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Phylotranscriptomic consolidation of the jawed vertebrate timetree
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology. Lehrstuhl für Zoologie und Evolutionsbiologie, Department of Biology, University of Konstanz, Germany. (Burki Lab)ORCID iD: 0000-0002-3628-1137
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2017 (English)In: Nature Ecology & Evolution, ISSN 2397-334X, 1-9 p., 10.1038/s41559-017-0240-5Article in journal (Refereed) Published
Abstract [en]

Phylogenomics is extremely powerful but introduces new challenges as no agreement exists on ‘standards’ for data selection, curation and tree inference. We use jawed vertebrates (Gnathostomata) as a model to address these issues. Despite considerable efforts in resolving their evolutionary history and macroevolution, few studies have included a full phylogenetic diversity of gnathostomes, and some relationships remain controversial. We tested a new bioinformatic pipeline to assemble large and accu- rate phylogenomic datasets from RNA sequencing and found this phylotranscriptomic approach to be successful and highly cost- effective. Increased sequencing effort up to about 10 Gbp allows more genes to be recovered, but shallower sequencing (1.5 Gbp) is sufficient to obtain thousands of full-length orthologous transcripts. We reconstruct a robust and strongly supported timetree of jawed vertebrates using 7,189 nuclear genes from 100 taxa, including 23 new transcriptomes from previously unsampled key species. Gene jackknifing of genomic data corroborates the robustness of our tree and allows calculating genome-wide divergence times by overcoming gene sampling bias. Mitochondrial genomes prove insufficient to resolve the deepest relationships because of limited signal and among-lineage rate heterogeneity. Our analyses emphasize the importance of large, curated, nuclear datasets to increase the accuracy of phylogenomics and provide a reference framework for the evolutionary history of jawed vertebrates.

Place, publisher, year, edition, pages
2017. 1-9 p., 10.1038/s41559-017-0240-5
National Category
Evolutionary Biology Zoology Bioinformatics (Computational Biology)
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URN: urn:nbn:se:uu:diva-334533DOI: 10.1038/s41559-017-0240-5OAI: oai:DiVA.org:uu-334533DiVA: diva2:1159775
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2017-11-27

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Irisarri, Iker

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