{"doi":"10.1093/bioinformatics/btae129","title":"NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data","abstract":"MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. RESULTS: NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. AVAILABILITY AND IMPLEMENTATION: Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.","journal":"Bioinformatics","year":2024,"id":444281,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9489,"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":1259395,"name":"Jacob Wallace","orcid":"0009-0004-7136-5688","position":1,"is_corresponding":false},{"id":1259820,"name":"Alderik van der Heyde","orcid":null,"position":2,"is_corresponding":false},{"id":1259396,"name":"Eliza Wieman","orcid":"0000-0002-6807-7632","position":3,"is_corresponding":false},{"id":1259821,"name":"Daniel Brey","orcid":null,"position":4,"is_corresponding":false},{"id":1259397,"name":"Yiran Shi","orcid":"0000-0002-5311-3784","position":5,"is_corresponding":false},{"id":258181,"name":"Peter J. Hansen","orcid":"0000-0003-3061-9333","position":6,"is_corresponding":false},{"id":1259398,"name":"Zahra Shamsi","orcid":"0000-0002-9834-2978","position":7,"is_corresponding":false},{"id":756129,"name":"Jeremiah Zhe Liu","orcid":"0000-0002-7410-4502","position":8,"is_corresponding":false},{"id":24961,"name":"Bruce D. Gelb","orcid":"0000-0001-8527-5027","position":9,"is_corresponding":false},{"id":227322,"name":"Ali Bashir","orcid":"0000-0002-5240-9604","position":10,"is_corresponding":false},{"id":540981,"name":"Michael D. Linderman","orcid":"0000-0002-9643-7148","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T02:01:33.526738Z","pmid":"38444093","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":[]}