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ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data.

Yuan Gao1, Feng Wang1, Robert Wang1,2

  • 1Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.

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|January 20, 2023
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Summary

ESPRESSO improves transcript isoform discovery and quantification from error-prone long-read RNA sequencing data. This computational tool enhances analysis of the full-length transcriptome, aiding eukaryotic gene expression studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long-read RNA sequencing (RNA-seq) offers insights into transcriptome variation and full-length transcript isoforms.
  • High error rates in current long-read platforms challenge accurate transcript characterization.

Purpose of the Study:

  • To develop ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads.
  • To improve splice junction identification and transcript isoform detection using individual read error profiles.

Main Methods:

  • ESPRESSO jointly analyzes all long reads aligned to a gene.
  • It incorporates individual read error profiles to enhance accuracy.
  • The tool was validated on synthetic spike-in and human RNA samples.

Main Results:

  • ESPRESSO demonstrated superior performance in both transcript isoform discovery and quantification compared to contemporary tools.
  • Analysis of ~1.1 billion nanopore RNA-seq reads from human samples was conducted.
  • The study generated a valuable dataset for transcriptome research.

Conclusions:

  • ESPRESSO provides a robust solution for analyzing error-prone long-read RNA-seq data.
  • The tool enhances the study of transcript isoforms and the overall RNA repertoire.
  • ESPRESSO and its associated dataset serve as a significant resource for eukaryotic transcriptome research.