PyEvoMotion: a Python tool for population-based time-course analysis of genome evolution

TL;DR

PyEvoMotion is a Python tool that analyzes genome evolution over time using statistical models and can handle large datasets.

Contribution

PyEvoMotion introduces a modular, scalable tool for inferring time-dependent evolutionary models with Gaussian noise.

Findings

PyEvoMotion can process thousands to millions of sequences to compute statistical parameters of genome evolution.

It detects non-Brownian evolutionary motions with subdiffusive behavior in viral genomic data.

The tool integrates into bioinformatic pipelines with customizable filtering and mutation classification.

Abstract

We present PyEvoMotion, an open-source Python tool for inferring molecular clock models with time-dependent Gaussian noise from high-throughput genomic datasets. PyEvoMotion features a command-line interface and a modular architecture, allowing seamless integration into larger bioinformatic pipelines. The tool supports customizable filtering, temporal discretization definition, and mutation classification, making it adaptable to diverse research needs. While traditional phylogenetic methods may encounter computational challenges with large datasets, PyEvoMotion can process thousands to millions of sequences to compute statistical parameters associated with a stochastic differential equation model, thereby weighting the genetic variation within the population. Using viral genomic data, we demonstrate its capability to infer evolutionary rates and detect non-Brownian evolutionary motions with subdiffusive behavior. PyEvoMotion shows potential to provide overlooked insights into genome evolution in different contexts.

The open source software is available on GitHub at https://github.com/luksgrin/PyEvoMotion and on SourceForge at https://sourceforge.net/projects/pyevomotion.

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