Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

Estimating Genome-wide Phylogenies Using Probabilistic Topic Modeling

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 T opic C ontml . 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 C ontml in the PHYLIP package, which is used to generate a species tree. We evaluated the performance of T opic C ontml 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 P ac B io 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.

Challenges in Assembling the Dated Tree of Life

The assembly of a comprehensive and dated Tree of Life (ToL) remains one of the most formidable challenges in evolutionary biology. The complexity of life's history, involving both vertical and horizontal transmission of genetic information, defies its representation by a simple bifurcating phylogeny. With the advent of genome and metagenome sequencing, vast amounts of data have become available. However, employing this information for phylogeny and divergence time inference has introduced significant theoretical and computational hurdles. This perspective addresses some key methodological challenges in assembling the dated ToL, namely, the identification and classification of homologous genes, accounting for gene tree-species tree mismatch due to population-level processes along with duplication, loss, and horizontal gene transfer, and the accurate dating of evolutionary events. Ultimately, the success of this endeavor requires new approaches that integrate knowledge databases with optimized phylogenetic algorithms capable of managing complex evolutionary models.

Quantifying the uncertainty of assembly-free genome-wide distance estimates and phylogenetic relationships using subsampling

Computing distance between two genomes without alignments or even access to assemblies has many downstream analyses. However, alignment-free methods, including in the fast-growing field of genome skimming, are hampered by a significant methodological gap. While accurate methods (many k-mer-based) for assembly-free distance calculation exist, measuring the uncertainty of estimated distances has not been sufficiently studied. In this paper, we show that bootstrapping, the standard non-parametric method of measuring estimator uncertainty, is not accurate for k-mer-based methods that rely on k-mer frequency profiles. Instead, we propose using subsampling (with no replacement) in combination with a correction step to reduce the variance of the inferred distribution. We show that the distribution of distances using our procedure matches the true uncertainty of the estimator. The resulting phylogenetic support values effectively differentiate between correct and incorrect branches and identify controversial branches that change across alignment-free and alignment-based phylogenies reported in the literature.