TY - JOUR
T1 - The 'TranSeq' 3'-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes
AU - Tzfadia, Oren
AU - Bocobza, Samuel
AU - Defoort, Jonas
AU - Almekias-Siegl, Efrat
AU - Panda, Sayantan
AU - Levy, Matan
AU - Storme, Veronique
AU - Rombauts, Stephane
AU - Jaitin, Diego Adhemar
AU - Keren-Shaul, Hadas
AU - Van de Peer, Yves
AU - Aharoni, Asaph
N1 - © 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Arabidopsis/genetics
KW - Genes, Plant/genetics
KW - Genome, Plant/genetics
KW - High-Throughput Nucleotide Sequencing/methods
KW - Lycopersicon esculentum/genetics
KW - Sequence Analysis, RNA/methods
KW - Whole Exome Sequencing/methods
U2 - 10.1111/tpj.14015
DO - 10.1111/tpj.14015
M3 - A1: Web of Science-article
C2 - 29979480
SN - 0032-0889
VL - 96
SP - 223
EP - 232
JO - Plant Physiology
JF - Plant Physiology
IS - 1
ER -