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Related Experiment Video

Updated: Dec 30, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent.

Stephan Schiffels¹, Ke Wang²

¹Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany. schiffels@shh.mpg.de.

Methods in Molecular Biology (Clifton, N.J.)

|January 25, 2020

Summary

The Multiple Sequentially Markovian Coalescent (MSMC) method and software infer population history from genomes. This guide details processing genomic data and generating demographic plots using MSMC and MSMC2.

Keywords:

Coalescent modelling Complete genome sequencing Demographic inference Phasing Population structure

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Published on: August 16, 2017

Area of Science:

Population Genetics
Genomics
Computational Biology

Background:

The Multiple Sequentially Markovian Coalescent (MSMC) is a key method for analyzing population genetic history.
Inferring demographic changes and population structure over time from genomic data is crucial in evolutionary biology.
Existing tools require detailed understanding of data processing and methodological background.

Purpose of the Study:

To provide a comprehensive guide for using the MSMC and MSMC2 software.
To detail the workflow for processing genomic data from BAM files to demographic history plots.
To offer background on the MSMC methodology and point to community resources.

Main Methods:

Utilizing the MSMC and MSMC2 software packages for population genetic analyses.
Processing genomic data, including alignment and variant calling, from BAM files.
Generating visualizations of inferred historical population sizes and genetic separation.

Main Results:

A clear workflow for applying MSMC/MSMC2 to genomic sequence data is presented.
The guide covers data preparation, software execution, and result interpretation.
Included are bash scripts and Python code to facilitate the analysis.

Conclusions:

MSMC and MSMC2 are powerful tools for reconstructing population genetic histories.
This guide empowers researchers to apply these methods effectively to their genomic data.
Community resources are highlighted for ongoing support and further development.