Skip Navigation


Bioinformatics Advance Access originally published online on January 19, 2007
Bioinformatics 2007 23(5):629-630; doi:10.1093/bioinformatics/btl681
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
23/5/629    most recent
btl681v1
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 Välimäki, N.
Right arrow Articles by Mäkinen, V.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Välimäki, N.
Right arrow Articles by Mäkinen, V.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Compressed suffix tree—a basis for genome-scale sequence analysis

Niko Välimäki 1, Wolfgang Gerlach 2, Kashyap Dixit 3 and Veli Mäkinen 1,*

1Department of Computer Science, P.O. Box 68 (Gustaf Hällströmin katu 2b), FI-00014 University of Helsinki, Finland, 2Technische Fakultät, Universität Bielefeld, Germany and 3Indian Institute of Technology, Kanpur, India

*To whom correspondence should be addressed.


  Abstract

Summary: Suffix tree is one of the most fundamental data structures in string algorithms and biological sequence analysis. Unfortunately, when it comes to implementing those algorithms and applying them to real genomic sequences, often the main memory size becomes the bottleneck. This is easily explained by the fact that while a DNA sequence of length n from alphabet {Sigma} = {A, C, G, T } can be stored in n log |{Sigma}|= 2n bits, its suffix tree occupies O(n log n) bits. In practice, the size difference easily reaches factor 50.

We provide an implementation of the compressed suffix tree very recently proposed by Sadakane (Theory of Computing Systems, in press). The compressed suffix tree occupies space proportional to the text size, i.e. O(n log} | {Sigma} |) bits, and supports all typical suffix tree operations with at most log n factor slowdown. Our experiments show that, e.g. on a 10 MB DNA sequence, the compressed suffix tree takes 10% of the space of normal suffix tree. Typical operations are slowed down by factor 60.

Availability: The C++ implementation under GNU license is available at http://www.cs.helsinki.fi/group/suds/cst/. An example program implementing a typical pattern discovery task is included. Experimental results in this note correspond to version 0.95.

Contact: vmakinen{at}cs.helsinki.fi

Received on November 3, 2006; revised on January 5, 2007; accepted on January 5, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




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.