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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
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MetaSMC: a coalescent-based shotgun sequence simulator for evolving microbial populations.

Ki-Hok Liao1, Wing-Kai Hon1, Chuan-Yi Tang1,2

  • 1Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan.

Bioinformatics (Oxford, England)
|October 16, 2018
PubMed
Summary

MetaSMC is an efficient algorithm for simulating shotgun sequences from evolving microbial populations. This tool aids in developing and evaluating analysis algorithms for metagenomics and cancer evolution studies.

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Area of Science:

  • Computational Biology
  • Genomics
  • Evolutionary Biology

Background:

  • High-throughput sequencing has transformed metagenomics and cancer evolution studies.
  • Analyzing microbial or tumor cell population evolution requires robust simulation tools.

Purpose of the Study:

  • To introduce MetaSMC, an efficient algorithm for simulating shotgun sequences from evolving microbial populations.
  • To provide a tool that supports various evolutionary scenarios and substitution models.

Main Methods:

  • MetaSMC is based on coalescent theory and the Sequentially Markov Coalescent.
  • It efficiently handles frequent recombination by ignoring unnecessary chromosomal segments.
  • Supports multiple substitution models (Jukes-Cantor, HKY85, GTR) and mutator phenotypes.

Main Results:

  • The algorithm is efficient and equivalent to Sequentially Markov Coalescent with an incomplete sample.
  • Accuracy was validated using summary statistics and Monte Carlo integration.
  • Supports diverse evolutionary scenarios and complex mutation patterns.

Conclusions:

  • MetaSMC offers an efficient and accurate simulation method for population genomics.
  • It is a valuable resource for developing and evaluating analysis algorithms in metagenomics and cancer research.