{"doi":"10.1093/bioadv/vbaf151","title":"Blackbird: structural variant detection using synthetic and low-coverage long-reads","abstract":"Abstract Motivation Recent benchmarks show that most structural variations, especially within 50–10,000 bp range cannot be resolved with short-read sequencing, but long-read structural variant callers perform better on the same datasets. However, high-coverage long-read sequencing is costly and requires substantial input DNA. Reducing coverage lowers cost but significantly impacts the performance of existing structural variation (SV) callers. Synthetic long-read technologies offer long-range information at lower cost, but leveraging them for SVs under 50 kbp remains challenging. Results We propose a novel hybrid alignment- and local-assembly-based algorithm, Blackbird, that uses synthetic long reads and low-coverage long reads to improve structural variant detection. Instead of relying on whole-genome assembly, Blackbird uses a sliding window approach and synthetic long-read barcode information to assemble local segments, integrating long reads to improve structural variant detection accuracy. We evaluated Blackbird on real human genome datasets. On the HG002 Genome in a Bottle (GIAB) benchmark, Blackbird in hybrid mode demonstrated results comparable to state-of-the-art long-read tools, while using less long-read coverage. Blackbird requires only 5× coverage to achieve F1-scores (0.835 and 0.808 for deletions and insertions) similar to PBSV and Sniffles2 using 10× PacBio Hi-Fi long-read coverage. Availability and implementation Blackbird is available at https://github.com/1dayac/Blackbird.","journal":"Bioinformatics Advances","year":2024,"id":482871,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9488,"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":955721,"name":"Rui Yang","orcid":"0000-0002-7281-7672","position":1,"is_corresponding":false},{"id":1134683,"name":"Salil Maharjan","orcid":null,"position":2,"is_corresponding":false},{"id":363450,"name":"David Danko","orcid":"0000-0002-1456-9498","position":3,"is_corresponding":false},{"id":1329,"name":"Anton Korobeynikov","orcid":"0000-0002-2937-9259","position":4,"is_corresponding":false},{"id":35202,"name":" Iman Hajirasouliha ","orcid":"0000-0002-0600-3371","position":5,"is_corresponding":false},{"id":550383,"name":"Dmitry Meleshko","orcid":"0000-0002-7398-9820","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T02:07:18.280369Z","pmid":"40630502","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":[]}