Marine & Environmental Sciences Faculty Articles

Revisiting the Phylogeny of Zoanthidea (Cnidaria: Anthozoa): Staggered Alignment of Hypervariable Sequences Improves Species Tree Inference

ORCID

0000-0002-6485-6823

ResearcherID

M-7702-2013

Document Type

Article

Publication Title

Molecular Phylogenetics and Evolution

ISSN

1055-7903

Publication Date

1-2018

Keywords

Gene tree; Hypervariable sequences; Phylogeny-informed alignment; Species tree; Staggered alignment

Abstract

The recent rapid proliferation of novel taxon identification in the Zoanthidea has been accompanied by a parallel propagation of gene trees as a tool of species discovery, but not a corresponding increase in our understanding of phylogeny. This disparity is caused by the trade-off between the capabilities of automated DNA sequence alignment and data content of genes applied to phylogenetic inference in this group. Conserved genes or segments are easily aligned across the order, but produce poorly resolved trees; hypervariable genes or segments contain the evolutionary signal necessary for resolution and robust support, but sequence alignment is daunting. Staggered alignments are a form of phylogeny-informed sequence alignment composed of a mosaic of local and universal regions that allow phylogenetic inference to be applied to all nucleotides from both hypervariable and conserved gene segments. Comparisons between species tree phylogenies inferred from all data (staggered alignment) and hypervariable-excluded data (standard alignment) demonstrate improved confidence and greater topological agreement with other sources of data for the complete-data tree. This novel phylogeny is the most comprehensive to date (in terms of taxa and data) and can serve as an expandable tool for evolutionary hypothesis testing in the Zoanthidea. Spanish language abstract available in Text S1. Translation by L. O. Swain, DePaul University, Chicago, Illinois, 60604, USA.

DOI

10.1016/j.ympev.2017.09.008

Volume

118

First Page

1

Last Page

12

Comments

©2017 Elsevier Inc.

Additional Comments

NSF grant #s: CBET-0937987, CBET-1240416, OCE-0550599

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