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A Service-Oriented Platform for Approximate Bayesian Computation in Population Genetics.

Roberto Solar1,2, Victor Sepulveda1,2, Alonso Inostrosa-Psijas3

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Approximate Bayesian computation (ABC) accelerates evolutionary history analysis in population genetics. This study introduces a distributed web platform for efficient, scalable ABC simulations, reducing computational costs.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Approximate Bayesian computation (ABC) is vital for Bayesian inference without likelihood functions, particularly in population genetics for evolutionary history analysis.
  • Traditional forward-in-time simulations for ABC are computationally intensive, necessitating high-performance computing for timely results.
  • Existing methods often struggle with scalability and efficiency when analyzing complex demographic and genetic forces.

Purpose of the Study:

  • To develop a fully distributed, web service-oriented platform for Approximate Bayesian computation (ABC) using forward-in-time simulations.
  • To enhance the efficiency and scalability of population genetic simulations for evolutionary analyses.
  • To reduce the computational expense and response times associated with ABC.

Main Methods:

  • A client-server architecture was implemented, allowing users to define simulation scenarios via a client.
  • The server comprises four services: workload generator, simulation controller, results analyzer, and result builder for scalable simulations.
  • Multithreaded simulations are performed using a simulation kernel within the proposed libgdrift library, with three algorithmic approaches evaluated.

Main Results:

  • The proposed platform enables efficient and scalable population simulations, deployable on distributed clusters or the cloud.
  • The libgdrift library's algorithms were evaluated for their effectiveness in reducing execution times and memory consumption.
  • The web service-oriented approach facilitates easier access and utilization of ABC for population genetics research.

Conclusions:

  • The developed distributed platform significantly improves the efficiency and scalability of ABC, making complex population genetic analyses more accessible.
  • The libgdrift library offers optimized algorithms for faster and less memory-intensive simulations.
  • This work provides a robust and flexible solution for leveraging ABC in population genetics research, supporting the study of evolutionary histories.