{"doi":"10.1093/nar/gkae1260","title":"Machine learning-optimized targeted detection of alternative splicing","abstract":"RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.","journal":"Nucleic Acids Research","year":2024,"id":461824,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":2,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9598,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1291309,"name":"Nathaniel Islas","orcid":null,"position":1,"is_corresponding":false},{"id":1018595,"name":"San Jewell","orcid":"0000-0002-6494-8015","position":2,"is_corresponding":false},{"id":1290785,"name":"Di Wu","orcid":"0000-0002-9433-7725","position":3,"is_corresponding":false},{"id":286526,"name":"Anupama Jha","orcid":"0000-0003-3029-2086","position":4,"is_corresponding":false},{"id":809115,"name":"Caleb M. Radens","orcid":"0000-0001-5339-6908","position":5,"is_corresponding":false},{"id":51514,"name":"Jeffrey A. Pleiss","orcid":"0000-0002-2145-4007","position":6,"is_corresponding":false},{"id":302511,"name":"Kristen W. Lynch","orcid":"0000-0002-0120-8079","position":7,"is_corresponding":false},{"id":281155,"name":"Yoseph Barash","orcid":"0000-0003-3005-5048","position":8,"is_corresponding":false},{"id":695015,"name":"Peter S. Choi","orcid":"0000-0002-2820-3032","position":9,"is_corresponding":false},{"id":1290784,"name":"Kevin Yang","orcid":"0000-0002-4897-3691","position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:04:16.410510Z","pmid":"39727154","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":[]}