Homology-aware Phylogenomics at Gigabase Scales

Abstract

Obstacles to inferring species trees from whole genome data sets range from algorithmic and data management challenges to the wholesale discordance in evolutionary history found in different parts of a genome. Recent work that builds trees directly from genomes by parsing them into sets of small \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$\end{document}-mer strings holds promise to streamline and simplify these efforts, but existing approaches do not account well for gene tree discordance. We describe a “seed and extend” protocol that finds nearly exact matching sets of orthologous \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$\end{document}-mers and extends them to construct data sets that can properly account for genomic heterogeneity. Exploiting an efficient suffix array data structure, sets of whole genomes can be parsed and converted into phylogenetic data matrices rapidly, with contiguous blocks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$\end{document}-mers from the same chromosome, gene, or scaffold concatenated as needed. Phylogenetic trees constructed from highly curated rice genome data and a diverse set of six other eukaryotic whole genome, transcriptome, and organellar genome data sets recovered trees nearly identical to published phylogenomic analyses, in a small fraction of the time, and requiring many fewer parameter choices. Our method’s ability to retain local homology information was demonstrated by using it to characterize gene tree discordance across the rice genome, and by its robustness to the high rate of interchromosomal gene transfer found in several rice species.

Genomes of 13 domesticated and wild rice relatives highlight genetic conservation, turnover and innovation across the genus Oryza

“…As a 'control' , we also considered the tree (O. punctata, (O. meridionalis, (O. barthii, O. rufipogon))), which replaces the problematic O. glumaepatula with another taxon in the AA clade, O. meridionalis, which is more distantly related to the Asian/African pair. Small alignment blocks sampled across the sequences of each chromosome were assembled using the program hakmer 115 , which identifies exactly matching syntenic single-copy k-mers (k = 32) found in all four taxa, extended by an ungapped 25-nt region up-and downstream of each k-mer. Typically, the final datasets included 10-15% of the original sequence data after this culling.…”

A multitiered sequence capture strategy spanning broad evolutionary scales: Application for phylogenetic and phylogeographic studies of orchids

“…We further consider the sequence capture success and the congruence of the phylogenetic results obtained via two widely‐adopted approaches: de novo (Jones & Good, 2016; McCormack et al, 2013) and reference‐guided (Johnson et al, 2016) assembly of target/exon sequence reads followed by sequence annotation and alignment. We also tested the application of a promising, yet underutilized, phylogenomic approach based on homologous substrings of sequence ( k ‐ mer ) (Sanderson et al, 2017). Gene and species trees were generated by maximum likelihood in IQ‐TREE 2 (Minh, Schmidt, et al, 2020) with branch support estimated by ultrafast bootstrap, and topological variation measured by gene concordance and site concordance factors (Minh, Hahn, et al, 2020).…”

A Not-So-Long Introduction to Computational Molecular Evolution

“…Here again, attempts have been made to using standard ML approaches such as support vector machines [123] to guide the comparison of tree shapes, for instance, [124], which can then be used in epidemiology [121], but estimating a phylogenetic tree has proved more challenging. In one notable exception, an alignment-free distance-based tree-reconstruction method was proposed [125], but its main legacy seems to be in the development of k-mers, or unaligned sequences chopped into words of length k, to reconstruct phylogenetic trees-in particular in the context of phylogenomics (phylogenetics at a genomics scale) [126,127]. To the best of our knowledge, nobody has ever tried, yet, to train a neural network or even a deep learning algorithm [128][129][130] on a database of phylogenetic trees with corresponding alignments such as TreeBASE [131] or PANDIT [132].…”