Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands
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Abstract
SARS-CoV-2 is a novel coronavirus that has rapidly spread across the globe. In the Netherlands, the first case of SARS-CoV-2 has been notified on the 27 th of February. Here, we describe the first three weeks of the SARS-CoV-2 outbreak in the Netherlands, which started with several different introductory events from Italy, Austria, Germany and France followed by local amplification in, and later also, outside the South of the Netherlands. The timely generation of whole genome sequences combined with epidemiological investigations facilitated early decision making in an attempt to control local transmission of SARS-CoV-2 in the Netherlands.
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SciScore for 10.1101/2020.04.21.050633: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Sequence data analysis: The resulting raw sequence data was demultiplexed using qcat (https://github.com/nanoporetech/qcat) or Porechop (https://github.com/rrwick/Porechop). Porechopsuggested: (Porechop, RRID:SCR_016967)The run was monitored using RAMPART (https://artic-network.github.io/rampart/) and the analysis process was automated using snakemake22 which was used to perform near to real-time analysis with new data every 10 minutes. RAMPARTsuggested: (Rampart, RRID:SCR_016742)T… SciScore for 10.1101/2020.04.21.050633: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Sequence data analysis: The resulting raw sequence data was demultiplexed using qcat (https://github.com/nanoporetech/qcat) or Porechop (https://github.com/rrwick/Porechop). Porechopsuggested: (Porechop, RRID:SCR_016967)The run was monitored using RAMPART (https://artic-network.github.io/rampart/) and the analysis process was automated using snakemake22 which was used to perform near to real-time analysis with new data every 10 minutes. RAMPARTsuggested: (Rampart, RRID:SCR_016742)The consensus genome was extracted and positions with a coverage <30 were replaced with an “N” with a custom script using biopython and pysam. biopythonsuggested: (Biopython, RRID:SCR_007173)Phylogenetic analysis: All available full-length SARS-CoV-2 genomes were retrieved from GISAID (supplementary table 2) on the 22nd of March 2020 and aligned with the Dutch SARS-CoV-2 sequences from this study using MUSCLE. MUSCLEsuggested: (MUSCLE, RRID:SCR_011812)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.04.21.050633: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Sequence data analysisThe resulting raw sequence data was demultiplexed using qcat ( https://github.com/nanoporetech/qcat ) or Porechop ( https://github.com/rrwick/Porechop). Porechopsuggested: (Porechop, SCR_016967)The run was monitored using RAMPART ( https://artic-network.github.io/rampart/ ) and the analysis process was automated using snakemake22 which was used to perform near to real-time analysis with new data every 10 minutes . RAMPARTsuggested: (Rampart, SCR_016742)SciScore for 10.1101/2020.04.21.050633: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Sequence data analysisThe resulting raw sequence data was demultiplexed using qcat ( https://github.com/nanoporetech/qcat ) or Porechop ( https://github.com/rrwick/Porechop). Porechopsuggested: (Porechop, SCR_016967)The run was monitored using RAMPART ( https://artic-network.github.io/rampart/ ) and the analysis process was automated using snakemake22 which was used to perform near to real-time analysis with new data every 10 minutes . RAMPARTsuggested: (Rampart, SCR_016742)The consensus genome was extracted and positions with a coverage <30 were replaced with an “N” with a custom script using biopython and pysam . biopythonsuggested: (Biopython, SCR_007173)Phylogenetic analysisAll available full-length SARS-CoV-2 genomes were retrieved from GISAID ( supplementary table 2 ) on the 22nd of March 2020 and aligned with the Dutch SARS-CoV-2 sequences from this study using MUSCLE . MUSCLEsuggested: (MUSCLE, SCR_011812)BEAST analysisAll available full-length SARS-CoV-2 genomes were retrieved from GISAID24,25 on the 18th of March 2020 and downsampled to include only representative sequences from epidemiologically linked cases BEASTsuggested: (BEAST, SCR_010228)Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).
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