Multilocus Phylogeny Estimation Using Probabilistic Topic Modeling

Abstract

AO_SCPLOWBSTRACTC_SCPLOWMethods 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 TO_SCPLOWOPICC_SCPLOWCO_SCPLOWONTMLC_SCPLOW. 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 CO_SCPLOWONTMLC_SCPLOW in the PHYLIP package, which is used to generate a species tree. We evaluated the performance of TO_SCPLOWOPICC_SCPLOWCO_SCPLOWONTMLC_SCPLOW 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 PO_SCPLOWACC_SCPLOWBO_SCPLOWIOC_SCPLOW sequences from 12 bird species. Our empirical results and simulated data suggest that our method is efficient and statistically robust. We also assessed the uncertainty of the estimated relationships among clades using a bootstrap procedure.