Literature DB >> 33680581

High-throughput methods for efficiently building massive phylogenies from natural history collections.

Ryan A Folk1, Heather R Kates2, Raphael LaFrance2, Douglas E Soltis2,3,4,5, Pamela S Soltis2,4,5, Robert P Guralnick2,5.   

Abstract

PREMISE: Large phylogenetic data sets have often been restricted to small numbers of loci from GenBank, and a vetted sampling-to-sequencing phylogenomic protocol scaling to thousands of species is not yet available. Here, we report a high-throughput collections-based approach that empowers researchers to explore more branches of the tree of life with numerous loci.
METHODS: We developed an integrated Specimen-to-Laboratory Information Management System (SLIMS), connecting sampling and wet lab efforts with progress tracking at each stage. Using unique identifiers encoded in QR codes and a taxonomic database, a research team can sample herbarium specimens, efficiently record the sampling event, and capture specimen images. After sampling in herbaria, images are uploaded to a citizen science platform for metadata generation, and tissue samples are moved through a simple, high-throughput, plate-based herbarium DNA extraction and sequencing protocol.
RESULTS: We applied this sampling-to-sequencing workflow to ~15,000 species, producing for the first time a data set with ~50% taxonomic representation of the "nitrogen-fixing clade" of angiosperms. DISCUSSION: The approach we present is appropriate at any taxonomic scale and is extensible to other collection types. The widespread use of large-scale sampling strategies repositions herbaria as accessible but largely untapped resources for broad taxonomic sampling with thousands of species.
© 2021 Folk et al. Applications in Plant Sciences is published by Wiley Periodicals LLC on behalf of the Botanical Society of America.

Entities:  

Keywords:  destructive sampling; herbaria; herbariomics; museomics; phylogenomics

Year:  2021        PMID: 33680581      PMCID: PMC7910806          DOI: 10.1002/aps3.11410

Source DB:  PubMed          Journal:  Appl Plant Sci        ISSN: 2168-0450            Impact factor:   1.936


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