CAM: an alignment-free method to recover phylogenies using codon aversion motifs

Abstract

Background Common phylogenomic approaches for recovering phylogenies are often time-consuming and require annotations for orthologous gene relationships that are not always available. In contrast, alignment-free phylogenomic approaches typically use structure and oligomer frequencies to calculate pairwise distances between species. We have developed an approach to quickly calculate distances between species based on codon aversion. Methods Utilizing a novel alignment-free character state, we present CAM, an alignment-free approach to recover phylogenies by comparing differences in codon aversion motifs (i.e., the set of unused codons within each gene) across all genes within a species. Synonymous codon usage is non-random and differs between organisms, between genes, and even within a single gene, and many genes do not use all possible codons. We report a comprehensive analysis of codon aversion within 229,742,339 genes from 23,428 species across all kingdoms of life, and we provide an alignment-free framework for its use in a phylogenetic construct. For each species, we first construct a set of codon aversion motifs spanning all genes within that species. We define the pairwise distance between two species, A and B, as one minus the number of shared codon aversion motifs divided by the total codon aversion motifs of the species, A or B, containing the fewest motifs. This approach allows us to calculate pairwise distances even when substantial differences in the number of genes or a high rate of divergence between species exists. Finally, we use neighbor-joining to recover phylogenies. Results Using the Open Tree of Life and NCBI Taxonomy Database as expected phylogenies, our approach compares well, recovering phylogenies that largely match expected trees and are comparable to trees recovered using maximum likelihood and other alignment-free approaches. Our technique is much faster than maximum likelihood and similar in accuracy to other alignment-free approaches. Therefore, we propose that codon aversion be considered a phylogenetically conserved character that may be used in future phylogenomic studies. Availability CAM, documentation, and test files are freely available on GitHub at https://github.com/ridgelab/cam.

A comprehensive analysis of the phylogenetic signal in ramp sequences in 211 vertebrates

“…S5,S6). Table 2 shows the branch percent identity and compares www.nature.com/scientificreports/ that percent identity to other previously reported tree reconstruction algorithms and genomic features 24 including Codon Aversion Motifs, Amino Acid Motifs, Codon Pairing, Feature Frequency Profiles, the word-based methods of CVTree, ACS, Andi, and Filter-spaced word matches, as well as maximum likelihood 24,28,30,[37][38][39][40][41] . A brief description of these algorithms is provided in Supplementary Table S1.…”

“…Although this phylogeny cannot be considered the "true" tree, it is created from a conglomeration of many phylogenetic studies, and provides a useful resource for benchmarking ramp sequences as a new character state. The synthetic phylogeny was retrieved from the OTL using a previously-published parser, getOTLtree.py 30 , that references the OTL application programming interface (API) to obtain OTL taxonomy identifiers for each species and retrieves the phylogeny from the OTL database. The exact command is included in Supplementary Note S4 .…”

CUBAP: an interactive web portal for analyzing codon usage biases across populations

“…Since codon usage biases play an integral role in regulating translation rates ( 3 , 4 , 11 , 25 , 26 ), gene expression ( 7 ), mRNA and protein secondary structure ( 12 ), and can be used in phylogenetics ( 59 , 71 , 72 ), we anticipate that this resource will be widely used for cross-population analyses and gene-specific inquiries. For instance, traditional genetic analyses of complex diseases rarely evaluate the effects of synonymous codon usage biases because they are generally thought to not contribute significantly to disease.…”

How Codon Usage Bias Affects Our Ability to Recover the Tree of Life

“…The second group matches lengths of overlapping sequences (e.g., ACS (Ulitsky, et al 2006), KMACS (Leimeister and Morgenstern 2014), and Kr (Haubold, et al 2009)). The last group calculates informational content between sequences (e.g., Co-phylog (Yi and Jin 2013), FSWM (Leimeister, et al 2017), andi (Haubold, et al 2015), CAM (Miller, McKinnon, et al 2019a), and codon pairing (Miller, McKinnon, et al 2019b)). These techniques are still being developed, and new software packages are constantly updated to recover more robust trees.…”

“…That study also found that stop codon usage had the highest phylogenetic signal (Miller, Hippen, Belyeu, et al 2017), meaning a codon matrix of 64 x 64 (the probability of all codons including the stop codons transitioning to all other codons) might be better than the traditional 61 x 61 codon matrix in a likelihood framework. Codon aversion has also been used in an alignment-free context by comparing sets of codon tuples found in a genome, where each tuple is a list of codons not used in a gene (Miller, McKinnon, et al 2019a). A similar technique used codon pairing and codon pairs (i.e., the same codon being used within a ribosomal window) and was phylogenetically informative in both alignment-free and parsimony frameworks (Miller, McKinnon, et al 2019b).…”