{"doi":"10.1126/sciadv.abq5072","title":"ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data","abstract":"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.","journal":"Science Advances","year":2023,"id":316700,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":94,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9407,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1020975,"name":"Feng Wang","orcid":"0000-0001-8346-4692","position":1,"is_corresponding":false},{"id":260330,"name":"Robert Wang","orcid":"0000-0003-2614-5956","position":2,"is_corresponding":false},{"id":1020976,"name":"Eric Kutschera","orcid":"0000-0002-2160-1820","position":3,"is_corresponding":false},{"id":797247,"name":"Yang Xu","orcid":"0000-0001-8997-5995","position":4,"is_corresponding":false},{"id":1020977,"name":"Stephan Xie","orcid":"0000-0002-5248-7602","position":5,"is_corresponding":false},{"id":766906,"name":"Yuanyuan Wang","orcid":"0000-0001-9472-8606","position":6,"is_corresponding":false},{"id":348290,"name":"Kathryn E. Kadash-Edmondson","orcid":"0000-0002-1437-0147","position":7,"is_corresponding":false},{"id":548366,"name":"Lan Lin","orcid":"0000-0002-9905-8928","position":8,"is_corresponding":false},{"id":263373,"name":"Yi Xing","orcid":"0000-0001-9257-7613","position":9,"is_corresponding":false},{"id":295774,"name":"Yuan Gao","orcid":"0000-0002-9510-6515","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-19T01:06:44.698773Z","pmid":"36662851","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}