{"doi":"10.1101/gr.278836.123","title":"Analytical validation of germline small variant detection using long-read HiFi genome sequencing","abstract":"Long-read sequencing has the capacity to interrogate difficult genomic regions and phase variants; however, short-read sequencing is more commonly implemented for clinical testing. Given the advances in long-read HiFi sequencing chemistry and variant calling, we analytically validated this technology for small variant detection (single nucleotide variants, insertions/deletions; SNVs/indels; <50 bp). HiFi genome sequencing was performed on DNA from reference materials and clinical specimen types, and accuracy results were compared to short-read genome sequencing data. HiFi genome sequencing recall and precision across Genome in a Bottle (GIAB)-defined non-difficult and difficult genomic regions (high confidence) for SNVs are >99.9% and >99.7%, respectively, and for indels are >99.8% and >99.1%, respectively. Moreover, HiFi genome sequencing outperforms short-read genome sequencing on overall SNV/indel F1-score accuracy at all paired sequencing depths, which are further stratified across 100 total GIAB-defined genomic regions for a comprehensive evaluation of performance. Of note, HiFi genome sequencing F1-scores for SNVs and indels surpass 99% at ∼15× and ∼25×, respectively. In addition, high confidence small variant concordance across all HiFi genome sequencing reproducibility assessments (two specimens, three independent sequencing data sets) are >99.8% for SNVs and >98.6% for indels, and average high confidence small variant concordance between paired blood, saliva, and swab specimens are all >99.8%. Taken together, these data underscore that long-read HiFi genome sequencing detection of SNVs and indels is very accurate and robust, which supports the implementation of this technology for clinical diagnostic testing.","journal":"Genome Research","year":2025,"id":555485,"datarank":0.2306047350709049,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.022660580902921286,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.022660580902921286,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9437,"is_data_producer":true,"deposit_databanks":{"BioProject":["PRJNA1143955"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":240805,"name":"Linda M. Liao","orcid":"0000-0002-1923-5294","position":1,"is_corresponding":false},{"id":1454271,"name":"Pun Wai Tong","orcid":null,"position":2,"is_corresponding":false},{"id":1368341,"name":"Zena Ng","orcid":"0000-0001-9789-4212","position":3,"is_corresponding":false},{"id":1454272,"name":"Thuy-mi P. Nguyen","orcid":null,"position":4,"is_corresponding":false},{"id":683441,"name":"Chandler Ho","orcid":"0009-0006-0035-058X","position":5,"is_corresponding":false},{"id":942448,"name":"Yao Yang","orcid":"0000-0003-0784-8859","position":6,"is_corresponding":false},{"id":235380,"name":"Stuart A. Scott","orcid":"0000-0001-5720-1864","position":7,"is_corresponding":false},{"id":558816,"name":"Nathan Hammond","orcid":"0000-0001-9635-2294","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-19T02:54:59.329539Z","pmid":"40216554","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":[]}