Journal Article

Insect Phylogenomics: Exploring the Source of Incongruence Using New Transcriptomic Data

Sabrina Simon, Apurva Narechania, Rob DeSalle and Heike Hadrys

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 4, issue 12, pages 1295-1309
Published in print January 2012 |
Published online November 2012 | e-ISSN: 1759-6653 | DOI: https://dx.doi.org/10.1093/gbe/evs104

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The evolution of the diverse insect lineages is one of the most fascinating issues in evolutionary biology. Despite extensive research in this area, the resolution of insect phylogeny especially of interordinal relationships has turned out to be still a great challenge. One of the challenges for insect systematics is the radiation of the polyneopteran lineages with several contradictory and/or unresolved relationships. Here, we provide the first transcriptomic data for three enigmatic polyneopteran orders (Dermaptera, Plecoptera, and Zoraptera) to clarify one of the most debated issues among higher insect systematics. We applied different approaches to generate 3 data sets comprising 78 species and 1,579 clusters of orthologous genes. Using these three matrices, we explored several key mechanistic problems of phylogenetic reconstruction including missing data, matrix selection, gene and taxa number/choice, and the biological function of the genes. Based on the first phylogenomic approach including these three ambiguous polyneopteran orders, we provide here conclusive support for monophyletic Polyneoptera, contesting the hypothesis of Zoraptera + Paraneoptera and Plecoptera + remaining Neoptera. In addition, we employ various approaches to evaluate data quality and highlight problematic nodes within the Insect Tree that still exist despite our phylogenomic approach. We further show how the support for these nodes or alternative hypotheses might depend on the taxon- and/or gene-sampling.

Keywords: polyneoptera; zoraptera; dermaptera; plecoptera; data quality

Journal Article.  6954 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics

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