Estimating Genome-Wide Phylogenies Using Probabilistic Topic Modeling.

ABSTRACT: Methods for rapidly inferring the evolutionary history of species or populations with genome-wide data are progressing, but computational constraints still limit our abilities in this area. We developed an alignment-free method to infer genome-wide phylogenies and implemented it in the Python package TopicContml. The method uses probabilistic topic modeling (specifically, Latent Dirichlet Allocation) to extract "topic" frequencies from k-mers, which are derived from multilocus DNA sequences. These extracted frequencies then serve as an input for the program Contml in the PHYLIP package, which is used to generate a species tree. We evaluated the performance of TopicContml on simulated datasets with gaps and three biological datasets: 1) 14 DNA sequence loci from two Australian bird species

SUBMITTER: Khodaei M 

PROVIDER: S-EPMC12699994 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature