DOI: 10.5061/dryad.7b470
title: Data from: Mitochondrial phylogenomics of early land plants: mitigating the effects of saturation, compositional heterogeneity, and codon-usage bias

description: Phylogenetic analyses using concatenation of genomic-scale data have been
 seen as the panacea to resolving the incongruences among inferences from
 few or single genes. However, phylogenomics may also suffer from
 systematic errors, due to the, perhaps cumulative, effects of saturation,
 among-taxa compositional (GC content) heterogeneity, or codon-usage bias
 plaguing the individual nucleotide loci that are concatenated. Here we
 provide an example of how these factors affect the inferences of the
 phylogeny of early land plants based on mitochondrial genomic data.
 Mitochondrial sequences evolve slowly in plants and hence are thought to
 be suitable for resolving deep relationships. We newly assembled
 mitochondrial genomes from 20 bryophytes, complemented these with 40 other
 streptophytes (land plants plus algal outgroups), compiling a data matrix
 of 60 taxa and 41 mitochondrial genes. Homogeneous analyses of the
 concatenated nucleotide data resolve mosses as sister-group to the
 remaining land plants. However, the corresponding translated amino acid
 data support the liverwort lineage in this position. Both results receive
 weak to moderate support in maximum likelihood analyses, but strong
 support in Bayesian inferences. Tests of alternative hypotheses using
 either nucleotide or amino-acid data provide implicit support for the
 respective optimal topologies. By analyzing the nucleotide data, we found
 that the 3rd codon positions are more saturated than the 1st and 2nd codon
 positions, and excluding these from the analyses leads to a topology
 congruent with that obtained using amino-acid data. Further, we determined
 that land plant lineages differ in their nucleotide composition, and in
 their usage of synonymous codon variants. Composition heterogeneous
 Bayesian analyses employing a non-stationary model that accounts for
 variation in among-lineage composition, and inferences from degenerated
 nucleotide data that avoids the effects of synonymous mutations that
 underlie codon-usage bias, again recovered liverworts being sister to the
 remaining land plants. These analyses indicate that the discrepancy
 between the nucleotide-based and the amino acid-based trees is caused by
 the lineage specific, parallel compositional bias, or synonymous mutations
 driving codon-usage bias, as well as saturation in the 3rd codon
 positions. While genomic data may generate highly supported phylogenetic
 trees, these inferences may be artifacts. We suggest that phylogenomic
 analyses should assess the possible impact of potential biases through
 comparisons of protein coding gene data and their amino-acids
 translations, by analyzing data modeling compositional bias, and by
 excluding nucleotide noisy signals due to saturation or codon-usage bias.
 We caution against relying on any one presentation of the data (nucleotide
 or amino acid) or any one type of analysis even when analyzing large-scale
 data sets, no matter how well-supported, without fully exploring the
 effects of substitution models.
description: Matrix_nt_nexMatrix of nucleotide sequences of 41 mitochondrial genes for 60 streptophytes.Matrix_nt.nexMatrix_aaMatrix of amino acid translations of 41 mitochondrial genes for 60 streptophytesMatrix_nt_degenerateMatrix of degenerated nucleotide sequences of 41 mitochondrial genes for 60 streptophytesstmtREV_modelSubstitutional rate matrix of amino acid translations of mitochondrial genes of streptophytesextractdataScript used for extracting the data matrix from the SEQ-GEN output filesextract_likelihood_scriptScript to extract likelihood scores from RAxML resultextractlikelihoodExtract-CDSa Perl script for extracting mitochondrial coding genes from the GenBank file.extractdataScript used for extracting the data matrix from the SEQ-GEN output filesSupplementary_Material_Liu_et_alTable S1. List of 60 taxa sampled for the mitochondrial genomic dataset in this study. Table S2. Characteristics of 41 mitochondrial genes. Table S3. Characteristics of data matrices and statistics of the best-scoring ML trees inferred from each partitioning strategy. Table S4. Average GC percentage of each plant group, and difference significance test among groups. Figure S1. The average pairwise distances of the 41mitochondrial genes. Figure S2. A summary of single gene ML tree topologies. Figure S3-S19. Supplementary trees.