{"doi":"10.1101/2021.12.03.470766","title":"Pandemic-scale phylogenetics","abstract":"Phylogenetics has been central to the genomic surveillance, epidemiology and contact tracing efforts during the COVD-19 pandemic. But the massive scale of genomic sequencing has rendered the pre-pandemic tools inadequate for comprehensive phylogenetic analyses. Here, we discuss the phylogenetic package that we developed to address the needs imposed by this pandemic. The package incorporates several pandemic-specific optimization and parallelization techniques and comprises four programs: UShER, matOptimize, RIPPLES and matUtils. Using high-performance computing, UShER and matOptimize maintain and refine daily a massive mutation-annotated phylogenetic tree consisting of all SARS-CoV-2 sequences available in online repositories. With UShER and RIPPLES, individual labs - even with modest compute resources - incorporate newly-sequenced SARS-CoV-2 genomes on this phylogeny and discover evidence for recombination in real-time. With matUtils, they rapidly query and visualize massive SARS-CoV-2 phylogenies. These tools have empowered scientists worldwide to study the SARS-CoV-2 evolution and transmission at an unprecedented scale, resolution and speed.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":215659,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9279,"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":265722,"name":"Bryan Thornlow","orcid":"0000-0001-6334-5186","position":1,"is_corresponding":false},{"id":632636,"name":"Alexander Krämer","orcid":"0000-0003-3630-3209","position":2,"is_corresponding":false},{"id":409298,"name":"Jakob McBroome","orcid":"0000-0002-5002-5156","position":3,"is_corresponding":false},{"id":20036,"name":"Angie S. Hinrichs","orcid":"0000-0002-1697-1130","position":4,"is_corresponding":false},{"id":32462,"name":"Russell Corbett‐Detig","orcid":"0000-0001-6535-2478","position":5,"is_corresponding":false},{"id":265720,"name":"Yatish Turakhia","orcid":"0000-0001-5600-2900","position":6,"is_corresponding":false},{"id":445508,"name":"Cheng Ye","orcid":"0009-0003-1113-6990","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-18T23:52:55.971300Z","pmid":"34927180","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":[]}