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dc.contributor.authorMarx, Hannah E.
dc.contributor.authorScheidt, Stephen
dc.contributor.authorBarker, Michael S.
dc.contributor.authorDlugosch, Katrina M.
dc.date.accessioned2021-05-04T01:49:58Z
dc.date.available2021-05-04T01:49:58Z
dc.date.issued2020-11-22
dc.identifier.citationMarx, H. E., Scheidt, S., Barker, M. S., & Dlugosch, K. M. (2020). TagSeq for gene expression in non‐model plants: A pilot study at the Santa Rita Experimental Range NEON core site. Applications in Plant Sciences, 8(11), e11398.en_US
dc.identifier.issn2168-0450
dc.identifier.doi10.1002/aps3.11398
dc.identifier.urihttp://hdl.handle.net/10150/658115
dc.description.abstractPremise TagSeq is a cost-effective approach for gene expression studies requiring a large number of samples. To date, TagSeq studies in plants have been limited to those with a high-quality reference genome. We tested the suitability of reference transcriptomes for TagSeq in non-model plants, as part of a study of natural gene expression variation at the Santa Rita Experimental Range National Ecological Observatory Network (NEON) core site. Methods Tissue for TagSeq was sampled from multiple individuals of four species (Bouteloua aristidoides and Eragrostis lehmanniana [Poaceae], Tidestromia lanuginosa [Amaranthaceae], and Parkinsonia florida [Fabaceae]) at two locations on three dates (56 samples total). One sample per species was used to create a reference transcriptome via standard RNA-seq. TagSeq performance was assessed by recovery of reference loci, specificity of tag alignments, and variation among samples. Results A high fraction of tags aligned to each reference and mapped uniquely. Expression patterns were quantifiable for tens of thousands of loci, which revealed consistent spatial differentiation in expression for all species. Discussion TagSeq using de novo reference transcriptomes was an effective approach to quantifying gene expression in this study. Tags were highly locus specific and generated biologically informative profiles for four non-model plant species.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.language.isoenen_US
dc.publisherWILEYen_US
dc.rights© 2020 Marx et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectgene expressionen_US
dc.subjectNational Ecological Observatory Network (NEON)en_US
dc.subjectRNA-seqen_US
dc.subjectSonoran Deserten_US
dc.subjectTagSeqen_US
dc.subjecttranscriptomeen_US
dc.titleTagSeq for gene expression in non‐model plants: A pilot study at the Santa Rita Experimental Range NEON core siteen_US
dc.typeArticleen_US
dc.identifier.eissn2168-0450
dc.contributor.departmentUniv Arizona, Dept Ecol & Evolutionary Biolen_US
dc.identifier.journalAPPLICATIONS IN PLANT SCIENCESen_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleApplications in Plant Sciences
dc.source.volume8
dc.source.issue11
refterms.dateFOA2021-05-04T01:50:00Z


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© 2020 Marx et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as © 2020 Marx et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.