Inference of Ancestral Recombination Graphs through Topological Data Analysis

Area of Science:

• Computational Biology
• Evolutionary Genetics
• Genomics

Background:

• Phylogenetic analyses struggle to represent complex evolutionary events like recombination and horizontal gene transfer.
• Traditional methods for reconstructing evolutionary histories, such as ancestral recombination graphs, are computationally intensive and infeasible for large genomic datasets.
• Topological Data Analysis (TDA) offers a scalable approach to analyze complex biological data and has shown promise in detecting recombination.

Purpose of the Study:

• To develop a novel framework for reconstructing evolutionary histories from large-scale genomic data.
• To accurately quantify the scale and identify the genomic locations of recombination events.
• To provide an interpretable method that overcomes the computational limitations of existing approaches.

Main Methods:

• Utilized Topological Data Analysis (TDA) to develop a novel computational framework for evolutionary history reconstruction.
• Built upon existing TDA methods to enhance the detection and quantification of recombination.
• Implemented the framework in a software package named TARGet for practical application.

Main Results:

• The TARGet framework successfully reconstructs evolutionary histories from hundreds of genomes.
• The method accurately quantifies the scale and identifies genomic locations of recombination events.
• Applied to diverse datasets, including population migration, human recombination, and Galápagos finch evolution, demonstrating broad applicability.

Conclusions:

• The TARGet framework provides a scalable and interpretable solution for reconstructing evolutionary histories from large genomic datasets.
• This approach effectively captures complex evolutionary events, including recombination, which are not well-represented by traditional tree-like models.
• TARGet offers a significant advancement in analyzing genomic data for evolutionary insights.