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A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
1Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada.
Molecular Ecology Resources
|March 29, 2022
Summary
We developed an ancestral graph model to study ecological guild evolution using metagenomic data. Our findings reveal signals of diversifying selection, moving beyond neutral drift in microbial communities.
Area of Science:
- Evolutionary biology
- Computational ecology
- Metagenomics
Background:
- Ecological communities harbor guilds that evolve over time.
- Metagenomic sampling provides a community-level view of microbial life.
- Understanding diversification processes is key to community ecology.
Purpose of the Study:
- To present an ancestral graph model for ecological guild evolution.
- To investigate signals of diversifying selection in metagenomic data.
- To develop computational tools for eco-evolutionary modeling.
Main Methods:
- Developed an ancestral graph model based on metagenomic sampling.
- Utilized a 3% sequence divergence rule to define operational taxonomic units (OTUs).
- Analyzed population genetic (joint site frequency spectrum) and ecological (abundance distribution) data.
Main Results:
- Observed deviations from neutrality in both population genetic and ecological analyses.
- Identified indirect signals indicative of diversifying selection.
- The model incorporates ecological drift, random genetic drift, and differential viability.
Conclusions:
- Metagenomic studies can detect signals of diversifying selection under specific conditions.
- The developed model provides a framework for studying eco-evolutionary dynamics.
- The computational model is implemented in C/C++ with OpenCL for GPU acceleration.
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