CoV Genome Tracker: tracing genomic footprints of Covid-19 pandemic

  1. Saymon Akther
  2. Edgaras Bezrucenkovas
  3. Brian Sulkow
  4. Christopher Panlasigui
  5. Li Li
  6. Weigang Qiu
  7. Lia Di

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Abstract

Summary

Genome sequences constitute the primary evidence on the origin and spread of the 2019-2020 Covid-19 pandemic. Rapid comparative analysis of coronavirus SARS-CoV-2 genomes is critical for disease control, outbreak forecasting, and developing clinical interventions. CoV Genome Tracker is a web portal dedicated to trace Covid-19 outbreaks in real time using a haplotype network, an accurate and scalable representation of genomic changes in a rapidly evolving population. We resolve the direction of mutations by using a bat-associated genome as outgroup. At a broader evolutionary time scale, a companion browser provides gene-by-gene and codon-by-codon evolutionary rates to facilitate the search for molecular targets of clinical interventions.

Availability and Implementation

CoV Genome Tracker is publicly available at http://cov.genometracker.org and updated weekly with the data downloaded from GISAID ( http://gisaid.org ). The website is implemented with a custom JavaScript script based on jQuery ( https://jquery.com ) and D3-force ( https://github.com/d3/d3-force ).

Contact

weigang@genectr.hunter.cuny.edu , City University of New York, Hunter College

Supplementary Information

All supporting scripts developed in JavaScript, Python, BASH, and PERL programming languages are available as Open Source at the GitHub repository https://github.com/weigangq/cov-browser .

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  1. ScreenIT

    SciScore for 10.1101/2020.04.10.036343: (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
    SentencesResources
    For the Covid-19 genome browser, we download genomic sequences and associated metadata of SARS-CoV-2 isolates from GISAID (5), which are subsequently parsed with a PYTHON script (“parse-metadata.ipynb”; all scripts available in GitHub repository http://cov.genometracker.org).
    Covid-19 genome browser
    suggested: None
    We use a custom BASH script (“align-genome.sh”) to align each genome to an NCBI reference genome (isolate Wuhan-Hu-1, GenBank accession NC_045512) with Nucmer4 (17), identify genome polymorphisms with Samtools and Bcftools (18), and create a haplotype alignment using Bcftools.
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    To maximize network stability, a custom Perl script (“impute-hap.pl”) is used to trim SNP sites at genome ends where missing bases are common, discard haplotypes with more than 10% missing bases, (optionally) impute missing bases of a haplotype with homologous bases from a closest haplotype (19), and identify unique haplotypes using the BioPerl package Bio::SimpleAlign (20).
    BioPerl
    suggested: (BioPerl, RRID:SCR_002989)
    We extract coding sequences from each genome and identify orthologous gene families using BLASTp (22).
    BLASTp
    suggested: (BLASTP, RRID:SCR_001010)
    For each gene family, we obtain a codon alignment using MUSCLE and Bioaln (23,24).
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    We reconstruct maximum-likelihood trees for individual genes as well as for the whole genome based on a concatenated alignment of ten genes using FastTree (25).
    FastTree
    suggested: (FastTree, RRID:SCR_015501)
    For each gene, we estimate the maximum-parsimony number of nucleotide changes at each codon position using DNACOMP of the PHYLIP package (21).
    PHYLIP
    suggested: (PHYLIP, RRID:SCR_006244)

    Results from OddPub: Thank you for sharing your code.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 9. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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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  2. Version published to 10.1101/2020.04.10.036343v1 on bioRxiv