TL;DR
This paper introduces TARGet, a scalable topological data analysis framework for reconstructing ancestral recombination graphs from hundreds of genomes, capturing complex evolutionary histories involving recombination, mutation, and horizontal gene transfer.
Contribution
The paper presents a novel TDA-based method and software for inferring ancestral recombination graphs from large genomic datasets, enabling interpretation of mutation and recombination events.
Findings
Successfully applied to human recombination data
Revealed recombination hotspots and migration patterns
Scalable analysis of hundreds of genomes
Abstract
The recent explosion of genomic data has underscored the need for interpretable and comprehensive analyses that can capture complex phylogenetic relationships within and across species. Recombination, reassortment and horizontal gene transfer constitute examples of pervasive biological phenomena that cannot be captured by tree-like representations. Starting from hundreds of genomes, we are interested in the reconstruction of potential evolutionary histories leading to the observed data. Ancestral recombination graphs represent potential histories that explicitly accommodate recombination and mutation events across orthologous genomes. However, they are computationally costly to reconstruct, usually being infeasible for more than few tens of genomes. Recently, Topological Data Analysis (TDA) methods have been proposed as robust and scalable methods that can capture the genetic scale and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
