CoVizu: Rapid analysis and visualization of the global diversity of SARS-CoV-2 genomes

Ferreira, Roux-Cil; Wong, Emmanuel; Gugan, Gopi; Wade, Kaitlyn E; Liu, Molly; Baena, Laura Muñoz; Chato, Connor; Lu, B. H.; Olabode, Abayomi S; Poon, Art F. Y.

doi:10.1101/2021.07.20.453079

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…For very large datasets, it can be useful to divide them into lineages which can be analysed separately and in parallel [ 49 , 99 ]. New tools have recently emerged to deal with the very large numbers of sequences of SARS-CoV-2 genomes [ 100 , 101 ] which are likely to provide inspiration for how to improve the future scalability of bacterial genome analysis.…”

Section: Discussionmentioning

confidence: 99%

A scalable analytical approach from bacterial genomes to epidemiology

Didelot

Parkhill

2022

Phil. Trans. R. Soc. B

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Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’.

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Section: Discussionmentioning

confidence: 99%

A scalable analytical approach from bacterial genomes to epidemiology

Didelot

Parkhill

2022

Phil. Trans. R. Soc. B

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“…Numerous publicly available genomic surveillance platforms have been developed to understand the evolution and transmission of SARS-CoV-2; for example, Nextstrain [9] uses time-scaled phylogenies to connect source and transmission chains of Covid-19 outbreaks [10,11]. CoVizu [12] reconstructs robust evolutionary and epidemiological relationships among SARS-CoV-2 genomes. It provides rapid analysis and visualization of the global diversity of SARS-CoV-2.…”

Section: Introductionmentioning

confidence: 99%

COVID-MVP: an interactive visualization for tracking SARS-CoV-2 mutations, variants, and prevalence, enabled by curated functional annotations and portable genomics workflow

Anwar

Gill

Iseminger

et al. 2022

Preprint

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The SARS-CoV-2 pandemic has reemphasized the importance of genomic epidemiology to track the evolution of the virus, dynamics of epidemics, geographic origins, and the emerging variants. It is vital in understanding the epidemiological spread of the virus on global, national, and local scales. Several analytical (bioinformatics) resources have been developed for molecular surveillance. However, a resource that combines genetic mutations and functional annotations on the impact of these mutations has been lacking in SARS-CoV-2 genomics surveillance. COVID-MVP provides an interactive visualization application that summarizes the mutations and their prevalence in SARS-CoV-2 viral lineages and provides functional annotations from the literature curated in an ongoing effort, Pokay. COVID-MVP is a tool that can be used for routine surveillance including spatio-temporal analyses. We have powered the visualization through a scalable and reproducible genomic analysis workflow nf-ncov-voc wrapped in Nextflow. COVID-MVP allows users to interactively explore data and download summarized surveillance reports. COVID-MVP, Pokay, and nf-ncov-voc are open-source tools available under the Massachusetts Institute of Technology (MIT) and GPL-3.0 licenses. COVID-MVP source code is available at https://github.com/cidgoh/COVID-MVP and an instance is hosted at https://covidmvp.cidgoh.ca.

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Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2

Bleher

Hahn

Patiño-Galindo

et al. 2021

Preprint

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The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. As the virus spreads and evolves it acquires new mutations that could have important public health consequences, including higher transmissibility, morbidity, mortality, and immune evasion, among others. Ideally, we would like to quickly identify new mutations that could confer adaptive advantages to the evolving virus by leveraging the large number of SARS-CoV-2 genomes. One way of identifying adaptive mutations is by looking at convergent mutations, mutations in the same genomic position that occur independently. The large number of currently available genomes, more than a million at this moment, however precludes the efficient use of phylogeny-based techniques. Here, we establish a fast and scalable Topological Data Analysis approach for the early warning and surveillance of emerging adaptive mutations of the coronavirus SARS-CoV-2 in the ongoing COVID-19 pandemic. Our method relies on a novel topological tool for the analysis of viral genome datasets based on persistent homology. It systematically identifies convergent events in viral evolution merely by their topological footprint and thus overcomes limitations of current phylogenetic inference techniques. This allows for an unbiased and rapid analysis of large viral datasets. We introduce a new topological measure for convergent evolution and apply it to the complete GISAID dataset as of February 2021, comprising 303,651 high-quality SARS-CoV-2 isolates taken from patients all over the world since the beginning of the pandemic. A complete list of mutations showing topological signals of convergence is compiled. We find that topologically salient mutations on the receptor-binding domain appear in several variants of concern and are linked with an increase in infectivity and immune escape. Moreover, for many adaptive mutations the topological signal precedes an increase in prevalence. We demonstrate the capability of our method to effectively identify emerging adaptive mutations at an early stage. By localizing topological signals in the dataset, we are able to extract geo-temporal information about the early occurrence of emerging adaptive mutations. The identification of these mutations can help to develop an alert system to monitor mutations of concern and guide experimentalists to focus the study of specific circulating variants.

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CoVizu: Rapid analysis and visualization of the global diversity of SARS-CoV-2 genomes

Cited by 3 publications

References 25 publications

A scalable analytical approach from bacterial genomes to epidemiology

A scalable analytical approach from bacterial genomes to epidemiology

COVID-MVP: an interactive visualization for tracking SARS-CoV-2 mutations, variants, and prevalence, enabled by curated functional annotations and portable genomics workflow

Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2

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