ESPRESSO : Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data
Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Science advances - 9(2023), 3 vom: 20. Jan., Seite eabq5072 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gao, Yuan [VerfasserIn] |
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| Anmerkungen: |
Date Completed 24.01.2023 Date Revised 24.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1126/sciadv.abq5072 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM351735887 |
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| 520 | |a Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes | ||
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| 700 | 1 | |a Wang, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Kutschera, Eric |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Stephan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Kadash-Edmondson, Kathryn E |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Lan |e verfasserin |4 aut | |
| 700 | 1 | |a Xing, Yi |e verfasserin |4 aut | |
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