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Detecting microsatellites within genomes: significant variation among algorithms.

Sébastien Leclercq1, Eric Rivals, Philippe Jarne

  • 1LIRMM, UMR 5506 CNRS--Université de Montpellier II, Montpellier, France. sebastien.leclercq@cefe.cnrs.fr

BMC Bioinformatics
|April 20, 2007
PubMed
Summary
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The choice of microsatellite detection algorithm significantly impacts results, affecting analyses of DNA sequence evolution. Different tools yield varying distributions, especially for short microsatellites, necessitating careful algorithm selection.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Evolution

Background:

  • Microsatellites, or short tandem repeats (STRs), are abundant in genomes and crucial for evolutionary studies.
  • Analyzing microsatellite characteristics requires robust computational tools for exhaustive genome-wide extraction.
  • Existing algorithms for microsatellite detection vary in their efficiency and output.

Purpose of the Study:

  • To compare the detection efficiency of five prominent microsatellite analysis algorithms: TRF, Mreps, Sputnik, STAR, and RepeatMasker.
  • To assess the influence of user-defined parameters on microsatellite distribution analysis.
  • To evaluate algorithm performance across diverse genomes with varying structures.

Main Methods:

  • Comparative analysis of five microsatellite detection algorithms (TRF, Mreps, Sputnik, STAR, RepeatMasker).

Related Experiment Videos

  • Initial analysis on the human X chromosome, followed by extension to Saccharomyces cerevisiae, Neurospora crassa, and Drosophila melanogaster.
  • Characterization of microsatellite number, length, and motif divergence; parameter sensitivity analysis.

    • Main Results:

    • Algorithm choice significantly impacts microsatellite distribution characteristics (number, length, divergence).
    • Differences were observed across algorithms but not consistently across the tested genomes.
    • Short microsatellites (<20 bp) showed particularly striking variations in detection, irrespective of motif type.

    Conclusions:

    • Empirical microsatellite distributions are highly dependent on the detection algorithm used.
    • Studies comparing empirical and theoretical distributions for evolutionary analysis require cautious interpretation due to algorithmic bias.
    • The current typological definition of microsatellites may limit comprehensive understanding of their genomic distribution.