The 'TranSeq' 3′‐end sequencing method for high‐throughput transcriptomics and gene space refinement in plant genomes. (29th July 2018)

• Record Type: Journal Article
• Authors: Tzfadia, Oren; Bocobza, Samuel; Defoort, Jonas; Almekias‐Siegl, Efrat; Panda, Sayantan; Levy, Matan; Storme, Veronique; Rombauts, Stephane; Jaitin, Diego Adhemar; Keren‐Shaul, Hadas; Van de Peer, Yves; Aharoni, Asaph
• Is Part Of: Plant journal. Volume 96:Number 1(2018)
• Page Start: 223
• Page End: 232
• Publication Date: 2018-07-29
• Subjects: TranSeq -- RNA‐seq -- tomato -- paralogous genes -- genome annotation -- technical advance; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.14015
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: High‐throughput RNA sequencing has proven invaluable not only to explore gene expression but also for both gene prediction and genome annotation. However, RNA sequencing, carried out on tens or even hundreds of samples, requires easy and cost‐effective sample preparation methods using minute RNA amounts. Here, we present TranSeq, a high‐throughput 3′‐end sequencing procedure that requires 10‐ to 20‐fold fewer sequence reads than the current transcriptomics procedures. TranSeq significantly reduces costs and allows a greater increase in size of sample sets analyzed in a single experiment. Moreover, in comparison with other 3′‐end sequencing methods reported to date, we demonstrate here the reliability and immediate applicability of TranSeq and show that it not only provides accurate transcriptome profiles but also produces precise expression measurements of specific gene family members possessing high sequence similarity. This is difficult to achieve in standard RNA‐seq methods, in which sequence reads cover the entire transcript. Furthermore, mapping TranSeq reads to the reference tomato genome facilitated the annotation of new transcripts improving >45% of the existing gene models. Hence, we anticipate that using TranSeq will boost large‐scale transcriptome assays and increase the spatial and temporal resolution of gene expression data, in both model and non‐model plant species. Moreover, as already performed for tomato (ITAG3.0;www.solgenomics.net ), we strongly advocate its integration into current and future genome annotations. Significance Statement: High‐throughput RNA‐sequencing technologies hold great value for exploring gene expression. Here, we describe 'TranSeq', a 3′‐end‐based sequencing approach that enables comprehensive transcriptome assays. Its application in plants will probably increase the size of transcriptomics sample sets on the scale of hundreds or even thousands. TranSeq also outcompetes standard 'full transcript length'‐based RNA‐seq methods in discriminating between highly similar transcripts. Mapping TranSeq reads to the current reference tomato genome improved >45% of the gene models.