{"doi":"10.1101/2025.11.26.690754","title":"Haplotype-resolved diploid genome inference on pangenome graphs","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Recent algorithmic advancements have shown how to utilize pangenome graphs in combination with the haplotype reconstruction framework of Li and Stephens to accurately reconstruct a haplotype from a reference pangenome graph and a set of input reads. However, significant work remains in developing techniques that utilize a pangenome graph to obtain a pair of phased haplotypes called a diploid pair.</jats:p>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>\n                    We introduce new problem formulations and scalable algorithms for inferring phased diploid genomes from a pangenome graph and a set of input reads. We implement them in our tool\n                    <jats:monospace>DipGenie</jats:monospace>\n                    . The key idea is to jointly optimize genotyping and phasing along global paths through the pangenome graph, guided by a biologically motivated recombination budget that constrains inferred haplotypes to plausible mosaics of reference haplotypes. We evaluate\n                    <jats:monospace>DipGenie</jats:monospace>\n                    on real Illumina short-read data from the highly polymorphic MHC region in 22 leave-one-out diploid experiments, benchmarking against three tools that also operate on graph structures:\n                    <jats:monospace>VG</jats:monospace>\n                    , which samples haplotypes directly from the pangenome graph, and\n                    <jats:monospace>PanGenie + Beagle</jats:monospace>\n                    and\n                    <jats:monospace>Paragraph + Beagle</jats:monospace>\n                    , which derive local graphs from a VCF panel for per-site genotyping and delegate phasing to a statistical method. At full coverage,\n                    <jats:monospace>DipGenie</jats:monospace>\n                    achieves a geometric mean switch error rate (SER) of 0.86%, which is 5.7\n                    <jats:italic>×</jats:italic>\n                    lower than\n                    <jats:monospace>PanGenie + Beagle</jats:monospace>\n                    (4.88%), 7.9\n                    <jats:italic>×</jats:italic>\n                    lower than\n                    <jats:monospace>VG</jats:monospace>\n                    (6.77%), and 13.2\n                    <jats:italic>×</jats:italic>\n                    lower than\n                    <jats:monospace>Paragraph + Beagle</jats:monospace>\n                    (11.35%). For structural variant calling,\n                    <jats:monospace>DipGenie</jats:monospace>\n                    leads with a geometric mean F1-score of 0.571, compared to 0.470 (\n                    <jats:monospace>PanGenie + Beagle</jats:monospace>\n                    ), 0.450 (\n                    <jats:monospace>VG</jats:monospace>\n                    ), and 0.379 (\n                    <jats:monospace>Paragraph + Beagle</jats:monospace>\n                    ). These advantages hold at every coverage level tested.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Availability and Implementation</jats:title>\n                  <jats:p>\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/gsc74/DipGenie\">https://github.com/gsc74/DipGenie</jats:ext-link>\n                    .\n                  </jats:p>\n                </jats:sec>","journal":null,"year":null,"id":653049,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1703877,"name":"William T. Doan","orcid":null,"position":1,"is_corresponding":false},{"id":1338078,"name":"Daniel Gibney","orcid":"0000-0003-1493-5432","position":2,"is_corresponding":false},{"id":1338076,"name":"Ghanshyam Chandra","orcid":"0000-0001-7687-4132","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Haplotype-resolved diploid genome inference on pangenome graphs","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Recent algorithmic advancements have shown how to utilize pangenome graphs in combination with the haplotype reconstruction framework of Li and Stephens to accurately reconstruct a haplotype from a reference pangenome graph and a set of input reads. However, significant work remains in developing techniques that utilize a pangenome graph to obtain a pair of phased haplotypes called a diploid pair.</jats:p>\n                <jats:sec>\n                  <jats:title>Results</jats:title>\n                  <jats:p>\n                    We introduce new problem formulations and scalable algorithms for inferring phased diploid genomes from a pangenome graph and a set of input reads. We implement them in our tool\n                    <jats:monospace>DipGenie</jats:monospace>\n                    . The key idea is to jointly optimize genotyping and phasing along global paths through the pangenome graph, guided by a biologically motivated recombination budget that constrains inferred haplotypes to plausible mosaics of reference haplotypes. We evaluate\n                    <jats:monospace>DipGenie</jats:monospace>\n                    on real Illumina short-read data from the highly polymorphic MHC region in 22 leave-one-out diploid experiments, benchmarking against three tools that also operate on graph structures:\n                    <jats:monospace>VG</jats:monospace>\n                    , which samples haplotypes directly from the pangenome graph, and\n                    <jats:monospace>PanGenie + Beagle</jats:monospace>\n                    and\n                    <jats:monospace>Paragraph + Beagle</jats:monospace>\n                    , which derive local graphs from a VCF panel for per-site genotyping and delegate phasing to a statistical method. At full coverage,\n                    <jats:monospace>DipGenie</jats:monospace>\n                    achieves a geometric mean switch error rate (SER) of 0.86%, which is 5.7\n                    <jats:italic>×</jats:italic>\n                    lower than\n                    <jats:monospace>PanGenie + Beagle</jats:monospace>\n                    (4.88%), 7.9\n                    <jats:italic>×</jats:italic>\n                    lower than\n                    <jats:monospace>VG</jats:monospace>\n                    (6.77%), and 13.2\n                    <jats:italic>×</jats:italic>\n                    lower than\n                    <jats:monospace>Paragraph + Beagle</jats:monospace>\n                    (11.35%). For structural variant calling,\n                    <jats:monospace>DipGenie</jats:monospace>\n                    leads with a geometric mean F1-score of 0.571, compared to 0.470 (\n                    <jats:monospace>PanGenie + Beagle</jats:monospace>\n                    ), 0.450 (\n                    <jats:monospace>VG</jats:monospace>\n                    ), and 0.379 (\n                    <jats:monospace>Paragraph + Beagle</jats:monospace>\n                    ). These advantages hold at every coverage level tested.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Availability and Implementation</jats:title>\n                  <jats:p>\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/gsc74/DipGenie\">https://github.com/gsc74/DipGenie</jats:ext-link>\n                    .\n                  </jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W4416758200","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/11/27/2025.11.26.690754.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/11/27/2025.11.26.690754.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2025.11.26.690754","host_type":"publisher"},{"url":"https://doi.org/10.1101/2025.11.26.690754","host_type":"repository"}],"fields_of_study":["Genetic Associations and Epidemiology","Genomics and Phylogenetic Studies","Genomics and Rare Diseases"],"mesh_terms":[],"keywords":["Haplotype","Phaser","Inference","Genome","Genotyping","Ploidy","Scalability"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T18:02:36.997161Z","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":[]}