{"doi":"10.1101/2022.06.13.495326","title":"Aberrant splicing prediction across human tissues","abstract":"Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models allow prioritizing rare variants for affecting splicing, their performance on predicting tissue-specific aberrant splicing remains unassessed. Here, we generated the first aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues. At 20% recall, state-of-the-art DNA-based models cap at 10% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by three-fold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to non-coding loss-of-function variant identification and to genetic diagnostics design and analytics.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":297714,"datarank":0.38211593457012066,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.05253224796968768,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.05253224796968768,"corpus_percentile":52.57987158660168,"corpus_rank":6131,"citation_count":8,"citer_count":3,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7613,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":985847,"name":"Nils Wagner","orcid":"0009-0006-5661-1646","position":1,"is_corresponding":false},{"id":985848,"name":"Florian R. Hölzlwimmer","orcid":"0000-0002-5522-2562","position":2,"is_corresponding":false},{"id":50557,"name":"Vicente A. Yépez","orcid":"0000-0001-7916-3643","position":3,"is_corresponding":false},{"id":28488,"name":"Christian Mertes","orcid":"0000-0002-1091-205X","position":4,"is_corresponding":false},{"id":19630,"name":"Holger Prokisch","orcid":"0000-0003-2379-6286","position":5,"is_corresponding":false},{"id":249406,"name":"Julien Gagneur","orcid":"0000-0002-8924-8365","position":6,"is_corresponding":false},{"id":625395,"name":"Muhammed Hasan Çelik","orcid":"0000-0001-7185-3711","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T00:31:26.161109Z","pmid":null,"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":[]}