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Related Experiment Video

Updated: Jul 17, 2026

The ITS2 Database

The ITS2 Database

Published on: March 12, 2012

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

Niko Välimäki¹, Wolfgang Gerlach, Kashyap Dixit

¹Department of Computer Science, P.O. Box 68 (Gustaf Hällströmin katu 2b), FI-00014 University of Helsinki, Finland.

Bioinformatics (Oxford, England)

|January 24, 2007

Summary

Compressed suffix trees significantly reduce memory usage for biological sequence analysis. This implementation offers substantial space savings, making large genomic datasets more manageable for string algorithms.

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Area of Science:

Computer Science
Bioinformatics
Data Structures

Background:

Suffix trees are essential for string algorithms and biological sequence analysis.
Standard suffix trees require substantial memory (O(n log n) bits), posing a bottleneck for large genomic sequences.
The memory requirement for a suffix tree can be up to 50 times larger than the original DNA sequence.

Purpose of the Study:

To implement and evaluate Sadakane's compressed suffix tree.
To demonstrate the practical memory and performance benefits of compressed suffix trees for genomic data.

Main Methods:

Implementation of a compressed suffix tree based on Sadakane's recent proposal.
Experimental evaluation on a 10 MB DNA sequence.

Main Results:

The compressed suffix tree uses only 10% of the space of a normal suffix tree for a 10 MB DNA sequence.
Typical suffix tree operations show a slowdown factor of 60.
The compressed suffix tree occupies space proportional to the text size (O(n log |sigma|) bits).

Conclusions:

Compressed suffix trees offer a practical solution to memory limitations in string algorithms and bioinformatics.
Despite a slowdown in operation speed, the significant space reduction makes compressed suffix trees viable for large-scale genomic analysis.