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Updated: Nov 6, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic

François-David Collin1, Ghislain Durif1, Louis Raynal1

  • 1IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France.

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|May 5, 2021
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Summary

This study introduces DIYABC Random Forest v1.0, a computational package using Random Forest (RF) for efficient population genetic history inferences. It simplifies complex analyses of genetic data, improving scenario choice and parameter estimation.

Keywords:
SNPapproximate Bayesian computationdemographic historymodel or scenario selectionparameter estimationpool-sequencingpopulation geneticsrandom forestsupervised machine learning

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Simulation-based methods like approximate Bayesian computation (ABC) are crucial for analyzing complex population and species genetic histories.
  • Supervised machine learning (SML) offers efficient statistical solutions for scenario choice and parameter estimation in population genetics.

Purpose of the Study:

  • To implement Random Forest (RF) algorithms for efficient inferences in population genetics.
  • To develop a user-friendly package (DIYABC Random Forest v1.0) integrating simulation and RF analysis for molecular data.
  • To evaluate the accuracy and power of RF methods for inferring complex population genetic histories from various data types, including large SNP datasets.

Main Methods:

  • Implemented Random Forest (RF) algorithms using simulated datasets from DIYABC v2.1.0.
  • Developed DIYABC Random Forest v1.0, a package for simulating molecular data (microsatellites, DNA sequences, SNPs) and performing RF analyses.
  • Applied RF methods for scenario choice and parameter estimation on pseudo-observed and real SNP datasets (pool-sequencing and individual-sequencing).

Main Results:

  • DIYABC Random Forest v1.0 provides a user-friendly interface for genetic data simulation and RF-based inference.
  • RF methods enable efficient inferences at low computational cost, bypassing the need for preliminary summary statistic selection and ABC tolerance level derivation.
  • The package demonstrates effectiveness in analyzing large SNP datasets for complex population genetic history inferences.

Conclusions:

  • DIYABC Random Forest v1.0 offers a powerful and efficient tool for population geneticists.
  • The integration of RF methods significantly enhances the analysis of complex genetic datasets, particularly large SNP datasets.
  • This approach facilitates robust scenario choice and parameter estimation in evolutionary studies.