1
|
Dell’Ampio E, Meusemann K, Szucsich NU, Peters RS, Meyer B, Borner J, Petersen M, Aberer AJ, Stamatakis A, Walzl MG, Minh BQ, von Haeseler A, Ebersberger I, Pass G, Misof B. Decisive data sets in phylogenomics: lessons from studies on the phylogenetic relationships of primarily wingless insects. Mol Biol Evol 2014; 31:239-49. [PMID: 24140757 PMCID: PMC3879454 DOI: 10.1093/molbev/mst196] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Phylogenetic relationships of the primarily wingless insects are still considered unresolved. Even the most comprehensive phylogenomic studies that addressed this question did not yield congruent results. To get a grip on these problems, we here analyzed the sources of incongruence in these phylogenomic studies by using an extended transcriptome data set. Our analyses showed that unevenly distributed missing data can be severely misleading by inflating node support despite the absence of phylogenetic signal. In consequence, only decisive data sets should be used which exclusively comprise data blocks containing all taxa whose relationships are addressed. Additionally, we used Four-cluster Likelihood Mapping (FcLM) to measure the degree of congruence among genes of a data set, as a measure of support alternative to bootstrap. FcLM showed incongruent signal among genes, which in our case is correlated neither with functional class assignment of these genes nor with model misspecification due to unpartitioned analyses. The herein analyzed data set is the currently largest data set covering primarily wingless insects, but failed to elucidate their interordinal phylogenetic relationships. Although this is unsatisfying from a phylogenetic perspective, we try to show that the analyses of structure and signal within phylogenomic data can protect us from biased phylogenetic inferences due to analytical artifacts.
Collapse
Affiliation(s)
| | - Karen Meusemann
- Zoologisches Forschungsmuseum Alexander Koenig, Zentrum für Molekulare Biodiversitätsforschung (zmb), Bonn, Germany
- CSIRO Ecosystem Sciences, Australian National Insect Collection, Acton, ACT, Australia
| | | | - Ralph S. Peters
- Zoologisches Forschungsmuseum Alexander Koenig, Abteilung Arthropoda, Bonn, Germany
| | - Benjamin Meyer
- Institut für Systemische Neurowissenschaften, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Janus Borner
- Biozentrum Grindel & Zoologisches Museum, Universität Hamburg, Hamburg, Germany
| | - Malte Petersen
- Zoologisches Forschungsmuseum Alexander Koenig, Zentrum für Molekulare Biodiversitätsforschung (zmb), Bonn, Germany
| | - Andre J. Aberer
- Heidelberg Institute for Theoretical Studies (HITS), Scientific Computing Group, Heidelberg, Germany
| | - Alexandros Stamatakis
- Heidelberg Institute for Theoretical Studies (HITS), Scientific Computing Group, Heidelberg, Germany
- Karlsruher Institut für Technologie, Fakultät für Informatik, Karlsruhe, Germany
| | - Manfred G. Walzl
- Department of Integrative Zoology, University of Vienna, Vienna, Austria
| | - Bui Quang Minh
- Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna, Austria
| | - Arndt von Haeseler
- Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Ingo Ebersberger
- Institute for Cell Biology and Neuroscience, Goethe-Universität Frankfurt, Frankfurt am Main, Germany
| | - Günther Pass
- Department of Integrative Zoology, University of Vienna, Vienna, Austria
| | - Bernhard Misof
- Zoologisches Forschungsmuseum Alexander Koenig, Zentrum für Molekulare Biodiversitätsforschung (zmb), Bonn, Germany
| |
Collapse
|