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

Area of Science:

• Genomics
• Computational Biology
• Evolutionary Biology

Background:

• High-throughput genomic datasets present computational challenges for traditional phylogenetic methods.
• Understanding genome evolution requires sophisticated tools to analyze molecular clock models and genetic variation.

Purpose of the Study:

• To introduce PyEvoMotion, an open-source Python tool for inferring molecular clock models with time-dependent Gaussian noise.
• To provide a flexible and scalable solution for analyzing large genomic datasets.

Main Methods:

• PyEvoMotion utilizes a stochastic differential equation model to compute statistical parameters.
• The tool offers a command-line interface and modular architecture for bioinformatic pipeline integration.
• Features include customizable filtering, temporal discretization, and mutation classification.

Main Results:

• PyEvoMotion successfully processes thousands to millions of sequences, overcoming computational limitations.
• The tool infers evolutionary rates and detects non-Brownian evolutionary motions with subdiffusive behavior using viral genomic data.
• Demonstrated capability to weight genetic variation within populations.

Conclusions:

• PyEvoMotion is a powerful, open-source tool for analyzing molecular clock models in large genomic datasets.
• It offers adaptable features for diverse research needs and potential for novel insights into genome evolution.
• The software is available on GitHub and SourceForge.