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scrm: efficiently simulating long sequences using the approximated coalescent with recombination.

Paul R Staab1, Sha Zhu1, Dirk Metzler1

  • 1Department of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.

Bioinformatics (Oxford, England)
|January 18, 2015
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Summary
This summary is machine-generated.

We developed a new simulation method, the sequential coalescent with recombination model (SCRM), to accurately generate large genomic datasets. This approach bridges the gap between approximations and exact models for efficient in silico sequence generation.

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Area of Science:

  • Genomics
  • Computational Biology
  • Population Genetics

Background:

  • Coalescent-based simulation software is efficient for small genetic sequences.
  • Simulating genome-size datasets with next-generation sequencing data currently relies on approximations.

Purpose of the Study:

  • To introduce a new method for accurately simulating genome-size datasets.
  • To bridge the gap between current approximations and exact coalescent models.

Main Methods:

  • Developed the sequential coalescent with recombination model (SCRM).
  • Implemented an efficient computational approach for SCRM.

Main Results:

  • SCRM efficiently and accurately approximates the coalescent with recombination.
  • The method simulates genomic-scale datasets with a correct linkage structure.

Conclusions:

  • SCRM offers a significant improvement for simulating large genomic datasets.
  • This method enhances the accuracy and efficiency of in silico sequence generation for population genetics research.