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Related Experiment Video

Updated: Feb 10, 2026

A Venturi Effect Can Help Cure Our Trees

A Venturi Effect Can Help Cure Our Trees

Published on: October 1, 2013

Relative Suffix Trees.

Andrea Farruggia¹, Travis Gagie^2,3, Gonzalo Navarro^2,4

¹Department of Computer Science, University of Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa PI, Italy.

The Computer Journal

|May 26, 2018

Summary

This summary is machine-generated.

We introduce a novel method for compressing suffix trees in repetitive sequence collections by referencing a common suffix tree. This approach significantly reduces space usage while maintaining competitive query times for bioinformatics applications.

Keywords:

compressed text indexing repetitive collections suffix trees

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Area of Science:

Stringology
Bioinformatics
Data Structures

Background:

Suffix trees are essential in stringology and bioinformatics but suffer from large memory footprints.
Compressed suffix trees aim to reduce space complexity while simulating full suffix tree functionality.
Repetitive sequence collections, like multiple genomes, pose unique challenges for suffix tree compression.

Purpose of the Study:

To develop a new approach for compressing suffix trees tailored to repetitive sequence collections.
To compress individual suffix trees relative to a reference sequence's suffix tree.
To achieve competitive time and space trade-offs for compressed suffix trees in this context.

Main Methods:

Developing a relative compression strategy for suffix trees.
Utilizing a reference sequence's suffix tree as a basis for compression.
Evaluating the performance of these relative compressed suffix trees.

Main Results:

The proposed relative data structures achieve space usage comparable to the smallest compressed suffix trees for repetitive collections.
The method offers query times competitive with the largest and fastest compressed suffix trees.
Demonstrated effective compression for collections of individual genomes.

Conclusions:

Relative compression of suffix trees offers an efficient solution for handling repetitive sequence collections in bioinformatics.
This approach balances space reduction with computational efficiency.
The method presents a viable alternative for large-scale genomic data analysis.