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Related Experiment Videos

Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic

Taikai Takeda1, Michiaki Hamada1,2,3,4,5, John Hancock

  • 1Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.

Bioinformatics (Oxford, England)
|October 18, 2017
PubMed
Summary

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This study introduces a new method for selecting optimal Pair Hidden Markov Models (PHMMs) by determining the best number of hidden states. The approach enhances sequence alignment accuracy and model selection capabilities.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pair Hidden Markov Models (PHMMs) are fundamental probabilistic tools for pairwise sequence alignment in bioinformatics.
  • Existing PHMM studies often use a limited number of hidden states (match, insertion, deletion), potentially hindering optimal alignment accuracy.

Purpose of the Study:

  • To develop and evaluate a novel method for selecting superior PHMMs, specifically optimizing the number of hidden states.
  • To improve the accuracy of sequence alignment by employing more sophisticated PHMMs.

Main Methods:

  • A new method was developed to select optimal PHMMs based on the highest posterior probability.
  • The Factorized Information Criterion was employed for model selection, a technique suitable for probabilistic models with hidden variables.

Related Experiment Videos

  • Simulations and application to multi-species DNA datasets were used for evaluation.

    • Main Results:

    • The proposed method demonstrated excellent capabilities in selecting appropriate PHMMs, including the optimal number of hidden states.
    • A slight improvement in sequence alignment accuracy was observed.
    • Application to 5 and 28 species DNA datasets resulted in the selection of more complex models compared to previous studies.

    Conclusions:

    • The novel method effectively identifies superior PHMMs, enhancing model selection and alignment accuracy.
    • The findings suggest that more complex PHMMs, with a greater number of hidden states, can be beneficial for sequence alignment tasks.
    • The developed software is publicly available for the research community.