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Updated: Jul 6, 2026
09:40
Novel Sequence Discovery by Subtractive Genomics
Published on: January 25, 2019
uShuffle: a useful tool for shuffling biological sequences while preserving the k-let counts
Minghui Jiang1, James Anderson, Joel Gillespie
1Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA. mjiang@cc.usu.edu
BMC Bioinformatics
|April 15, 2008
Summary
A new bioinformatics tool, uShuffle, generates random biological sequences while preserving k-let counts. This sophisticated shuffling method ensures statistical significance for DNA, RNA, and protein sequence analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Sequence Analysis
Background:
- Randomly shuffled sequences are crucial for evaluating biological sequence statistical significance.
- Existing tools often lack the sophistication to preserve higher-order statistics like k-let counts.
Purpose of the Study:
- To develop a sophisticated sequence analysis tool for generating uniform random permutations.
- To preserve exact k-let counts in biological sequences (DNA, RNA, proteins).
Main Methods:
- Implementation of the latest Euler algorithm variant.
- Utilization of Wilson's algorithm for arborescence generation.
- Engineering for efficiency and flexibility with arbitrary alphabet and k-let sizes.
Main Results:
- The uShuffle tool generates uniform random permutations preserving exact k-let counts.
- Achieves high performance and flexibility, surpassing existing Euler algorithm implementations.
- Available as a command-line program, web application, or utility library with provided source code.
Conclusions:
- uShuffle offers superior performance and flexibility for biological sequence shuffling.
- It is a valuable and useful tool for the bioinformatics community.
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