Orchid Phylotranscriptomics: The Prospects of Repurposing Multi-Tissue Transcriptomes for Phylogenetic Analysis and Beyond
Date
2022
Authors
Wong, Darren
Peakall, Rod
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Publisher
Frontiers Research Foundation
Abstract
The Orchidaceae is rivaled only by the Asteraceae as the largest plant family, with the estimated number of species exceeding 25,000 and encompassing more than 700 genera. To gain insights into the mechanisms driving species diversity across both global and local scales, well-supported phylogenies targeting different taxonomic groups and/or geographical regions will be crucial. High-throughput sequencing technologies have revolutionized the field of molecular phylogenetics by simplifying the process of obtaining genome-scale sequence data. Consequently, there has been an explosive growth of such data in public repositories. Here we took advantage of this unprecedented access to transcriptome data from predominantly non-phylogenetic studies to assess if it can be repurposed to gain rapid and accurate phylogenetic insights across the orchids. Exhaustive searches revealed transcriptomic data for more than 100 orchid species spanning 5 subfamilies, 13 tribes, 21 subtribes, and 50 genera that were amendable for exploratory phylotranscriptomic analysis. Next, we performed re-assembly of the transcriptomes before strategic selection of the final samples based on a gene completeness evaluation. Drawing on these data, we report phylogenetic analyses at both deep and shallow evolutionary scales via maximum likelihood and shortcut coalescent species tree methods. In this perspective, we discuss some key outcomes of this study and conclude by highlighting other complementary, albeit rarely explored, insights beyond phylogenetic analysis that repurposed multi-tissue transcriptome can offer.
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Keywords
phylogeny, orchids, transcriptome, next-generation sequencing–NGS, plastome, target sequence capture sequencing, phylogenomics, phylotranscriptomics
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Source
Frontiers in Plant Science
Type
Journal article
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Open Access
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Creative Commons Attribution 4.0 International License
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