GSE192955
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. Overall design: Long-read sequencing (Oxford Nanopore, ONT) was used to profile full-length transcript isoforms in poly(A)-selected RNA extracted from HEK293T cells, PC3E cells, and GS689 cells, each with three replicates. Long-read sequencing was also used to examine full-length transcript isoforms in poly(A)-selected RNA extracted from 30 human tissues.