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Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset
Kuo-Feng Tung1, Chao-Yu Pan1,2, Chao-Hsin Chen1
1Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, ROC.
Scientific Reports
|October 2, 2020
Summary
This study identifies top-ranked mRNA transcript isoforms from human protein-coding genes using GTEx data. These dominant transcripts, including some noncoding isoforms, are crucial for understanding gene expression and alternative splicing in various tissues.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- RNA-sequencing (RNA-Seq) data has advanced understanding of protein-coding gene transcript composition.
- Complex alternative splicing patterns in human protein-coding genes complicate gene expression analysis.
- A unified resource is needed to interrogate diverse mRNA isoforms for accurate gene expression studies.
Purpose of the Study:
- To establish a comprehensive data resource of top-ranked mRNA transcript isoforms for human protein-coding genes.
- To investigate tissue-specific expression profiles and modulations of these representative transcript isoforms.
- To develop a user-friendly web tool for examining these transcript isoforms across human normal tissues.
Main Methods:
- Utilized the Genotype-Tissue Expression (GTEx) dataset to identify and rank mRNA transcript isoforms.
- Analyzed tissue-specific expression patterns and dominance of identified top-ranked transcripts.
- Developed a web-based tool with graphical user interfaces for data visualization and exploration.
Main Results:
- Established a data resource of top-ranked transcript isoforms for human protein-coding genes.
- Observed distinct tissue-specific expression profiles and modulations for individual top-ranked transcripts.
- Found that top-ranked transcripts, including some noncoding splicing isoforms, are dominantly expressed in normal tissues.
Conclusions:
- Top-ranked transcript isoforms represent the dominant expression forms in human transcriptome data.
- The identified noncoding splicing isoforms suggest complex gene regulation mechanisms.
- This resource and associated tool aid in understanding the functional significance of alternatively spliced isoforms and improve transcriptome analysis.
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