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Published on: May 19, 2019
Study of LZ-word distribution and its application for sequence comparison
Qi Dai1, Zhaofang Yan, Zhuoxing Shi
1College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
Journal of Theoretical Biology
|July 24, 2013
Summary
This study introduces a new statistical method for comparing biological sequences by analyzing the distribution of Lempel-Ziv (LZ) words. The novel approach utilizes set operations on LZ-word sets, offering a new perspective beyond traditional complexity calculations.
Area of Science:
- Bioinformatics
- Computational Biology
- Sequence Analysis
Background:
- Lempel-Ziv complexity is a standard metric for sequence comparison.
- Previous studies have not explored the distribution of components within the exhaustive history of Lempel-Ziv words.
- A gap exists in understanding the full distributional properties of Lempel-Ziv components for sequence analysis.
Purpose of the Study:
- To investigate the complete distribution of Lempel-Ziv (LZ) words.
- To present a novel statistical method for biological sequence comparison.
- To revise Lempel-Ziv complexity by considering component lengths and applying set operations.
Main Methods:
- Investigated the whole distribution of LZ-words.
- Revised Lempel-Ziv complexity to account for component lengths, generating various LZ-word sets.
- Defined a series of set operations on LZ-word sets for sequence comparison, moving beyond content analysis.
- Conducted two experimental sets to validate the new method.
Main Results:
- The study successfully analyzed the distribution of LZ-words.
- A novel statistical method for sequence comparison based on LZ-word set operations was developed.
- The proposed method demonstrated effectiveness in preliminary experiments.
Conclusions:
- The distribution of LZ-words provides valuable insights for sequence comparison.
- The novel statistical method offers a new paradigm for analyzing biological sequences.
- The revised Lempel-Ziv complexity and set operations present a promising alternative to alignment-based methods.
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