{"doi":"10.1101/2021.03.04.433952","title":"Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks","abstract":"Abstract Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read based phasing. Third-generation nanopore sequence data has demonstrated a long read length, but current interpretation methods for its novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline PEPPER-Margin-DeepVariant that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single nucleotide variant identification method at the whole genome-scale and produces high-quality single nucleotide variants in segmental duplications and low-mappability regions where short-read based genotyping fails. We show that our pipeline can provide highly-contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% to 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance than the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio-HiFi-polished).","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":213172,"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":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9503,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":109452,"name":"Trevor Pesout","orcid":"0000-0002-1443-7970","position":1,"is_corresponding":false},{"id":24447,"name":"Pi-Chuan Chang","orcid":"0000-0003-3021-6446","position":2,"is_corresponding":false},{"id":30897,"name":"Maria Nattestad","orcid":"0000-0002-4796-2894","position":3,"is_corresponding":false},{"id":550821,"name":"Alexey Kolesnikov","orcid":"0000-0002-4761-4072","position":4,"is_corresponding":false},{"id":551494,"name":"Sidharth Goel","orcid":null,"position":5,"is_corresponding":false},{"id":30866,"name":"Gunjan Baid","orcid":"0000-0002-1339-2547","position":6,"is_corresponding":false},{"id":6318,"name":"Jordan M. Eizenga","orcid":"0000-0001-8345-8356","position":7,"is_corresponding":false},{"id":108047,"name":"Karen H. Miga","orcid":"0000-0002-3670-4507","position":8,"is_corresponding":false},{"id":92305,"name":"P. Carnevali","orcid":null,"position":9,"is_corresponding":false},{"id":109466,"name":"Miten Jain","orcid":"0000-0002-4571-3982","position":10,"is_corresponding":false},{"id":295289,"name":"Andrew J. Carroll","orcid":"0000-0001-9844-730X","position":11,"is_corresponding":false},{"id":108063,"name":"Benedict Paten","orcid":"0000-0001-8863-3539","position":12,"is_corresponding":false},{"id":24544,"name":"Kishwar Shafin","orcid":"0000-0001-5252-3434","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-18T23:52:31.378994Z","pmid":null,"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":[]}