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Evolutionary insights from suffix array-based genome sequence analysis.

Anindya Poddar1, Nagasuma Chandra, Madhavi Ganapathiraju

  • 1Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560 012, India.

Journal of Biosciences
|October 5, 2007
PubMed
Summary
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Improved suffix array construction enables efficient whole genome and proteome analysis. This computational method reveals evolutionary insights and uncovers patterns in peptide sequences, aiding in the identification of repeating elements.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Analyzing gene and protein sequences is crucial in modern biology.
  • Efficient whole genome analysis requires advanced computational methods.
  • Suffix trees and suffix arrays are effective data structures for sequence analysis.

Purpose of the Study:

  • To present an improved method for constructing suffix arrays.
  • To enhance the versatility and scalability of suffix array construction for biological sequence analysis.
  • To demonstrate the application of this improved method in analyzing whole genomes and proteomes.

Main Methods:

  • Development of an enhanced suffix array construction algorithm.
  • Application of the algorithm to analyze genomic and proteomic datasets.

Related Experiment Videos

  • Utilizing string matching and pattern recognition for sequence analysis.

    • Main Results:

    • The improved suffix array construction demonstrates enhanced versatility and scalability for whole genome analysis.
    • Analysis of n-grams provides evolutionary insights, suggesting genetic code influences genome composition.
    • Proteome analysis reveals under-sampled peptide spaces and identifies patterns in tetra-peptides and higher, indicating potential biological meaning.
    • Efficient identification of repeating sequences in whole proteomes, exemplified by 16 COG members in Mycobacterium tuberculosis H37Rv.

    Conclusions:

    • The enhanced suffix array construction method is suitable for large-scale biological sequence analysis.
    • The approach offers valuable insights into genome evolution and peptide space coverage.
    • This computational toolkit effectively identifies biologically relevant patterns and repeats within genomes and proteomes.