Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole.
However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories.
To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies.
We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects.
We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets.
Our method is available as an open-source R package, and assists with estimating, visualizing and interacting with a collection of bacterial gene phylogenies. dimension reduction, microbiome, non-Euclidean, statistical genetics, visualization
Sarah Teichman, Michael D. Lee, Amy D. Willis
https://www.biorxiv.org/content/10.1101/2023.08.15.553440v1
Astrobiology
