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Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset

With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation....

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Autores principales: Tung, Kuo-Feng, Pan, Chao-Yu, Chen, Chao-Hsin, Lin, Wen-chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530651/
https://www.ncbi.nlm.nih.gov/pubmed/33004865
http://dx.doi.org/10.1038/s41598-020-73081-5
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author Tung, Kuo-Feng
Pan, Chao-Yu
Chen, Chao-Hsin
Lin, Wen-chang
author_facet Tung, Kuo-Feng
Pan, Chao-Yu
Chen, Chao-Hsin
Lin, Wen-chang
author_sort Tung, Kuo-Feng
collection PubMed
description With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation. It is essential to exhaustively interrogate complex mRNA isoforms of protein-coding genes with an unified data resource. In order to investigate representative mRNA transcript isoforms to be utilized as transcriptome analysis references, we utilized GTEx data to establish a top-ranked transcript isoform expression data resource for human protein-coding genes. Distinctive tissue specific expression profiles and modulations could be observed for individual top-ranked transcripts of protein-coding genes. Protein-coding transcripts or genes do occupy much higher expression fraction in transcriptome data. In addition, top-ranked transcripts are the dominantly expressed ones in various normal tissues. Intriguingly, some of the top-ranked transcripts are noncoding splicing isoforms, which imply diverse gene regulation mechanisms. Comprehensive investigation on the tissue expression patterns of top-ranked transcript isoforms is crucial. Thus, we established a web tool to examine top-ranked transcript isoforms in various human normal tissue types, which provides concise transcript information and easy-to-use graphical user interfaces. Investigation of top-ranked transcript isoforms would contribute understanding on the functional significance of distinctive alternatively spliced transcript isoforms.
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spelling pubmed-75306512020-10-02 Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset Tung, Kuo-Feng Pan, Chao-Yu Chen, Chao-Hsin Lin, Wen-chang Sci Rep Article With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation. It is essential to exhaustively interrogate complex mRNA isoforms of protein-coding genes with an unified data resource. In order to investigate representative mRNA transcript isoforms to be utilized as transcriptome analysis references, we utilized GTEx data to establish a top-ranked transcript isoform expression data resource for human protein-coding genes. Distinctive tissue specific expression profiles and modulations could be observed for individual top-ranked transcripts of protein-coding genes. Protein-coding transcripts or genes do occupy much higher expression fraction in transcriptome data. In addition, top-ranked transcripts are the dominantly expressed ones in various normal tissues. Intriguingly, some of the top-ranked transcripts are noncoding splicing isoforms, which imply diverse gene regulation mechanisms. Comprehensive investigation on the tissue expression patterns of top-ranked transcript isoforms is crucial. Thus, we established a web tool to examine top-ranked transcript isoforms in various human normal tissue types, which provides concise transcript information and easy-to-use graphical user interfaces. Investigation of top-ranked transcript isoforms would contribute understanding on the functional significance of distinctive alternatively spliced transcript isoforms. Nature Publishing Group UK 2020-10-01 /pmc/articles/PMC7530651/ /pubmed/33004865 http://dx.doi.org/10.1038/s41598-020-73081-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Tung, Kuo-Feng
Pan, Chao-Yu
Chen, Chao-Hsin
Lin, Wen-chang
Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
title Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
title_full Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
title_fullStr Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
title_full_unstemmed Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
title_short Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
title_sort top-ranked expressed gene transcripts of human protein-coding genes investigated with gtex dataset
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530651/
https://www.ncbi.nlm.nih.gov/pubmed/33004865
http://dx.doi.org/10.1038/s41598-020-73081-5
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