<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Marx HE</submitter><funding>National Science Foundation</funding><pagination>e11398</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7705334</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>8(11)</volume><pubmed_abstract>&lt;h4>Premise&lt;/h4>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.&lt;h4>Methods&lt;/h4>Tissue for TagSeq was sampled from multiple individuals of four species (&lt;i>Bouteloua aristidoides&lt;/i> and &lt;i>Eragrostis lehmanniana&lt;/i> [Poaceae], &lt;i>Tidestromia lanuginosa&lt;/i> [Amaranthaceae], and &lt;i>Parkinsonia florida&lt;/i> [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.&lt;h4>Results&lt;/h4>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.&lt;h4>Discussion&lt;/h4>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.</pubmed_abstract><journal>Applications in plant sciences</journal><pubmed_title>TagSeq for gene expression in non-model plants: A pilot study at the Santa Rita Experimental Range NEON core site.</pubmed_title><pmcid>PMC7705334</pmcid><funding_grant_id>1750280</funding_grant_id><funding_grant_id>1550838</funding_grant_id><pubmed_authors>Marx HE</pubmed_authors><pubmed_authors>Dlugosch KM</pubmed_authors><pubmed_authors>Scheidt S</pubmed_authors><pubmed_authors>Barker MS</pubmed_authors></additional><is_claimable>false</is_claimable><name>TagSeq for gene expression in non-model plants: A pilot study at the Santa Rita Experimental Range NEON core site.</name><description>&lt;h4>Premise&lt;/h4>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.&lt;h4>Methods&lt;/h4>Tissue for TagSeq was sampled from multiple individuals of four species (&lt;i>Bouteloua aristidoides&lt;/i> and &lt;i>Eragrostis lehmanniana&lt;/i> [Poaceae], &lt;i>Tidestromia lanuginosa&lt;/i> [Amaranthaceae], and &lt;i>Parkinsonia florida&lt;/i> [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.&lt;h4>Results&lt;/h4>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.&lt;h4>Discussion&lt;/h4>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.</description><dates><release>2020-01-01T00:00:00Z</release><publication>2020 Nov</publication><modification>2025-04-21T17:51:57.228Z</modification><creation>2021-02-20T10:54:37Z</creation></dates><accession>S-EPMC7705334</accession><cross_references><pubmed>33304661</pubmed><doi>10.1002/aps3.11398</doi></cross_references></HashMap>