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A simple method for data partitioning based on relative evolutionary rates

Rota, Jadranka LU ; Malm, Tobias; Chazot, Nicolas LU ; Peña, Carlos and Wahlberg, Niklas LU (2018) In PeerJ 2018(8).
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 conducted simulations to assess the performance of our new method, and we applied... (More)

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 conducted simulations to assess the performance of our new method, and we applied it to eight published multi-locus phylogenetic datasets, representing different taxonomic ranks within the insect order Lepidoptera (butterflies and moths) and one phylogenomic dataset, which included ultra-conserved elements as well as introns. Methods. We used TIGER-rates to generate relative evolutionary rates for all sites in the alignments. Then, using RatePartitions, we partitioned the data into partitions 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. Simulations did not highlight any misbehaviour of our partitioning approach, even under difficult parameter conditions or missing data. In all eight phylogenetic datasets, partitioning using TIGER-rates 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. We compared the resulting topologies and node support for these eight datasets as well as for the phylogenomic dataset. Discussion. We developed a new method of partitioning phylogenetic datasets without using any prior knowledge (e.g. DNAsequence 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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
BIC, Intron, PartitionFinder, Phylogenetics, Phylogenomics, RatePartitions, TIGER, UCEs
in
PeerJ
volume
2018
issue
8
publisher
PeerJ
external identifiers
  • scopus:85052493397
ISSN
2167-8359
DOI
10.7717/peerj.5498
language
English
LU publication?
yes
id
846b688e-44fb-4ea7-b06b-db2aff1b3b72
date added to LUP
2018-10-04 14:08:26
date last changed
2019-05-14 14:29:29
@article{846b688e-44fb-4ea7-b06b-db2aff1b3b72,
  abstract     = {<p>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 conducted simulations to assess the performance of our new method, and we applied it to eight published multi-locus phylogenetic datasets, representing different taxonomic ranks within the insect order Lepidoptera (butterflies and moths) and one phylogenomic dataset, which included ultra-conserved elements as well as introns. Methods. We used TIGER-rates to generate relative evolutionary rates for all sites in the alignments. Then, using RatePartitions, we partitioned the data into partitions 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. Simulations did not highlight any misbehaviour of our partitioning approach, even under difficult parameter conditions or missing data. In all eight phylogenetic datasets, partitioning using TIGER-rates 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. We compared the resulting topologies and node support for these eight datasets as well as for the phylogenomic dataset. Discussion. We developed a new method of partitioning phylogenetic datasets without using any prior knowledge (e.g. DNAsequence 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.</p>},
  articleno    = {e5498},
  author       = {Rota, Jadranka and Malm, Tobias and Chazot, Nicolas and Peña, Carlos and Wahlberg, Niklas},
  issn         = {2167-8359},
  keyword      = {BIC,Intron,PartitionFinder,Phylogenetics,Phylogenomics,RatePartitions,TIGER,UCEs},
  language     = {eng},
  month        = {08},
  number       = {8},
  publisher    = {PeerJ},
  series       = {PeerJ},
  title        = {A simple method for data partitioning based on relative evolutionary rates},
  url          = {http://dx.doi.org/10.7717/peerj.5498},
  volume       = {2018},
  year         = {2018},
}