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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

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pyABC: distributed, likelihood-free inference.

Emmanuel Klinger^1,2,3, Dennis Rickert², Jan Hasenauer^2,3

¹Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt.

Bioinformatics (Oxford, England)

|May 16, 2018

Summary

This summary is machine-generated.

We developed pyABC, a scalable framework for likelihood-free inference using approximate Bayesian computation (ABC). This tool addresses the computational challenges of complex biological models, making advanced methods accessible.

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Area of Science:

Computational Biology
Systems Biology
Statistical Inference

Background:

Likelihood-free inference is crucial for complex biological systems.
Approximate Bayesian computation (ABC) offers a theoretical solution but faces computational hurdles.
Scalability is a major challenge for applying ABC to demanding stochastic models.

Purpose of the Study:

To develop a distributed and scalable framework for likelihood-free inference.
To overcome the computational limitations of traditional ABC methods.
To provide an accessible yet customizable tool for systems biology research.

Main Methods:

Developed pyABC, a Python-based ABC-Sequential Monte Carlo (ABC-SMC) framework.
Implemented a scalable, runtime-minimizing parallelization strategy for distributed environments.
Designed for both non-expert and advanced users with customizable ABC-SMC components.

Main Results:

pyABC enables scalable likelihood-free inference for computationally demanding stochastic models.
The framework supports parallelization across thousands of cores, minimizing runtime.
Offers flexibility for customization of acceptance thresholds, kernels, and distance functions.

Conclusions:

pyABC significantly enhances the practical application of ABC in systems biology.
The framework democratizes access to advanced computational methods for biological data analysis.
Provides visualization and data querying capabilities for enhanced usability.