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AlignStat: a web-tool and R package for statistical comparison of alternative multiple sequence alignments.

Thomas Shafee1, Ira Cooke2,3

  • 1Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, 3086, Australia. T.Shafee@LaTrobe.edu.au.

BMC Bioinformatics
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Summary

Comparing multiple sequence alignments (MSA) is crucial as different algorithms produce varied results. Our method quantifies these differences, identifying consensus regions for better downstream analysis and understanding algorithm variations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) algorithms produce divergent results, necessitating methods to compare them.
  • Quantifying similarities and differences between MSAs aids in identifying consensus regions for downstream analysis.
  • Such comparisons can reveal systematic variations stemming from distinct alignment algorithms.

Purpose of the Study:

  • To present a straightforward method for comparing two multiple sequence alignments.
  • To assess the similarity and quantify the differences between alternative alignments.
  • To facilitate the identification of consensus regions and algorithm-specific variations.

Main Methods:

  • Developed a method to align and compare two alternative multiple sequence alignments.
  • Categorized differences into merges, splits, or shifts relative to a reference alignment.
  • Implemented graphical visualizations for intuitive data interpretation.

Main Results:

  • A novel method for assessing MSA similarity and quantifying differences.
  • Classification of alignment discrepancies into merges, splits, and shifts.
  • Development of visual tools for clear interpretation of alignment comparisons.

Conclusions:

  • AlignStat provides an accessible tool for online MSA similarity comparisons and R pipeline integration.
  • The AlignStat web tool is available at AlignStat.Science.LaTrobe.edu.au.
  • An R package, documentation, and example data are accessible via CRAN and GitHub.