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Biological sequence compression algorithms.

T Matsumoto1, K Sadakane, H Imai

  • 1Department of Information Science, University of Tokyo,7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. toshikom@is.s.u-tokyo.ac.jp

Genome Informatics. Workshop on Genome Informatics
|November 9, 2001
PubMed
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Efficiently storing and communicating vast DNA sequence data is crucial. This study enhances the Context Tree Weighting Method (CTW) to better compress DNA sequences by utilizing their unique structures like palindromes and approximate repeats, achieving higher compression ratios.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The exponential growth of DNA sequence data necessitates efficient storage and communication methods.
  • Standard compression algorithms are ineffective for biological sequences, often increasing file sizes.
  • Existing DNA-specific algorithms show promise but can be further improved.

Purpose of the Study:

  • To enhance the Context Tree Weighting Method (CTW) for improved DNA sequence compression.
  • To leverage characteristic DNA structures, specifically palindromes and approximate repeats, for better compression.
  • To develop a novel compression algorithm for protein sequences.

Main Methods:

  • Modified the Context Tree Weighting Method (CTW) to incorporate biological sequence features.

Related Experiment Videos

  • Implemented a preprocessing step to identify palindromes and approximate repeats using hashing and dynamic programming.
  • Developed a new representation for identified palindromes and repeats based on length and distance.
  • Designed a new compression algorithm specifically for protein sequences.

    • Main Results:

    • The improved CTW algorithm achieves a higher compression ratio compared to existing DNA-oriented methods.
    • The enhanced method effectively utilizes palindromes and approximate repeats for compression.
    • A new compression algorithm for protein sequences was successfully developed.

    Conclusions:

    • Integrating biological sequence structures into compression algorithms significantly improves efficiency.
    • The developed method offers a superior approach for compressing large-scale DNA and protein sequence datasets.
    • This work contributes to more effective management and analysis of biological sequence information.