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A Practical Guide to Phylogenetics for Nonexperts
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Measuring the distance between multiple sequence alignments.

Benjamin P Blackburne1, Simon Whelan

  • 1Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK.

Bioinformatics (Oxford, England)
|December 27, 2011
PubMed
Summary
This summary is machine-generated.

New metrics help compare multiple sequence alignments (MSAs) by analyzing gap and indel events. This aids in understanding MSA method differences and their impact on bioinformatics analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is fundamental to bioinformatics, impacting downstream analyses like phylogenetic inference and protein structure prediction.
  • Diverse MSA methods exist, employing different objective functions and heuristics, leading to varied alignment outcomes.
  • Understanding the differences between inferred MSAs is crucial for evaluating method performance and downstream analysis reliability.

Purpose of the Study:

  • To develop and introduce novel metrics for quantifying differences between multiple sequence alignments.
  • To investigate how these metrics relate to the objective functions and heuristics of MSA methods.
  • To assess the impact of MSA differences on downstream bioinformatics applications.

Main Methods:

  • Introduction of four novel metrics to compare MSAs, focusing on gap positions and indel events on phylogenetic trees.
  • Utilized both real and synthetic datasets for comprehensive evaluation of the proposed metrics.
  • Explored the information content of individual metrics and their synergistic power when used in combination.

Main Results:

  • The four introduced metrics provide insights into the variations between different MSA methods.
  • Analysis using real and synthetic data demonstrates the utility of these metrics in characterizing MSA differences.
  • Combined application of metrics yields richer information regarding MSA method performance and discrepancies.

Conclusions:

  • The developed metrics offer a quantitative framework for comparing MSAs and understanding the nuances of different alignment algorithms.
  • These metrics can help researchers select appropriate MSA tools and interpret the results of downstream analyses more accurately.
  • The MetAl software implementation facilitates the application of these metrics in practical bioinformatics workflows.