Quantification of gene expression while taking into account RNA alternative splicing.


Journal

Genomics
ISSN: 1089-8646
Titre abrégé: Genomics
Pays: United States
ID NLM: 8800135

Informations de publication

Date de publication:
12 2019
Historique:
received: 12 09 2018
accepted: 16 10 2018
pubmed: 27 10 2018
medline: 2 5 2020
entrez: 27 10 2018
Statut: ppublish

Résumé

Gene expression has been widely used in functional genomics research; however, the gene expressions quantified with different methods have been frequently inconsistent, thus challenging the conclusions from such research. Here we have addressed this issue, while taking into account RNA alternative splicing. We found that when a gene was subjected to RNA alternative splicing, it was impossible or difficult to properly quantify the expression of a transcript of the gene or its overall expression using quantitative real-time PCR (qPCR), Northern hybridization, microarray, or serial analysis of gene expression. Shot-gun RNA-seq was the most proper to quantify the expression of a transcript or a gene in such cases. Moreover, the expressions of individual transcripts quantified by shot-gun RNA-seq were highly reproducible (r = 0.90-0.98) between individuals. Therefore, shot-gun or full-length RNA-seq should be the method of choice to properly quantify the expression of a transcript or a gene.

Identifiants

pubmed: 30366041
pii: S0888-7543(18)30533-0
doi: 10.1016/j.ygeno.2018.10.009
pii:
doi:

Substances chimiques

Plant Proteins 0
RNA, Plant 0

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1517-1528

Informations de copyright

Copyright © 2018 Elsevier Inc. All rights reserved.

Auteurs

Meiping Zhang (M)

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, United States. Electronic address: mpzhang@tamu.edu.

Yun-Hua Liu (YH)

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, United States. Electronic address: snowpiggy@tamu.edu.

Chih-Sheng Chang (CS)

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, United States.

Hui Zhi (H)

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, United States.

Shichen Wang (S)

Genomics and Bioinformatics Service, Texas A&M AgriLife Research, College Station, TX, 77845, United States. Electronic address: Shichen.Wang@ag.tamu.edu.

Wenwei Xu (W)

Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Lubbock, TX 79403, United States. Electronic address: wxu@ag.tamu.edu.

C Wayne Smith (CW)

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, United States. Electronic address: cwsmith@tamu.edu.

Hong-Bin Zhang (HB)

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, United States. Electronic address: hbz7049@tamu.edu.

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Classifications MeSH