The discovery of faster spreading variants of the virus that causes coronavirus disease 2019 (COVID-19) has raised alarm. These new variants are the result of changes (called mutations) in the virus’ genetic code. Random mutations can occur each time a virus multiplies. Although most mutations do not introduce any meaningful changes, some can alter the characteristics of the virus, for instance, helping the virus to spread more easily, reinfecting people who have had COVID-19 before, or reducing the sensitivity to treatments or vaccines.

Scientists need to know about mutations in the virus that make treatments or vaccines less effective as soon as possible, so they can adjust their pandemic response. As a result, tracking these genetic changes is essential. But individual scientists or public health agencies may not have the staff, time or computer resources to extract usable information from the growing amount of genetic data available.

A free online tool created by Chen et al. may help scientists and public health officials to track changes to the virus more easily. The COVID-19 CoV Genetics tool (COVID-19 CG) can quickly provide information on which virus mutations are present in an area during a specific period. It does this by processing data on mutations found in viral genetic material collected worldwide from hundreds of thousands of people with COVID-19, which are hosted in an existing online database. The COVID-19 CG tool presents customizable, interactive visualizations of the data.

Thousands of scientists, public health agencies, and COVID-19 vaccine and treatment developers in over 100 countries are already using the COVID-19 CG tool to find the most common mutations in their area and use it for research. They can use this information to develop more effective vaccines or treatments. Chen et al. plan to update and improve the tool as more information becomes available to help advance global efforts to end the COVID-19 pandemic.

Since the beginning of the pandemic, SARS-CoV-2 genomic data has been accumulating at an unprecedented rate (400,000+ virus genomes as of February 2020 on the GISAID database) (Elbe and Buckland-Merrett, 2017; Shu and McCauley, 2017). Numerous countries have mobilized to sequence thousands of SARS-CoV-2 genomes upon the occurrence of local outbreaks, collectively and consistently contributing more than 20,000 genomes per month (Figure 1—figure supplement 1A,B). It is important to note that, despite the slow accumulation of potentially functional (nonsynonymous) mutations, there has been a steady increase in the number of variants with more than six nonsynonymous mutations compared to the WIV04 reference, an early genome of SARS-CoV-2 that was sampled in Wuhan in December 2019 (Figure 1—figure supplement 1C). To evaluate the outcomes of anti-COVID-19 measures and detect keystone events of virus evolution, it is important to track changes in SARS-CoV-2 mutation and population dynamics in a location and date-specific manner. Indeed, several countries and the National Institutes of Health (NIH) have recognized how critical it is to collect SARS-CoV-2 genomic data to support contact tracing efforts and to inform public health decisions – these are paramount to the re-opening of countries and inter-regional travel (Collins, 2020; Gudbjartsson et al., 2020; Oude Munnink et al., 2020; Virological, 2020; Rockett et al., 2020). Yet, the quantity and complexity of SARS-CoV-2 genomic data (and metadata) make it challenging and costly for the majority of scientists to stay abreast of SARS-CoV-2 mutations in a way that is meaningful to their specific research goals. Currently, each group or organization has to independently expend labor, computing costs, and, most importantly, time to curate and analyze the genomic data from GISAID before they can generate specific hypotheses about SARS-CoV-2 lineages and mutations in their population(s) of interest.

To address this challenge, we built COVID-19 CoV Genetics (COVID-19 CG, covidcg.org), a performant, interactive, and fully scalable web application that tracks SARS-CoV-2 single-nucleotide variants (SNVs), lineages, and clades without sub-sampling. COVID-19 CG is a free, open access interface that allows users to adapt analyses according to their dates and locations of interest (Figure 1A,B; data processing workflow in Figure 1—figure supplement 2). Users can also select and compare trends in SARS-CoV-2 lineage, clade, or SNV frequency across multiple locations (Figure 1C) as we will demonstrate using case studies. COVID-19 CG provides functionalities that are complementary to other existing public browsers (see Discussion) and were designed to empower these specific user groups:

Figure 1 with 2 supplements see all

Download asset Open asset

Vaccine and therapeutics developers can inform the design and testing of their vaccine, antibody, or small molecule by using COVID-19 CG to rapidly identify all of the variants in their targeted SARS-CoV-2 protein or antigen, alongside the frequency of each variant in their geographical location(s) of interest. Scientists can use COVID-19 CG to generate hypotheses and determine whether the variants present in the location of vaccine/therapeutic implementation may impact their product-specific interaction interface or antigen.

Case study of SNVs in the receptor binding domain of the SARS-CoV-2 spike

Analyzing SNVs by geography and time is critical as the frequency of each SNV may vary significantly across different regions over time. For instance, as of December 2020, an S477N mutation in the receptor binding domain (RBD) has become dominant in Australia (69% of Australian SARS-CoV-2 genotypes, all time) although it constitutes less than 6% of SARS-CoV-2 genotypes globally (Figure 2A). SNV frequency in a given region can also shift over time, for example, an RBD N439K mutation not found in Ireland prior to July was present in 42% of the genomes collected mid-July through September, peaking in August and gradually fading after (Figure 2B). Another rare RBD S477N mutation, which was found in only 1% of the Australian SARS-CoV-2 sequences before June, has constituted 84% of the sequenced June through December genomes (Figure 2C). This geographical and temporal variation is important to incorporate into the design and testing of therapeutic antibodies (such as those under development as therapeutics by Regeneron that specifically target the SARS-CoV-2 spike RBD), as well as mRNA or recombinant protein-based vaccines. This will help to assure developers of the efficacy of their therapeutics and vaccines against the SARS-CoV-2 variants that are present in the intended location of implementation.

Figure 2

Download asset Open asset

In addition, COVID-19 CG can be harnessed to track changes in SARS-CoV-2 evolution post-implementation of therapeutics and vaccines. It will be crucial to watch for rare escape variants that could resist drug- or immune-based interventions to eventually become the dominant SARS-CoV-2 variant in the community. This need was particularly emphasized by a Regeneron study that demonstrated that single amino acid variants could evolve rapidly in the SARS-CoV-2 spike to ablate binding to antibodies that had been previously selected for their ability to neutralize all known RBD variants; some of these RBD amino acid variations are already present at low frequency among human SARS-CoV-2 genomes globally (Baum et al., 2020). The authors, Baum et al., suggested that these rare escape variants could be selected under the pressure of single antibody treatment, and, therefore, advocated for the application of cocktails of antibodies that bind to different epitopes to minimize SARS-CoV-2 mutational escape. A recent study by Greaney et al., 2021 generated high-resolution ‘escape maps’ delineating RBD mutations that could potentially result in virus escape from neutralization by 10 different human antibodies. Based on lessons learnt from the rise of multidrug resistant bacteria and cancer cells, it will be of the utmost importance to continue tracking SARS-CoV-2 evolution even when multiple vaccines and therapeutics are implemented in a given human population.

Diagnostics developers can evaluate their probe, primer, or point-of-care diagnostic according to user-defined regional and temporal SARS-CoV-2 genomic variation. More than 665 established primers/probes are built into COVID-19 CG, and new diagnostics will be continually incorporated into the browser as they become publicly available. Users can also input custom coordinates or sequences to evaluate their own target sequences and design new diagnostics.

Case study of SNVs that could impact the sensitivity of diagnostic primers

A recent preprint alerted us to the finding that a common G29140T SNV, found in 22.3% of the study’s samples from Madera County, California, was adversely affecting SARS-CoV-2 detection by the NIID_2019-nCoV_N_F2 diagnostic primer used at their sequencing center; the single SNV caused a ~ 30-fold drop in the quantity of amplicon produced by the NIID_2019-nCov_N_F2/R2 primer pair (Vanaerschot et al., 2020). We used COVID-19 CG to detect other SNVs that could impact the use of this primer pair, discovering that there are SARS-CoV-2 variants in several countries with a different C29144T mutation at the very 3’ end of the same NIID_2019-nCoV_N_F2 primer (Figure 3A). The authors of the preprint, Vanaerschot et al. noted that SNVs could impact assay accuracy if diagnostic primers and probes are also being used to quantify viral loads in patients. We found that at least 10 other primer pairs could potentially be at risk in different geographical regions due to SNVs that appear proximal to the 3’ ends of primers (Figure 3B–K): China-CDC-N-F and R; NIH, Thailand, WH-NIC N-F; US CDC 2019-nCoV-N1-R; US CDC 2019-nCoV-N2-F; ARTIC-V3_nCoV-2019_11_RIGHT; ARTIC-V3_nCoV-2019_13_LEFT; ARTIC-V3_nCoV-2019_34_LEFT; ARTIC-V3_nCoV-2019_39_LEFT (note that the ARTIC primers are used for nanopore sequencing; Tyson et al., 2020); WHO N_Sarbarco_R1; and Institut Pasteur, Paris 12759Rv. Labs and clinics can use COVID-19 CG (https://covidcg.org) to check their most commonly used primers and probes against the SARS-CoV-2 sequences that are prevalent in their geographical regions. We reiterate Vanaerschot et al.’s exhortation that SARS-CoV-2 detection strategies should ideally target multiple viral genes and check for concordant Ct values. In addition, scientists have cautioned against lab-specific errors that exist among the available SARS-CoV-2 genomes, often co-localizing with commonly used primer binding sites (Turakhia et al., 2020). These errors appear to be specific to particular labs and do not affect all of the genomes on the GISAID database. Therefore, we have added a caution to our site and a link to the evolving list of problematic sites (Issues with SARS-CoV-2 sequencing data, 2020) so that users are made aware of these potential errors in some subsets of the genomes deposited on GISAID.

Figure 3

Download asset Open asset

Researchers and public health professionals can use COVID-19 CG to gain insights as to how the virus is evolving in a given population over time (e.g., in which genes are mutations occurring, and do these lead to structural or phenotypic changes?). For example, users can track D614G distributions across any region of interest over time. Figure 4 shows a variety of different D614G population dynamics in different areas. Nonetheless, we caution against inferring (1) chains or directionality of transmission and (2) changes in the transmissibility of any SARS-CoV-2 SNV based on population dynamics alone. Inconsistent sampling, sampling biases, differences in founder host population traits (even median patient age), superspreading events, regionally and temporally differential travel restrictions, and numerous other factors instead of virus biological differences can influence the global distribution of SNVs (Grubaugh et al., 2020). Our site carries the following warning: ‘Inconsistent sampling in the underlying data can result in missing data and artefacts in this visualization. Please interpret this data with care.’

Figure 4

Download asset Open asset

Case study of Australia’s new dominant SARS-CoV-2 variant

In September 2020, we observed that the SARS-CoV-2 spike S477N mutation had become more prevalent in Australia (Figure 5A). Globally, the S477N mutation was first detected in a single sample of lineage B.1.1.25 that was collected on March 19, 2020, in Victoria, Australia, and became the dominant SARS-CoV-2 variant in the region between June and September (Figure 5B). In particular, the set of SNVs that co-occur with the S477N mutation in Australia (all time, as well as prior to May 2020 before the most recent outbreak) are different from the set of co-occurring SNVs in the United Kingdom (Figure 5C) — suggesting that the S477N mutation occurred separately in the Australian and the UK lineages. However, COVID-19 CG only reflects data contributed to GISAID. Variants of interest could be present in other countries, but not yet known to the public because the sequencing centers in those countries have not collected or deposited their data in GISAID. Furthermore, in instances where only a singular, sporadic variant is detected (no sustained transmission), there is also the possibility of sequencing error resulting in incorrect lineage assignment. Due to these caveats, the genetic data must be used in combination with other types of data, such as from contact tracing efforts, before it is possible to draw conclusions about the international transmission of SARS-CoV-2 variants. In the case of the S477N variant that is now dominating in Australia, the sequencing data alone indicate that the local transmission of this variant in Australia since March 2020 or earlier cannot be ruled out.

Figure 5

Download asset Open asset

COVID-19 CG is one of a growing number of COVID-19 public browsers that analyze and visualize the SARS-CoV-2 genomes in the GISAID database, serving different and complementary user objectives. NextStrain (Hadfield et al., 2018) visualizes real-time tracking of SARS-CoV-2 evolution on a global and continental level, using phylogenetic trees, geographical maps, and entropy and frequency plots; CoV-GLUE (Singer et al., 2020) is a browsable database of amino acid replacements and indels; the UCSC SARS-CoV-2 Genome Browser is an extension of their widely used browser that enables layering of annotation tracks and new features such as conservation with similar viruses, immune epitopes, primers, and CRISPR guides (Fernandes et al., 2020); the COVID-19 Viral Genome Analysis Pipeline (Korber et al., 2020) allows for exploring mutations geographically and over time with a focus on the spike protein; COVIDep (Ahmed et al., 2020) is a browser for real-time reporting of vaccine target recommendations; Genomic Signature Analysis of Virus Causing COVID-19 (Bauer et al., 2020) visualizes the similarity between SARS-CoV-2 genomes in 2D space; SARS-CoV-2 Alignment Screen (van Dorp et al., 2020a; van Dorp et al., 2020b) is a visualization tool providing the distribution of SNPs and homoplasies; the WashU Virus Genome Browser (Flynn et al., 2020) provides different tools such as a phylogenetic tree and a genomic-coordinate track-based view of viral sequencing data. There are also websites aimed at helping users to check COVID-19 diagnostic primers and probes, for example, the SARS-CoV-2 target regions browser from the European Commission and the Status of SARS-CoV-2 detection systems (RT-PCR) browser from the University of Turin. GISAID also monitors and reports RBD mutations and assigns clades and lineages as well as genome quality. The current GISAID reports describe the geographical distribution of new mutations, total occurrence by country, mutations in commonly used diagnostics, and 3D structural maps of mutations in the four largest clades (gisaid.org/spike). GISAID additionally provides CoVsurver (gisaid.org/covsurver) for users to screen for potential phenotypic changes based on curated literature annotations; BLAST searches and large-scale phylogenetic trees and alignments; and a new functionality for tracking emerging variants (gisaid.org/variants).

COVID-19 CG provides additional complementary functionalities to those that are available on the GISAID platform, as well as these other diverse COVID-19 public browsers. COVID-19 CG (https://covidcg.org) was built to help scientists and professionals worldwide, with varying levels of bioinformatics expertise, in their real-time analysis of SARS-CoV-2 genetic data. Mainly, COVID-19 CG enables users to track in real time, without sub-sampling, lineages, clades, and SNVs (nucleotide or amino acid) across the SARS-CoV-2 genome (or any genomic region of choice) while rapidly filtering on a user-friendly interface by geographical regions, date range, and other criteria of interest to the user. In this work, we explored several case studies that serve to highlight what users can quickly achieve using COVID-19 CG. As more detailed metadata is generated by COVID-19 studies and initiatives, we will update the application to enable filtering according to patient traits such as host species (e.g., human or mink), gender, age, ethnicity, and medical condition (e.g., symptoms, hospitalization). In addition, over the next year, as more mutations accumulate across the tens of millions of COVID cases worldwide, we plan to implement a server-client model for COVID-19 CG, where genomic data is filtered and processed on the server before being sent to the client for visualization. This change should significantly reduce the computational burden of COVID-19 CG on user’s computers, and allow our application to scale to a much larger number of genomes. Our team and colleagues continue to actively use the COVID-19 CG site to quickly generate hypotheses about COVID-19 before performing a deep analysis using the data on GISAID. We anticipate that novel questions will arise over the next year and intentionally designed COVID-19 CG (https://covidcg.org) to be modular in order to continually integrate different types of COVID-19 data and build in new features. Some of these functionalities exist on other platforms, and we recommend that scientists try each browser to find the one that best meets their needs.

SARS-CoV-2 public browsers such as COVID-19 CG and others we have highlighted here help scientists around the world to parse through large quantities of SARS-CoV-2 genomic data and metadata for the purposes of informing vaccine, therapeutics, and policy development. We advocate for decision makers around the world to sustain or accelerate their sequencing of virus genomes in their geographical area. Collecting virus genomic data is particularly relevant to regions that are experiencing increases in COVID-19 cases. If only sparse genomic data are sampled, we risk the late detection of SARS-CoV-2 variants that exhibit enhanced transmissibility, virulence or resistance against therapeutics or vaccination programs in these pandemic hotspots. Furthermore, the widespread implementation of vaccines and antibody therapies could stimulate the emergence and selection of new escape variants (Baum et al., 2020). To compound these risks, SARS-CoV-2 transmission from humans to minks (and in some cases back into humans) has already been detected at farms across at least 10 countries and one wild mink in the United States (OIE - World Organisation for Animal Health, 2020). This process of species crossing, if left unchecked, can risk the spread of SARS-CoV-2 into wild animal populations and the emergence of diverse SARS-CoV-2 variants.

Coordinated sequencing and contact tracing efforts (e.g., in the United Kingdom, Singapore, the Netherlands, Italy, California, and Australia) emphasize the urgency of establishing open access platforms to evaluate trends in virus introduction into each country or region in real time. Local policymakers, public health researchers, and scientists can use global SARS-CoV-2 genetic data, in complementation with contact tracing data, to better understand which lineages were imported into their region (from which potential international locations), whether these were introduced multiple times, and if particular lineages are dying out or persisting. Labs in numerous countries are already making incredible efforts to sequence the SARS-CoV-2 variants circulating in their local populations (Figure 6). When each country actively contributes to the database of SARS-CoV-2 genomes, this protects against sampling biases that can impact the ability to perform phylogenetic analysis and interpret global SARS-CoV-2 data. Toward this goal that affects all of humanity, we advocate for the increased sequencing of SARS-CoV-2 isolates from patients (and infected animals) around the world, and for these data to be shared in as timely a manner as possible.

Figure 6

Download asset Open asset

All of the data shown in this manuscript and displayed on COVID CG (https://covidcg.org) are downloaded from the GISAID EpiCovTM database (https://www.gisaid.org). All code and relevant documentation are hosted on an open-source, publicly available GitHub repository (https://github.com/vector-engineering/COVID19-CG; copy archived at https://archive.softwareheritage.org/swh:1:rev:e9558dc11b31b908f3af142e403d33e91d417b8a/).

1. 1. Ahmed SF
   2. Quadeer AA
   3. McKay MR

   (2020) COVIDep: a web-based platform for real-time reporting of vaccine target recommendations for SARS-CoV-2

   Nature Protocols 15:2141–2142.

   https://doi.org/10.1038/s41596-020-0358-9

   • PubMed
   • Google Scholar
2. 1. Bauer DC
   2. Tay AP
   3. Wilson LOW
   4. Reti D
   5. Hosking C
   6. McAuley AJ
   7. Pharo E
   8. Todd S
   9. Stevens V
   10. Neave MJ
   11. Tachedjian M
   12. Drew TW
   13. Vasan SS

   (2020) Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak

   Transboundary and Emerging Diseases 67:1453–1462.

   https://doi.org/10.1111/tbed.13588

   • PubMed
   • Google Scholar
3. 1. Baum A
   2. Fulton BO
   3. Wloga E
   4. Copin R
   5. Pascal KE
   6. Russo V
   7. Giordano S
   8. Lanza K
   9. Negron N
   10. Ni M
   11. Wei Y
   12. Atwal GS
   13. Murphy AJ
   14. Stahl N
   15. Yancopoulos GD
   16. Kyratsous CA

   (2020) Antibody cocktail to SARS-CoV-2 spike protein prevents rapid mutational escape seen with individual antibodies

   Science 369:1014–1018.

   https://doi.org/10.1126/science.abd0831

   • PubMed
   • Google Scholar
4. Software

   1. Chen AT

   (2021) Software Heritage, version swh:1:rev:e9558dc11b31b908f3af142e403d33e91d417b8a

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:93d863542348d0b310ece4bc5fac8b716a9106c0;origin=https://github.com/vector-engineering/COVID19-CG;visit=swh:1:snp:7bcc8c40c7f48560145e456d261db959748f8fe8;anchor=swh:1:rev:e9558dc11b31b908f3af142e403d33e91d417b8a/
5. Website

   1. Collins F

   (2020) Genome data help track community spread of COVID-19. NIH director’s Blog

   Accessed July 21, 2020.

   https://directorsblog.nih.gov/2020/07/21/genome-data-helps-track-community-spread-of-covid-19/
6. 1. Dong E
   2. Du H
   3. Gardner L

   (2020) An interactive web-based dashboard to track COVID-19 in real time

   The Lancet Infectious Diseases 20:533–534.

   https://doi.org/10.1016/S1473-3099(20)30120-1

   • PubMed
   • Google Scholar
7. 1. Elbe S
   2. Buckland-Merrett G

   (2017) Data, disease and diplomacy: gisaid's innovative contribution to global health

   Global Challenges 1:33–46.

   https://doi.org/10.1002/gch2.1018

   • PubMed
   • Google Scholar
8. 1. Fernandes JD
   2. Hinrichs AS
   3. Clawson H
   4. Gonzalez JN
   5. Lee BT
   6. Nassar LR
   7. Raney BJ
   8. Rosenbloom KR
   9. Nerli S
   10. Rao AA
   11. Schmelter D
   12. Fyfe A
   13. Maulding N
   14. Zweig AS
   15. Lowe TM
   16. Ares M
   17. Corbet-Detig R
   18. Kent WJ
   19. Haussler D
   20. Haeussler M

   (2020) The UCSC SARS-CoV-2 genome browser

   Nature Genetics 52:991–998.

   https://doi.org/10.1038/s41588-020-0700-8

   • PubMed
   • Google Scholar
9. 1. Flynn JA
   2. Purushotham D
   3. Choudhary MNK
   4. Zhuo X
   5. Fan C
   6. Matt G
   7. Li D
   8. Wang T

   (2020) Exploring the coronavirus pandemic with the WashU virus genome browser

   Nature Genetics 52:986–991.

   https://doi.org/10.1038/s41588-020-0697-z

   • PubMed
   • Google Scholar
10. 1. Greaney AJ
    2. Starr TN
    3. Gilchuk P
    4. Zost SJ
    5. Binshtein E
    6. Loes AN
    7. Hilton SK
    8. Huddleston J
    9. Eguia R
    10. Crawford KHD
    11. Dingens AS
    12. Nargi RS
    13. Sutton RE
    14. Suryadevara N
    15. Rothlauf PW
    16. Liu Z
    17. Whelan SPJ
    18. Carnahan RH
    19. Crowe JE
    20. Bloom JD

    (2021) Complete mapping of mutations to the SARS-CoV-2 spike receptor-binding domain that escape antibody recognition

    Cell Host & Microbe 29:44–57.

    https://doi.org/10.1016/j.chom.2020.11.007

    • PubMed
    • Google Scholar
11. 1. Grubaugh ND
    2. Hanage WP
    3. Rasmussen AL

    (2020) Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear

    Cell 182:794–795.

    https://doi.org/10.1016/j.cell.2020.06.040

    • PubMed
    • Google Scholar
12. 1. Gudbjartsson DF
    2. Helgason A
    3. Jonsson H
    4. Magnusson OT
    5. Melsted P
    6. Norddahl GL
    7. Saemundsdottir J
    8. Sigurdsson A
    9. Sulem P
    10. Agustsdottir AB
    11. Eiriksdottir B
    12. Fridriksdottir R
    13. Gardarsdottir EE
    14. Georgsson G
    15. Gretarsdottir OS
    16. Gudmundsson KR
    17. Gunnarsdottir TR
    18. Gylfason A
    19. Holm H
    20. Jensson BO
    21. Jonasdottir A
    22. Jonsson F
    23. Josefsdottir KS
    24. Kristjansson T
    25. Magnusdottir DN
    26. le Roux L
    27. Sigmundsdottir G
    28. Sveinbjornsson G
    29. Sveinsdottir KE
    30. Sveinsdottir M
    31. Thorarensen EA
    32. Thorbjornsson B
    33. Löve A
    34. Masson G
    35. Jonsdottir I
    36. Möller AD
    37. Gudnason T
    38. Kristinsson KG
    39. Thorsteinsdottir U
    40. Stefansson K

    (2020) Spread of SARS-CoV-2 in the icelandic population

    New England Journal of Medicine 382:2302–2315.

    https://doi.org/10.1056/NEJMoa2006100

    • Google Scholar
13. 1. Hadfield J
    2. Megill C
    3. Bell SM
    4. Huddleston J
    5. Potter B
    6. Callender C
    7. Sagulenko P
    8. Bedford T
    9. Neher RA

    (2018) Nextstrain: real-time tracking of pathogen evolution

    Bioinformatics 34:4121–4123.

    https://doi.org/10.1093/bioinformatics/bty407

    • PubMed
    • Google Scholar
14. Website

    1. Issues with SARS-CoV-2 sequencing data

    (2020) Updated analysis with data from 13th November 2020

    Accessed December 20, 2020.

    https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473/14
15. 1. Korber B
    2. Fischer WM
    3. Gnanakaran S
    4. Yoon H
    5. Theiler J
    6. Abfalterer W
    7. Hengartner N
    8. Giorgi EE
    9. Bhattacharya T
    10. Foley B
    11. Hastie KM
    12. Parker MD
    13. Partridge DG
    14. Evans CM
    15. Freeman TM
    16. de Silva TI
    17. McDanal C
    18. Perez LG
    19. Tang H
    20. Moon-Walker A
    21. Whelan SP
    22. LaBranche CC
    23. Saphire EO
    24. Montefiori DC
    25. Sheffield COVID-19 Genomics Group

    (2020) Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus

    Cell 182:812–827.

    https://doi.org/10.1016/j.cell.2020.06.043

    • PubMed
    • Google Scholar
16. 1. Köster J
    2. Rahmann S

    (2012) Snakemake--a scalable bioinformatics workflow engine

    Bioinformatics 28:2520–2522.

    https://doi.org/10.1093/bioinformatics/bts480

    • PubMed
    • Google Scholar
17. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • PubMed
    • Google Scholar
18. Website

    1. OIE - World Organisation for Animal Health

    (2020) Events in animals

    Accessed January 25, 2021.

    https://www.oie.int/en/scientific-expertise/specific-information-and-recommendations/questions-and-answers-on-2019novel-coronavirus/events-in-animals/
19. 1. Oude Munnink BB
    2. Nieuwenhuijse DF
    3. Stein M
    4. O'Toole Á
    5. Haverkate M
    6. Mollers M
    7. Kamga SK
    8. Schapendonk C
    9. Pronk M
    10. Lexmond P
    11. van der Linden A
    12. Bestebroer T
    13. Chestakova I
    14. Overmars RJ
    15. van Nieuwkoop S
    16. Molenkamp R
    17. van der Eijk AA
    18. GeurtsvanKessel C
    19. Vennema H
    20. Meijer A
    21. Rambaut A
    22. van Dissel J
    23. Sikkema RS
    24. Timen A
    25. Koopmans M
    26. Dutch-Covid-19 response team

    (2020) Rapid SARS-CoV-2 whole-genome sequencing and analysis for informed public health decision-making in the netherlands

    Nature Medicine 26:1405–1410.

    https://doi.org/10.1038/s41591-020-0997-y

    • PubMed
    • Google Scholar
20. 1. Rambaut A
    2. Holmes EC
    3. O'Toole Á
    4. Hill V
    5. McCrone JT
    6. Ruis C
    7. du Plessis L
    8. Pybus OG

    (2020) A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology

    Nature Microbiology 5:1403–1407.

    https://doi.org/10.1038/s41564-020-0770-5

    • PubMed
    • Google Scholar
21. 1. Rockett RJ
    2. Arnott A
    3. Lam C
    4. Sadsad R
    5. Timms V
    6. Gray KA
    7. Eden JS
    8. Chang S
    9. Gall M
    10. Draper J
    11. Sim EM
    12. Bachmann NL
    13. Carter I
    14. Basile K
    15. Byun R
    16. O'Sullivan MV
    17. Chen SC
    18. Maddocks S
    19. Sorrell TC
    20. Dwyer DE
    21. Holmes EC
    22. Kok J
    23. Prokopenko M
    24. Sintchenko V

    (2020) Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling

    Nature Medicine 26:1398–1404.

    https://doi.org/10.1038/s41591-020-1000-7

    • PubMed
    • Google Scholar
22. 1. Shu Y
    2. McCauley J

    (2017) GISAID: global initiative on sharing all influenza data - from vision to reality

    Eurosurveillance 22:30494.

    https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494

    • PubMed
    • Google Scholar
23. Preprint

    1. Singer J
    2. Gifford R
    3. Cotten M
    4. Robertson D

    (2020) CoV-GLUE: a web application for tracking SARS-CoV-2 genomic variation

    Preprints.

    https://doi.org/10.20944/preprints202006.0225.v1

    • Google Scholar
24. 1. Tang X
    2. Wu C
    3. Li X
    4. Song Y
    5. Yao X
    6. Wu X
    7. Duan Y
    8. Zhang H
    9. Wang Y
    10. Qian Z
    11. Cui J
    12. Lu J

    (2020) On the origin and continuing evolution of SARS-CoV-2

    National Science Review 7:1012–1023.

    https://doi.org/10.1093/nsr/nwaa036

    • Google Scholar
25. 1. Turakhia Y
    2. De Maio N
    3. Thornlow B
    4. Gozashti L
    5. Lanfear R
    6. Walker CR
    7. Hinrichs AS
    8. Fernandes JD
    9. Borges R
    10. Slodkowicz G
    11. Weilguny L
    12. Haussler D
    13. Goldman N
    14. Corbett-Detig R

    (2020) Stability of SARS-CoV-2 phylogenies

    PLOS Genetics 16:e1009175.

    https://doi.org/10.1371/journal.pgen.1009175

    • PubMed
    • Google Scholar
26. Preprint

    1. Tyson JR
    2. James P
    3. Stoddart D
    4. Sparks N
    5. Wickenhagen A
    6. Hall G
    7. Choi JH
    8. Lapointe H
    9. Kamelian K
    10. Smith AD
    11. Prystajecky N
    12. Goodfellow I
    13. Wilson SJ
    14. Harrigan R
    15. Snutch TP
    16. Loman NJ
    17. Quick J

    (2020) Improvements to the ARTIC multiplex PCR method for SARS-CoV-2 genome sequencing using nanopore

    bioRxiv.

    https://doi.org/10.1101/2020.09.04.283077

    • Google Scholar
27. 1. van Dorp L
    2. Acman M
    3. Richard D
    4. Shaw LP
    5. Ford CE
    6. Ormond L
    7. Owen CJ
    8. Pang J
    9. Tan CCS
    10. Boshier FAT
    11. Ortiz AT
    12. Balloux F

    (2020a) Emergence of genomic diversity and recurrent mutations in SARS-CoV-2

    Infection, Genetics and Evolution 83:104351.

    https://doi.org/10.1016/j.meegid.2020.104351

    • Google Scholar
28. 1. van Dorp L
    2. Richard D
    3. Tan CCS
    4. Shaw LP
    5. Acman M
    6. Balloux F

    (2020b) No evidence for increased transmissibility from recurrent mutations in SARS-CoV-2

    Nature Communications 11:5986.

    https://doi.org/10.1038/s41467-020-19818-2

    • PubMed
    • Google Scholar
29. Preprint

    1. Vanaerschot M
    2. Mann SA
    3. Webber JT
    4. Kamm J
    5. Bell SM
    6. Bell J
    7. Hong SN
    8. Nguyen MP
    9. Chan LY
    10. Bhatt KD
    11. Tan M
    12. Detweiler AM
    13. Espinosa A
    14. Wu W
    15. Batson J
    16. Dynerman D
    17. Consortium C
    18. Wadford DA
    19. Puschnik AS
    20. Crawford ED

    (2020) Identification of a polymorphism in the N gene of SARS-CoV-2 that adversely impacts detection by a widely-used RT-PCR Assa

    bioRxiv.

    https://doi.org/10.1101/2020.08.25.265074

    • Google Scholar
30. Website

    1. Virological

    (2020) Preliminary analysis of SARS-CoV-2 importation & establishment of UK transmission lineages

    Accessed June 9, 2020.

    https://virological.org/t/preliminary-analysis-of-sars-cov-2-importation-establishment-of-uk-transmission-lineages/507

Author details

1. Albert Tian Chen

   Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Kevin Altschuler

   Independent web designer, Cambridge, United States

   Contribution

   Software, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Shing Hei Zhan

   1. Department of Zoology & Biodiversity Research Centre, the University of British Columbia, Vancouver, Canada
   2. Fusion Genomics Corporation, Burnaby, Canada

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing - review and editing

   Contributed equally with

   Yujia Alina Chan and Benjamin E Deverman

   Competing interests

   is a current employee and shareholder of Fusion Genomics Corporation, which develops molecular diagnostic assays for infectious diseases including COVID-19.

4. Yujia Alina Chan

   Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Conceptualization, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Shing Hei Zhan and Benjamin E Deverman

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0731-637X

5. Benjamin E Deverman

   Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Conceptualization, Resources, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Shing Hei Zhan and Yujia Alina Chan

   For correspondence

   bdeverma@broadinstitute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6223-9303

• 6,804

  views

• 439

  downloads

• 82

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.