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<p>Accumulating NGS expression datasets suggest that protein-coding genes produce numerous alternatively spliced transcripts. However, this observation might be overestimated in short-read sequencing data, which often cannot accurately resolve distinct spliced isoforms and introduce ambiguity. Resolving tissue-specific expression profiles is crucial to identify bona fide translated peptide products. In this study, we identified the most highly expressed protein-coding transcripts in respective protein-coding genes by using the long-read GSE192955 dataset to better assess the dominant transcript isoforms. Using this nanopore sequencing GSE192955 long-read dataset from 30 normal human tissues, we identified 18,094 dominantly expressed representative protein-coding transcripts (Ref-Tx) from 18,557 human genes. Comparison with MANE-select transcripts revealed that 14,546 Ref-Tx transcripts matched those in the MANE-select dataset. Despite tissue or sample variations and other confounding factors (sequencing depth and annotations), GSE192955 long-read dataset has more topmost Ref-Tx transcripts and agrees better with MANE genes. Similar patterns were observed when Ref-Tx were compared with functional APPRIS annotations. Given the importance of tissue-specific expression profiles for protein-coding transcripts, we developed an expression visualization bioinformatic tool (eCPG). This webtool integrates the extensive expression information from 30 normal human tissues as well as from the GTEx project, which is designed to interrogate the dominant protein-coding transcripts.</p>

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Reference protein-coding transcripts of human genes annotated using long-read transcriptome datasets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Kuo-Feng Tung,
  • Wen-chang Lin

摘要

Accumulating NGS expression datasets suggest that protein-coding genes produce numerous alternatively spliced transcripts. However, this observation might be overestimated in short-read sequencing data, which often cannot accurately resolve distinct spliced isoforms and introduce ambiguity. Resolving tissue-specific expression profiles is crucial to identify bona fide translated peptide products. In this study, we identified the most highly expressed protein-coding transcripts in respective protein-coding genes by using the long-read GSE192955 dataset to better assess the dominant transcript isoforms. Using this nanopore sequencing GSE192955 long-read dataset from 30 normal human tissues, we identified 18,094 dominantly expressed representative protein-coding transcripts (Ref-Tx) from 18,557 human genes. Comparison with MANE-select transcripts revealed that 14,546 Ref-Tx transcripts matched those in the MANE-select dataset. Despite tissue or sample variations and other confounding factors (sequencing depth and annotations), GSE192955 long-read dataset has more topmost Ref-Tx transcripts and agrees better with MANE genes. Similar patterns were observed when Ref-Tx were compared with functional APPRIS annotations. Given the importance of tissue-specific expression profiles for protein-coding transcripts, we developed an expression visualization bioinformatic tool (eCPG). This webtool integrates the extensive expression information from 30 normal human tissues as well as from the GTEx project, which is designed to interrogate the dominant protein-coding transcripts.