{"doi":"10.1038/s41587-020-00747-w","title":"Efficient hybrid de novo assembly of human genomes with WENGAN","abstract":"<jats:title>Abstract</jats:title><jats:p>Generating accurate genome assemblies of large, repeat-rich human genomes has proved difficult using only long, error-prone reads, and most human genomes assembled from long reads add accurate short reads to polish the consensus sequence. Here we report an algorithm for hybrid assembly, WENGAN, that provides very high quality at low computational cost. We demonstrate de novo assembly of four human genomes using a combination of sequencing data generated on ONT PromethION, PacBio Sequel, Illumina and MGI technology. WENGAN implements efficient algorithms to improve assembly contiguity as well as consensus quality. The resulting genome assemblies have high contiguity (contig NG50: 17.24–80.64 Mb), few assembly errors (contig NGA50: 11.8–59.59 Mb), good consensus quality (QV: 27.84–42.88) and high gene completeness (BUSCO complete: 94.6–95.2%), while consuming low computational resources (CPU hours: 187–1,200). In particular, the <jats:sc>W</jats:sc>ENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 80.64 Mb (NGA50: 59.59 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50: 57.88 Mb).</jats:p>","journal":"Nature Biotechnology","year":2021,"id":16098,"datarank":2.5867336737713744,"base_score":4.5217885770490405,"endowment":4.5217885770490405,"self_citation_contribution":0.6782682865573562,"citation_network_contribution":1.9084653872140183,"self_endowment_contribution":0.6782682865573562,"citer_contribution":1.9084653872140183,"corpus_percentile":null,"corpus_rank":null,"citation_count":91,"citer_count":87,"citers_with_citation_signal":67,"citers_with_endowment":67,"datacite_reuse_total":2,"is_dataset":false,"is_dataset_confidence":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":59539,"name":"Elena Buena-Atienza","orcid":null,"position":1,"is_corresponding":false},{"id":13890,"name":"Stephan Ossowski","orcid":"0000-0002-7416-9568","position":2,"is_corresponding":false},{"id":120113,"name":"Marie-France Sagot","orcid":"0000-0002-5933-9960","position":3,"is_corresponding":false},{"id":120112,"name":"Alex Di Genova","orcid":"0000-0002-3120-9283","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":4.5217885770490405,"endowment":4.5217885770490405,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33318652","pmcid":"PMC8041623","openalex_id":"https://openalex.org/W3110957961","authors":[],"funders":[{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"unidentified","title":"unidentified"},{"funder_name":"National Laboratory for High Performance Computing (NLHPC - Chile) Grant ECM-02.","grant_id":"","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"","title":null},{"funder_name":"Institut national de recherche en informatique et en automatique","grant_id":"","title":null}],"total_grants":4,"fwci":4.4177,"citation_percentile":0.95713939,"influential_citations":4,"citation_trend":[{"year":2020,"count":2},{"year":2021,"count":12},{"year":2022,"count":22},{"year":2023,"count":17},{"year":2024,"count":16},{"year":2025,"count":18},{"year":2026,"count":4}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://www.nature.com/articles/s41587-020-00747-w.pdf","host_type":"journal"},{"url":"https://www.nature.com/articles/s41587-020-00747-w.pdf","host_type":"HYBRID"},{"url":"https://www.nature.com/articles/s41587-020-00747-w.pdf","host_type":"publisher"},{"url":"https://www.nature.com/articles/s41587-020-00747-w","host_type":"publisher"},{"url":"https://doi.org/10.1038/s41587-020-00747-w","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/33318652","host_type":"repository"},{"url":"https://inria.hal.science/hal-03065904","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8041623","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC8041623","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC8041623?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1038/s41587-020-00747-w","host_type":""},{"url":"https://inria.hal.science/hal-03065904v1","host_type":""},{"url":"https://inria.hal.science/hal-03065904v1/document","host_type":""},{"url":"https://dx.doi.org/10.1038/s41587-020-00747-w","host_type":""}],"fields_of_study":["Genomics and Phylogenetic Studies","Chromosomal and Genetic Variations","RNA and protein synthesis mechanisms","Biology","Medicine","Computer Science","0301 basic medicine","0206 medical engineering","02 engineering and technology","03 medical and health sciences","Algorithms","Computational Biology","Contig Mapping","Genome, Human","Haploidy","High-Throughput Nucleotide Sequencing","Humans","Sequence Analysis, DNA"],"mesh_terms":["Algorithms","Haploidy","Humans","Genome, Human","Sequence Analysis, DNA","Computational Biology","Contig Mapping","High-Throughput Nucleotide Sequencing"],"keywords":["Contig","Contiguity","Sequence assembly","Genome","Human genome","Computational biology","Reference genome","Computer science","Biology","Genetics","Gene","Transcriptome","Genome, Human","High-Throughput Nucleotide Sequencing","Sequence Analysis, DNA","Haploidy","Article","[SDV] Life Sciences [q-bio]","Contig Mapping","Humans","Algorithms","[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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