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Assessment of Inter-Laboratory Differences in SARS-CoV-2 Consensus Genome Assemblies between Public Health Laboratories in Australia.

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  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101509722 Publication Model: Electronic Cited Medium: Internet ISSN: 1999-4915 (Electronic) Linking ISSN: 19994915 NLM ISO Abbreviation: Viruses Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel, Switzerland : MDPI
    • Subject Terms:
      Consensus* ; Genome, Viral* ; Public Health*; Computational Biology/*standards ; Laboratories/*standards ; SARS-CoV-2/*genetics; Australia ; Computational Biology/methods ; Humans ; Phylogeny ; SARS-CoV-2/classification ; Whole Genome Sequencing
    • Abstract:
      Whole-genome sequencing of viral isolates is critical for informing transmission patterns and for the ongoing evolution of pathogens, especially during a pandemic. However, when genomes have low variability in the early stages of a pandemic, the impact of technical and/or sequencing errors increases. We quantitatively assessed inter-laboratory differences in consensus genome assemblies of 72 matched SARS-CoV-2-positive specimens sequenced at different laboratories in Sydney, Australia. Raw sequence data were assembled using two different bioinformatics pipelines in parallel, and resulting consensus genomes were compared to detect laboratory-specific differences. Matched genome sequences were predominantly concordant, with a median pairwise identity of 99.997%. Identified differences were predominantly driven by ambiguous site content. Ignoring these produced differences in only 2.3% (5/216) of pairwise comparisons, each differing by a single nucleotide. Matched samples were assigned the same Pango lineage in 98.2% (212/216) of pairwise comparisons, and were mostly assigned to the same phylogenetic clade. However, epidemiological inference based only on single nucleotide variant distances may lead to significant differences in the number of defined clusters if variant allele frequency thresholds for consensus genome generation differ between laboratories. These results underscore the need for a unified, best-practices approach to bioinformatics between laboratories working on a common outbreak problem.
    • References:
      Virus Evol. 2020 Apr 10;6(1):veaa027. (PMID: 32296544)
      PLoS Comput Biol. 2019 Apr 8;15(4):e1006650. (PMID: 30958812)
      Bioinformatics. 2014 Aug 1;30(15):2114-20. (PMID: 24695404)
      Genome Biol. 2020 Feb 7;21(1):30. (PMID: 32033565)
      PLoS Pathog. 2010 Jul 22;6(7):e1001005. (PMID: 20661479)
      Clin Infect Dis. 2021 Nov 16;73(10):1945-1946. (PMID: 33566076)
      Virus Evol. 2020 Aug 19;6(2):veaa061. (PMID: 33235813)
      Annu Rev Virol. 2020 Sep 29;7(1):63-81. (PMID: 32511081)
      Lancet Infect Dis. 2021 Sep;21(9):1246-1256. (PMID: 33857406)
      Cell. 2020 May 28;181(5):997-1003.e9. (PMID: 32359424)
      PLoS Comput Biol. 2013;9(3):e1002947. (PMID: 23555203)
      Lancet. 2020 Feb 22;395(10224):565-574. (PMID: 32007145)
      Nat Commun. 2020 Sep 1;11(1):4376. (PMID: 32873808)
      Viruses. 2021 Jan 19;13(1):. (PMID: 33477885)
      JAMA. 2021 Mar 16;325(11):1037-1038. (PMID: 33595644)
      Sci Rep. 2021 Feb 16;11(1):3934. (PMID: 33594223)
      Mol Biol Evol. 2013 Apr;30(4):772-80. (PMID: 23329690)
      Nat Commun. 2021 Jan 19;12(1):434. (PMID: 33469026)
      Cancer Res. 2017 Nov 1;77(21):e31-e34. (PMID: 29092934)
      Nat Med. 2020 Sep;26(9):1398-1404. (PMID: 32647358)
      Nat Commun. 2020 Dec 11;11(1):6351. (PMID: 33311501)
      Bioinformatics. 2018 Sep 1;34(17):i884-i890. (PMID: 30423086)
      Genome Res. 2016 Dec;26(12):1721-1729. (PMID: 27852649)
      Nature. 2017 Apr 20;544(7650):309-315. (PMID: 28405027)
      Gigascience. 2021 Feb 16;10(2):. (PMID: 33590861)
      Science. 2021 Apr 9;372(6538):. (PMID: 33658326)
      Mol Biol Evol. 2018 Feb 1;35(2):518-522. (PMID: 29077904)
      Mol Biol Evol. 2015 Jan;32(1):268-74. (PMID: 25371430)
      Nat Commun. 2020 Dec 9;11(1):6272. (PMID: 33298935)
      Nat Genet. 2021 Jun;53(6):809-816. (PMID: 33972780)
      Am J Bot. 2018 Mar;105(3):404-416. (PMID: 29729187)
      Genome Biol. 2019 Jan 8;20(1):8. (PMID: 30621750)
      Nat Microbiol. 2020 Nov;5(11):1403-1407. (PMID: 32669681)
      Syst Biol. 2019 Jan 1;68(1):32-46. (PMID: 29771371)
      Nature. 2020 Mar;579(7798):265-269. (PMID: 32015508)
    • Grant Information:
      APP1173594 Medical Research Future Fund; 2018/ECF013 Cancer Institute NSW Early Career Fellowship; No relevant number UNSW COVID-19 Rapid Response Research Initiative; 3-PDF-2020-940-A-N Juvenile Diabetes Research Foundation Postdoctoral Fellowship
    • Contributed Indexing:
      Keywords: Pango lineage; SARS-CoV-2; bioinformatics; whole-genome sequencing
    • Publication Date:
      Date Created: 20220226 Date Completed: 20220308 Latest Revision: 20220308
    • Publication Date:
      20231215
    • Accession Number:
      PMC8875182
    • Accession Number:
      10.3390/v14020185
    • Accession Number:
      35215779