Jennings-Shaffer, C. | Search bioRxiv

bioRxiv2026

Unifying phylogenetic traversal and deep learning to guide tree exploration

Deep learning offers hope for more efficient phylogenetic inference methods. However, it has yet to have the transformative effect on phylogenetics that it has had in other fields. Here we present a novel approach that combines deep learning with concepts behind current successful phylogenetic algorithms. Specifically, we give the deep learning algorithm access to the output of a phylogenetic dynamic program on the sequence alignment, rather than the raw sequence alignment. The algorithm then learns features based on these phylogenetically processed versions of the sequence data, providing information to guide local tree search. For this paper, our goal is simple: predict for each edge in a tree whether it is in a maximum parsimony tree or not. Our model consists of a recurrent neural network that learns features while traversing the input tree, which are used to classify the edge. The model makes high-quality predictions for this NP-complete problem on simulated and empirical datasets for trees of various sizes. We believe it is a stepping stone towards efficient phylogenetic inference using deep learning.

bioinformatics↗

bioRxiv2025

The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure

RNA viruses like SARS-CoV-2 have a high mutation rate, which contributes to their rapid evolution. The rate of mutations depends on the mutation type (e.g., A[->]C, A[->]G, etc.) and can vary between sites in the viral genome. Understanding this variation can shed light on the mutational processes at play, and is crucial for quantitative modeling of viral evolution. Using the millions of available SARS-CoV-2 full-genome sequences, we estimate rates of synonymous mutations for all 12 possible nucleotide mutation types and examine how much these rates vary between sites. We find a surprisingly high level of variability and several striking patterns: the rates of four mutation types suddenly increase at one of two gene boundaries; the rates of most mutation types strongly depend on a sites local sequence context, with up to 56-fold differences between contexts; consistent with a previous study, the rates of some mutation types are lower at sites engaged in RNA secondary structure. A simple log-linear model of these features explains [~]15-60% of the fold-variation of mutation rates between sites, depending on mutation type; more complex models only modestly improve predictive power out of sample. We estimate the fitness effect of each mutation based on the number of times it actually occurs versus the number of times it is expected to occur based on the model. We identify several small regions of the genome where synonymous or noncoding mutations occur much less often than expected, indicative of strong purifying selection on the RNA sequence that is independent of protein sequence. Overall, this work expands our basic understanding of SARS-CoV-2s evolution by characterizing the viruss mutation process at the level of individual sites and uncovering several striking mutational patterns that arise from unknown mechanisms.

bioinformatics↗