{"doi":"10.1093/bioinformatics/btad179","title":"SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion","abstract":"SUMMARY: SpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1212 single-nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance (≥84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins. AVAILABILITY AND IMPLEMENTATION: SAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values.","journal":"Bioinformatics","year":2023,"id":327234,"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":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.954,"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":1047323,"name":"Aimee L. Davidson","orcid":"0000-0001-5034-5996","position":1,"is_corresponding":false},{"id":249332,"name":"Miguel de la Hoya","orcid":"0000-0002-8113-1410","position":2,"is_corresponding":false},{"id":398386,"name":"Michael T. Parsons","orcid":"0000-0003-3242-8477","position":3,"is_corresponding":false},{"id":96380,"name":"Dylan M. Glubb","orcid":null,"position":4,"is_corresponding":false},{"id":572412,"name":"Olga Kondrashova","orcid":"0000-0003-0022-5149","position":5,"is_corresponding":false},{"id":55382,"name":"Amanda B. Spurdle","orcid":"0000-0003-1337-7897","position":6,"is_corresponding":false},{"id":1012411,"name":"Daffodil M. Canson","orcid":"0000-0002-8104-822X","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T01:08:42.846627Z","pmid":"37021934","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":[]}