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Multilocus inference of species trees and DNA barcoding.

Diego Mallo1, David Posada2

  • 1Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo 36310, Spain dmallo@uvigo.es.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|August 3, 2016
PubMed
Summary

Next-generation sequencing generates vast data for phylogenetic estimation. However, multilocus data reveal incongruence, challenging DNA barcoding

Keywords:
barcode gapincomplete lineage sortingmultilocus barcodingmultispecies coalescentphylogenetic incongruencespecies tree reconstruction

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Next-generation sequencing provides unprecedented data for phylogenetic estimation.
  • Massive, multilocus datasets reveal significant phylogenetic incongruence, challenging traditional approaches.
  • This incongruence arises from stochastic error and evolutionary processes like incomplete lineage sorting and gene transfer.

Purpose of the Study:

  • To review challenges in reconstructing species history using multilocus data.
  • To compare strategies for phylogenetic analysis of multilocus datasets.
  • To discuss the utility of multilocus data in DNA barcoding.

Main Methods:

  • Explanation of evolutionary events causing species tree-gene tree discordance.
  • Comparison of popular species tree reconstruction methods.
  • Discussion of challenges and utility of these methods in barcoding.

Main Results:

  • Multilocus datasets highlight phylogenetic incongruence, violating DNA barcoding assumptions.
  • Current barcoding methods using single loci have limited statistical power.
  • Transition to multilocus barcodes can improve accuracy and enable species-tree-based strategies.

Conclusions:

  • Phylogenetic incongruence is a major challenge in species history reconstruction.
  • Multilocus data offer improved phylogenetic resolution compared to single-locus barcoding.
  • Adopting multilocus barcoding strategies can enhance accuracy and address limitations of current methods.