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ProgSIO-MSA: Progressive-based single iterative optimization framework for multiple sequence alignment using an

Sanjay Bankapur1, Nagamma Patil1

  • 1Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Manglore 575025, Karnataka, India.

Journal of Bioinformatics and Computational Biology
|May 7, 2020
PubMed
Summary

A new multiple sequence alignment (MSA) model, ProgSIO-MSA, improves computational efficiency and biological accuracy for analyzing vast biological sequence data. This advanced method enhances sequence alignment quality, aiding fields like drug discovery and phylogenetics.

Keywords:
Bioinformaticsmultiple sequence alignmentpolynomial timeprotein sequencesscoring systemsingle iterative optimization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for analyzing biological sequences, with applications in phylogenetics, protein structure prediction, and drug discovery.
  • The NP-complete nature of MSA and the surge in Next-Generation Sequencing data necessitate computationally efficient and accurate alignment models.
  • Existing models struggle to handle the increasing volume of unanalyzed sequence data.

Purpose of the Study:

  • To propose a novel progressive-based alignment model, ProgSIO-MSA, designed for computationally efficient and accurate multiple sequence alignment.
  • To address the limitations of current MSA methods in handling large-scale biological sequence data.

Main Methods:

  • Developed ProgSIO-MSA, incorporating a unique scoring system with 'Look Back Ahead' and 'Position-Residue-Specific Dynamic Gap Penalty'.
  • Implemented a 'single iterative optimization' (SIO) framework to refine alignment quality and escape local optima.
  • Evaluated ProgSIO-MSA against state-of-the-art models on BAliBASE and SABmark benchmark datasets.

Main Results:

  • ProgSIO-MSA demonstrated a 17.7% increase in alignment quality (biological accuracy) on the BAliBASE dataset.
  • Runtime analysis and time complexity comparisons showed improved computational efficiency.
  • Statistical analysis confirmed that ProgSIO-MSA significantly outperformed existing progressive-based MSA models.

Conclusions:

  • ProgSIO-MSA offers a significant advancement in multiple sequence alignment, balancing accuracy and computational efficiency.
  • The model effectively handles large biological sequence datasets, supporting critical research areas.
  • This work provides a valuable tool for bioinformatics and computational biology research.