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The 'TranSeq' 3'-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes

(2018) PLANT JOURNAL. 96(1). p.223-232
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Abstract
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.
Keywords
CELL RNA-SEQ, EVOLUTION, EXPRESSION, TISSUES, TranSeq, RNA-seq, tomato, paralogous genes, genome annotation, technical, advance

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Chicago
Tzfadia, Oren, Samuel Bocobza, Jonas Defoort, Efrat Almekias-Siegl, Sayantan Panda, Matan Levy, Veronique Storme, et al. 2018. “The ‘TranSeq’ 3’-end Sequencing Method for High-throughput Transcriptomics and Gene Space Refinement in Plant Genomes.” Plant Journal 96 (1): 223–232.
APA
Tzfadia, O., Bocobza, S., Defoort, J., Almekias-Siegl, E., Panda, S., Levy, M., Storme, V., et al. (2018). The “TranSeq” 3’-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes. PLANT JOURNAL, 96(1), 223–232.
Vancouver
1.
Tzfadia O, Bocobza S, Defoort J, Almekias-Siegl E, Panda S, Levy M, et al. The “TranSeq” 3’-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes. PLANT JOURNAL. 2018;96(1):223–32.
MLA
Tzfadia, Oren, Samuel Bocobza, Jonas Defoort, et al. “The ‘TranSeq’ 3’-end Sequencing Method for High-throughput Transcriptomics and Gene Space Refinement in Plant Genomes.” PLANT JOURNAL 96.1 (2018): 223–232. Print.
@article{8577876,
  abstract     = {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 {\textrangle}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.},
  author       = {Tzfadia, Oren and Bocobza, Samuel and Defoort, Jonas and Almekias-Siegl, Efrat and Panda, Sayantan and Levy, Matan and Storme, Veronique and Rombauts, Stephane and Jaitin, Diego Adhemar and Keren-Shaul, Hadas and Van de Peer, Yves and Aharoni, Asaph},
  issn         = {0960-7412},
  journal      = {PLANT JOURNAL},
  language     = {eng},
  number       = {1},
  pages        = {223--232},
  title        = {The 'TranSeq' 3'-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes},
  url          = {http://dx.doi.org/10.1111/tpj.14015},
  volume       = {96},
  year         = {2018},
}

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