Fast and flexible simulation of DNA sequence data.

By Gary K. Chen, Paul Marjoram, Jeffrey D. Wall. Genome Research, 2008

388 citations recorded by the source corpus.

Simulation of genomic sequences under the coalescent with recombination has conventionally been impractical for regions beyond tens of megabases. This work presents an algorithm, implemented as the program MaCS (Markovian Coalescent Simulator), that can efficiently simulate haplotypes under any arbitrary model of population history. We present several metrics comparing the performance of MaCS with other available simulation programs. Practical usage of MaCS is demonstrated through a comparison of measures of linkage disequilibrium between generated program output and real genotype data from populations considered to be structured.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2b508a3682b664c75789810797eaeb146b00e920

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Citations in the corpus

Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, methodology): “Hudson’s ms is widely regarded as the most eﬃcient implementation of the exact coalescent and is the benchmark against which other programs are measured (Marjoram and Wall, 2006; Chen et al., 2009; Excoﬃer and Foll, 2011; Staab et al., 2014; Yang et al., 2014; Wang et al., 2014).”
SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes (2013, methodology): “With respect to simulated data sets, SweeD supports ms (Hudson 2002) and MaCS (Chen et al. 2009) formats.”
Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics (2013, methodology): “7 Neutral simulations of a complex demographic model To estimate the number of rare variants expected under neutrality, we used MaCS (62) to simulate 100 neutral collections of 1,354 regions (size range 10-10,373 bp, matching the number and size range of the ultrasensitive regions) in 911 individual”
A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis (2010, methodology): “The genomic marker data were generated using MaCS [29], a scalable approximate coalescent simulator, using the default approximation tree width.”
Ancient Admixture in Human History (2012, methodology): “To study the effect of drift post-dating admixture, we performed simulations using the MaCS coalescent simulator (Chen et al. 2009).”
Reconstructing routes of invasion using genetic data: why, how and so what? (2010, methodology): “In practice, ABC users can base their analysis on simulation programs, such as SIMCOAL (Laval & Excoffier 2004), ms (Hudson 2002), Serial SimCoal (Anderson et al. 2005) or MaCS (Chen et al. 2009) and then use statistical software to postprocess their simulation outputs.”
Computer simulations: tools for population and evolutionary genetics (2012, methodology): “Efficiency gains have been achieved for backward simulators using the sequential Markov coalescent 82,86 , as noted above.”
Inferring human population size and separation history from multiple genome sequences (2014, positive): “We use the Sequential Markovian Coalescent framework [5, 6, 50], SMC’, to derive transition rates between hidden states.”