A simple method for data partitioning based on relative evolutionary rates
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Abstract
Background. Multiple studies have demonstrated that partitioning of molecular datasets is important in model-based phylogenetic analyses. Commonly, partitioning is done a priori based on some known properties of sequence evolution, e.g. differences in rate of evolution among codon positions of a protein-coding gene. Here we propose a new method for data partitioning based on relative evolutionary rates of the sites in the alignment of the dataset being analysed. The rates are inferred using the previously published Tree Independent Generation of Evolutionary Rates (TIGER), and the partitioning is conducted using our novel python script RatePartitions. We applied this method to eight published multi-locus phylogenetic datasets, representing different taxonomic ranks within the insect order Lepidoptera (butterflies and moths).
Methods. We used TIGER to generate relative evolutionary rates for all sites in the alignments. Then, using RatePartitions, we partitioned the data into bins based on their relative evolutionary rate. RatePartitions applies a simple formula that ensures a distribution of sites into partitions following the distribution of rates of the characters from the full dataset. This ensures that the invariable sites are placed in a partition with slowly evolving sites, avoiding the pitfalls of previously used methods, such as k-means. Different partitioning strategies were evaluated using BIC scores as calculated by PartitionFinder.
Results. In all eight datasets, partitioning using TIGER and RatePartitions was significantly better as measured by the BIC scores than other partitioning strategies, such as the commonly used partitioning by gene and codon position.
Discussion. We developed a new method of partitioning phylogenetic datasets without using any prior knowledge (e.g. DNA sequence evolution). This method is entirely based on the properties of the data being analysed and can be applied to DNA sequences (protein-coding, introns, ultra-conserved elements), protein sequences, as well as morphological characters. A likely explanation for why our method performs better than other tested partitioning strategies is that it accounts for the heterogeneity in the data to a much greater extent than when data are simply subdivided based on prior knowledge.
Cite this as
2017. A simple method for data partitioning based on relative evolutionary rates. PeerJ Preprints 5:e3414v1 https://doi.org/10.7287/peerj.preprints.3414v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ for review.
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Supplemental Information
Relative evolutionary rate estimates for codon positions of all gene fragments
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Jadranka Rota conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Tobias Malm conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, reviewed drafts of the paper.
Niklas Wahlberg conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
The raw data has been supplied as Supplemental Dataset Files.
Funding
This work was supported by the Kone Foundation (JR and TM), Academy of Finland (NW) and the Swedish Research Council (NW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
