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 or LDA) 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 distributed across nine populations, (2), 5162 loci from 80 mammal species, and (3) raw, unaligned, non-orthologous PacBio, sequences from 12 bird species. We also assessed the uncertainty of the estimated, relationships among clades using a bootstrap procedure. Our empirical results and, simulated data suggest that our method is efficient and statistically robust.