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Updated: Mar 23, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
Published on: February 2, 2024
Evaluation of TagSeq, a reliable low-cost alternative for RNAseq
Brian K Lohman1, Jesse N Weber2, Daniel I Bolnick2
1Department of Integrative Biology, University of Texas at Austin, One University Station C0990, Austin TX 78712, USA. lohman@utexas.edu.
Abstract:
RNAseq is a relatively new tool for ecological genetics that offers researchers insight into changes in gene expression in response to a myriad of natural or experimental conditions. However, standard RNAseq methods (e.g., Illumina TruSeq® or NEBNext® ) can be cost prohibitive, especially when study designs require large sample sizes. Consequently, RNAseq is often underused as a method, or is applied to small sample sizes that confer poor statistical power. Low cost RNAseq methods could therefore enable far greater and more powerful applications of transcriptomics in ecological genetics and beyond. Standard mRNAseq is costly partly because one sequences portions of the full length of all transcripts. Such whole-mRNA data are redundant for estimates of relative gene expression. TagSeq is an alternative method that focuses sequencing effort on mRNAs' 3' end, reducing the necessary sequencing depth per sample, and thus cost. We present a revised TagSeq library construction procedure, and compare its performance against NEBNext® , the 'gold-standard' whole mRNAseq method. We built both TagSeq and NEBNext® libraries from the same biological samples, each spiked with control RNAs. We found that TagSeq measured the control RNA distribution more accurately than NEBNext® , for a fraction of the cost per sample (~10%). The higher accuracy of TagSeq was particularly apparent for transcripts of moderate to low abundance. Technical replicates of TagSeq libraries are highly correlated, and were correlated with NEBNext® results. Overall, we show that our modified TagSeq protocol is an efficient alternative to traditional whole mRNAseq, offering researchers comparable data at greatly reduced cost.
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