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WSE, a new sequence distance measure based on word frequencies
1Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China. junwang@dlut.edu.cn
Mathematical Biosciences
|July 2, 2008
Summary
We introduce Weighted Sequence Entropy (WSE), a novel metric for biological sequence analysis. WSE improves upon relative entropy by handling absent word types and yielding stable phylogenetic trees for longer sequences.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Traditional sequence analysis relies on metrics like relative entropy (RE).
- RE can suffer from degeneracy when comparing sequences with differing word compositions.
- Phylogenetic analysis requires robust distance metrics for accurate evolutionary reconstructions.
Purpose of the Study:
- To introduce Weighted Sequence Entropy (WSE) as an improved distance metric for biological sequences.
- To address the limitations of relative entropy, particularly with absent word types.
- To evaluate WSE's performance in phylogenetic tree construction.
Main Methods:
- Development of the Weighted Sequence Entropy (WSE) metric based on short word composition.
- Comparison of WSE with classical relative entropy (RE) using varying word lengths (k).
- Application of WSE and RE to phylogenetic analysis of 25 viruses, including SARS-CoVs.
Main Results:
- WSE is equivalent to RE for small word lengths (k <= 3).
- WSE avoids degeneracy issues present in RE when word types are absent in sequences.
- WSE produces convergent phylogenetic topologies for k > 3, while RE yields degenerate results.
Conclusions:
- Weighted Sequence Entropy (WSE) offers a more robust method for biological sequence comparison.
- WSE enhances phylogenetic accuracy, especially for longer sequence analyses.
- The WSE metric is a valuable advancement for evolutionary studies in bioinformatics.
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