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

• Published in: Scientific reports 2020-10, Vol.10 (1), p.16245-16245, Article 16245
• Main Authors: Tung, Kuo-Feng, Pan, Chao-Yu, Chen, Chao-Hsin, Lin, Wen-chang
• Format: Article
• Language: English
• Subjects: 631/114; 631/114/129/2043; 631/114/2184; Alternative splicing; Datasets as Topic; Gene Expression; Gene Expression Profiling - methods; Gene regulation; Genes - genetics; Humanities and Social Sciences; Humans; Interfaces; Investigations; Isoforms; multidisciplinary; Proteins; Proteins - genetics; Science; Science (multidisciplinary); Transcription; Transcriptome - genetics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-73081-5

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.