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Related Experiment Videos

Markov models of genome segmentation.

Vivek Thakur1, Rajeev K Azad, Ram Ramaswamy

  • 1Center for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi 110 067, India.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 16, 2007
PubMed
Summary

We present higher-order Markov models for segmenting symbolic sequences, improving biological sequence analysis. These models accurately identify compositional changes and genomic features in DNA sequences.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Segmentation of symbolic sequences is crucial for biological data analysis.
  • Previous methods based on Jensen-Shannon divergence have limitations in capturing local patterns.
  • Higher-order Markov models offer enhanced sensitivity to sequence details.

Purpose of the Study:

  • To introduce and evaluate higher-order Markov models for symbolic sequence segmentation.
  • To improve the identification of biologically meaningful segmentation points in genomic sequences.
  • To demonstrate the utility of these models in detecting compositional inhomogeneity and specific genomic features.

Main Methods:

  • Development of higher-order Markov models for sequence segmentation.
  • Application of Jensen-Shannon divergence-based segmentation procedures.

Related Experiment Videos

  • Analysis of chimeric DNA sequences from diverse species.
  • Segmentation of the E. coli K12 genome.

    • Main Results:

    • Higher-order Markov models show increased sensitivity to local patterns.
    • Accurate detection of compositional inhomogeneity in chimeric DNA sequences.
    • Precise identification of genomic islands, cryptic prophages, and horizontally acquired regions in the E. coli K12 genome.
    • Demonstrated advantage over earlier segmentation methods.

    Conclusions:

    • Higher-order Markov models provide a powerful tool for accurate sequence segmentation.
    • These models enhance the biological interpretation of genomic sequences.
    • The approach is effective for identifying key genomic structures and regions.