correspondence should be addressed.
Florencia G. Leonardi, E-mail: leonardi{at}ime.usp.br


  Abstract

Motivation: A central problem in genomics is to determine the function of a protein using the information contained in its amino acid sequence. Variable Length Markov Chains (VLMC) are a promising class of models that can effectively classify proteins into families and they can be estimated in linear time and space.

Results: We introduce a new algorithm, called Sparse Probabilistic Suffix Trees (SPST), that identifies equivalences between the contexts of a VLMC. We show that, in many cases, the identification of these equivalences can improve the classification rate of the classical Probabilistic Suffix Trees (PST) algorithm. We also show that better classification can be achieved identifying representative fingerprints in the amino acid chains, and this variation in the SPST algorithm is called F-SPST.

Availability: The SPST algorithm can be freely downloaded from the site http://www.ime.usp.br/~leonardi/spst/.

Associate Editor: Thomas Lengauer
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
 BioinformaticsHome page
H. Li, X. Dai, and X. Zhao
A nearest neighbor approach for automated transporter prediction and categorization from protein sequences
Bioinformatics, May 1, 2008; 24(9): 1129 - 1136.
[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.