High-throughput transcriptomics

被引:0
|
作者
Nunzio D’Agostino
Wenli Li
Dapeng Wang
机构
[1] University of Naples Federico II,Department of Agricultural Sciences
[2] USDA-ARS,Dairy Forage Research Center
[3] National Heart and Lung Institute,undefined
[4] Imperial College London,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
High-throughput transcriptomics has revolutionised the field of transcriptome research by offering a cost-effective and powerful screening tool. Standard bulk RNA sequencing (RNA-Seq) enables characterisation of the average expression profiles for individual samples and facilitates identification of the molecular functions associated with genes differentially expressed across conditions. RNA-Seq can also be applied to disentangle splicing variants and discover novel transcripts, thus contributing to a comprehensive understanding of the transcriptome landscape. A closely related technique, single-cell RNA-Seq, has enabled the study of cell-type-specific gene expressions in hundreds to thousands of cells, aiding the exploration of cell heterogeneity. Nowadays, bulk RNA-Seq and single-cell RNA-Seq serve as complementary tools to advance and accelerate the development of transcriptome-based resources. This Collection illustrates how the current global research community makes use of these techniques to address a broad range of questions in life sciences. It demonstrates the usefulness and popularity of high-throughput transcriptomics and presents the best practices and potential issues for the benefit of future end-users.
引用
收藏
相关论文
共 50 条
  • [21] Tench (Tinca tinca) high-throughput transcriptomics reveal feed dependent gut profiles
    Panicz, Remigiusz
    Klopp, Christophe
    Igielski, Rafal
    Hofsoe, Paulina
    Sadowski, Jacek
    Coller, John A., Jr.
    [J]. AQUACULTURE, 2017, 479 : 200 - 207
  • [22] Development of a Zebrafish S1500+Sentinel Gene Set for High-Throughput Transcriptomics
    Balik-Meisner, Michele R.
    Mav, Deepak
    Phadke, Dhiral P.
    Everett, Logan J.
    Shah, Ruchir R.
    Tal, Tamara
    Shepard, Peter J.
    Merrick, B. Alex
    Paules, Richard S.
    [J]. ZEBRAFISH, 2019, 16 (04) : 331 - 347
  • [23] Droplet-based microfluidics in drug discovery, transcriptomics and high-throughput molecular genetics
    Shembekar, Nachiket
    Chaipan, Chawaree
    Utharala, Ramesh
    Merten, Christoph A.
    [J]. LAB ON A CHIP, 2016, 16 (08) : 1314 - 1331
  • [24] High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells
    Bundy, Joseph L.
    Everett, Logan J.
    Rogers, Jesse D.
    Nyffeler, Jo
    Byrd, Gabrielle
    Culbreth, Megan
    Haggard, Derik E.
    Word, Laura J.
    Chambers, Bryant A.
    Fritz, Sarah Davidson -
    Harris, Felix
    Willis, Clinton
    Paul-Friedman, Katie
    Shah, Imran
    Judson, Richard
    Harrill, Joshua A.
    [J]. TOXICOLOGY AND APPLIED PHARMACOLOGY, 2024, 491
  • [25] Pathway-based assessment of single chemicals and mixtures by a high-throughput transcriptomics approach
    Xia, Pu
    Zhang, Hanxin
    Peng, Ying
    Shi, Wei
    Zhang, Xiaowei
    [J]. ENVIRONMENT INTERNATIONAL, 2020, 136
  • [26] Automation enables high-throughput and reproducible single-cell transcriptomics library preparation
    Kind, David
    Baskaran, Praveen
    Ramirez, Fidel
    Giner, Martin
    Hayes, Michael
    Santacruz, Diana
    Koss, Carolin K.
    el Kasmi, Karim C.
    Wijayawardena, Bhagya
    Viollet, Coralie
    [J]. SLAS TECHNOLOGY, 2022, 27 (02): : 135 - 142
  • [27] Strategic Use of High-Throughput Transcriptomics and Phenotypic Profiling Data in Support of Regulatory Decisions
    Harrill, J.
    Everett, L.
    Nyffeler, J.
    Willis, C.
    Brockway, R.
    Freidman, K. P.
    Shah, I.
    Judson, R.
    [J]. TOXICOLOGY LETTERS, 2021, 350 : S46 - S47
  • [28] Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury
    Li, Ting
    Tong, Weida
    Roberts, Ruth
    Liu, Zhichao
    Thakkar, Shraddha
    [J]. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2020, 8
  • [29] High-throughput transcriptomics toxicity assessment of eleven data-poor bisphenol A alternatives
    Beal, Marc A.
    Coughlan, Melanie C.
    Nunnikhoven, Andree
    Gagne, Matthew
    Barton-Maclaren, Tara S.
    Bradford, Lauren M.
    Rowan-Carroll, Andrea
    Williams, Andrew
    Meier, Matthew J.
    [J]. ENVIRONMENTAL POLLUTION, 2024, 361
  • [30] High-throughput technology (proteomics and transcriptomics) to identify and functionally characterise new egg proteins
    Gautron, J.
    Rehault-Godbert, S.
    Jonchere, V.
    Herve-Grepinet, V.
    Mann, K.
    Nys, Y.
    [J]. PRODUCTIONS ANIMALES, 2010, 23 (02): : 133 - 141