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MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior

Yongchao Liu1, Bertil Schmidt, Douglas L Maskell

  • 1School of Computer Engineering, Nanyang Technological University, Singapore. liuy0039@ntu.edu.sg

Bioinformatics (Oxford, England)
|June 26, 2010
PubMed
Summary
This summary is machine-generated.

MSAProbs is a novel multiple sequence alignment algorithm that enhances protein sequence alignment accuracy using pair hidden Markov models and partition functions. It outperforms existing top aligners and is optimized for multi-core CPUs.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for bioinformatics and computational biology.
  • Developing efficient and accurate MSA algorithms remains a significant challenge.

Purpose of the Study:

  • To introduce MSAProbs, a new algorithm for accurate multiple sequence alignment of protein sequences.
  • To improve upon existing MSA methods in terms of accuracy and computational efficiency.

Main Methods:

  • MSAProbs utilizes a combination of pair hidden Markov models and partition functions to compute posterior probabilities.
  • Incorporates weighted probabilistic consistency transformation and weighted profile-profile alignment techniques.
  • Optimized for multi-core CPUs using a multi-threaded design.

Main Results:

  • MSAProbs demonstrates statistically significant accuracy improvements over leading aligners like ClustalW, MAFFT, MUSCLE, ProbCons, and Probalign.
  • Achieved high accuracy on standard benchmarks including BAliBASE, PREFAB, SABmark, and OXBENCH.
  • Offers competitive execution times due to its multi-threaded design.

Conclusions:

  • MSAProbs represents a practical advancement in multiple sequence alignment for protein sequences.
  • The algorithm offers superior accuracy and competitive performance, making it a valuable tool for bioinformatics research.
  • Source code is publicly available for broader scientific adoption.