A general species delimitation method with applications to phylogenetic placements

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ID: 290287
2013
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Abstract
Abstract Motivation: Sequence-based methods to delimit species are central to DNA taxonomy, microbial community surveys and DNA metabarcoding studies. Current approaches either rely on simple sequence similarity thresholds (OTU-picking) or on complex and compute-intensive evolutionary models. The OTU-picking methods scale well on large datasets, but the results are highly sensitive to the similarity threshold. Coalescent-based species delimitation approaches often rely on Bayesian statistics and Markov Chain Monte Carlo sampling, and can therefore only be applied to small datasets. Results: We introduce the Poisson tree processes (PTP) model to infer putative species boundaries on a given phylogenetic input tree. We also integrate PTP with our evolutionary placement algorithm (EPA-PTP) to count the number of species in phylogenetic placements. We compare our approaches with popular OTU-picking methods and the General Mixed Yule Coalescent (GMYC) model. For de novo species delimitation, the stand-alone PTP model generally outperforms GYMC as well as OTU-picking methods when evolutionary distances between species are small. PTP neither requires an ultrametric input tree nor a sequence similarity threshold as input. In the open reference species delimitation approach, EPA-PTP yields more accurate results than de novo species delimitation methods. Finally, EPA-PTP scales on large datasets because it relies on the parallel implementations of the EPA and RAxML, thereby allowing to delimit species in high-throughput sequencing data. Availability and implementation: The code is freely available at www.exelixis-lab.org/software.html. Contact: Alexandros.Stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W2125948619 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiajie Zhang, Paschalia Kapli, Pavlos Pavlidis, Alexandros Stamatakis
Journal BMC Bioinformatics
Year 2013
DOI
10.1093/bioinformatics/btt499
URL
https://academic.oup.com/bioinformatics/article-pdf/29/22/2869/17100395/btt499.pdf
Keywords Keywords not found

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