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Probalign: multiple sequence alignment using partition function posterior probabilities.

Usman Roshan1, Dennis R Livesay

  • 1Department of Computer Science, New Jersey Institute of Technology GITC 4400, University Heights, NJ 07102, USA. usman@cs.njit.edu

Bioinformatics (Oxford, England)
|September 7, 2006
PubMed
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Probalign, a new multiple sequence alignment tool, combines maximum expected accuracy with partition function probabilities. It outperforms leading methods on benchmarks, especially for complex protein datasets.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding protein evolution and function.
  • Traditional MSA methods often struggle with accuracy, particularly for sequences with variations.
  • Existing methods rely on pairwise posterior probabilities, with partition function methodology being a key estimation technique.

Purpose of the Study:

  • To develop a novel multiple sequence alignment method that maximizes expected accuracy.
  • To integrate pairwise posterior probabilities with partition function methodology for enhanced alignment construction.
  • To introduce the Probalign program for creating maximal expected accuracy sequence alignments.

Main Methods:

  • Developed the Probalign program, integrating maximum expected accuracy optimization with partition function-based probability estimation.

Related Experiment Videos

  • Utilized pairwise posterior probabilities of residues for sequence alignment.
  • Benchmarked Probalign against leading MSA tools like Probcons, MAFFT, and MUSCLE.

    • Main Results:

    • Probalign demonstrated superior accuracy over Probcons, MAFFT, and MUSCLE on BAliBASE 3.0, HOMSTRAD, and OXBENCH benchmarks.
    • Probalign achieved statistically highest rankings (P-value < 0.005) across all tested benchmarks.
    • Significant accuracy improvements were observed for datasets with N/C-terminal extensions and long, heterogeneous-length proteins, including those with repeats.
    • Alignment accuracy scores were 10-15% higher for datasets with high standard deviation in protein length (>300 and >400).

    Conclusions:

    • Probalign represents a significant advancement in multiple sequence alignment accuracy.
    • The method is particularly effective for challenging datasets, including those with variable protein lengths and repeats.
    • The Probalign software and associated data are publicly available for research use.