Phylogenetic network analysis of SARS-CoV-2 genomes

Forster, Peter; Forster, Lucy; Renfrew, Colin; Förster, Michael

doi:10.1073/pnas.2004999117

Cited by 955 publications

(1,058 citation statements)

References 8 publications

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“…Arrows indicate mutation directions determined according to an outgroup genome (MN996532, strain “RaTG-13”, bat icon ). The network is consistent with a published one consisting of half the number of genomes (15). However, the maximum parsimony network registers 59 (or 40.7%) sites that have changed more than once (i.e., homoplasy).…”

Section: Usages and Innovationssupporting

confidence: 86%

“…The directed haplotype network in CoV Genome Tracker is thus informative for tracing the origin, following the spread, and forecasting the trend of Covid-19 outbreaks across the globe (Fig 1). To date, one published study and two preprint manuscripts use haplotype networks to represent the genealogy of SARS-CoV-2 isolates (13–15). These networks are however based on a much smaller number of genomes, non-interactive, and non-directional.…”

Section: Usages and Innovationsmentioning

confidence: 99%

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

Akther

Bezrucenkovas

Sulkow

et al. 2020

Preprint

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12 Summary: Genome sequences constitute the primary evidence on the origin and spread 13 of the 2019-2020 Covid-19 pandemic. Rapid comparative analysis of coronavirus SARS-CoV-2 14 genomes is critical for disease control, outbreak forecasting, and developing clinical interven-15 tions. CoV Genome Tracker is a web portal dedicated to trace Covid-19 outbreaks in real time 16 using a haplotype network, an accurate and scalable representation of genomic changes in a 17 rapidly evolving population. We resolve the direction of mutations by using a bat-associated 18 genome as outgroup. At a broader evolutionary time scale, a companion browser provides gene-19 by-gene and codon-by-codon evolutionary rates to facilitate the search for molecular targets of 20 clinical interventions.

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Section: Usages and Innovationssupporting

confidence: 86%

Section: Usages and Innovationsmentioning

confidence: 99%

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

Akther

Bezrucenkovas

Sulkow

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Recent evolutionary tracing studies suggest the emergence of multiple novel, evolved subtypes of SARS-CoV-2( 10 ), including the S/L-subtypes( 11 ) and the A/B/C-variants( 12 ). New variants will likely continue to emerge as the virus mutates, and to uncover them requires deep, complete coverage of viral genomes from a large number of patients.…”

Section: Discussionmentioning

confidence: 99%

MINERVA: A facile strategy for SARS-CoV-2 whole genome deep sequencing of clinical samples

Chen¹,

Lin

et al. 2020

Preprint

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32The novel coronavirus disease 2019 pandemic poses a serious public health 33 risk. Analyzing the genome of severe acute respiratory syndrome coronavirus 2 (SARS-34 CoV-2) from clinical samples is crucial for the understanding of viral spread and viral 35 evolution, as well as for vaccine development. Existing sample preparation methods for 36 viral genome sequencing are demanding on user technique and time, and thus not ideal 37 for time-sensitive clinical samples; these methods are also not optimized for high 38 performance on viral genomes. We have developed MetagenomIc RNA EnRichment 39 VirAl sequencing (MINERVA), a facile, practical, and robust approach for metagenomic 40 and deep viral sequencing from clinical samples. This approach uses direct tagmentation 41 of RNA/DNA hybrids using Tn5 transposase to greatly simplify the sequencing library 42 construction process, while subsequent targeted enrichment can generate viral genomes 43 with high sensitivity, coverage, and depth. We demonstrate the utility of MINERVA on 44 pharyngeal, sputum and stool samples collected from COVID-19 patients, successfully 45 obtaining both whole metatranscriptomes and complete high-depth high-coverage SARS-46 CoV-2 genomes from these clinical samples, with high yield and robustness. MINERVA 47 is compatible with clinical nucleic extracts containing carrier RNA. With a shortened 48 hands-on time from sample to virus-enriched sequencing-ready library, this rapid, 49 versatile, and clinic-friendly approach will facilitate monitoring of viral genetic variations 50 during outbreaks, both current and future. 51 52 53 Introduction 54As of April 24, 2020, the ongoing COVID-19 viral pandemic has affected more than 2.6 55 million people in over 200 countries and territories around the world, and has claimed 56 more than 180 thousand lives. Closely monitoring the genetic diversity and distribution of 57 viral strains at the population level is essential for epidemiological tracking, and for 58 understanding viral evolution and transmission; additionally examining the viral 59 heterogeneity within a single individual is imperative for diagnosis and treatment. The 60 disease-causing pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-61 CoV-2), was identified from early disease cases and its draft genome sequenced within 62 weeks, thanks to the rapid responses from researchers around the world(1, 2). The initial 63 SARS-CoV-2 draft genome was obtained independently from the same early COVID-19 64 patient samples using various conventional RNA-seq sequencing library construction 65 methods. Although these library construction methods successfully generated a draft 66 genome, several drawbacks hinder the use of these methods for routine viral genome 67 sequencing from the surge of clinical samples during an outbreak. 68 69 One direct library construction approach which was used to generate the SARS-CoV-2 70 draft genome(1, 2) essentially captures each sample's entire metatranscriptome, in which 71 SARS-CoV-2 is just one species ...

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“…Infection 1,2 , transcription and replication 3,4 by SARS-COV-2 involves a number of rate limiting interactions with host cells that are likely to be modulated by mutations in cellular as well as viral genes. At the same time, phylogenetic analysis shows geographic specificity 5,6 , indicating that geographic regions may show specific exposure to distinctive SNP combinations, or viral haplotypes, in SARS-COV-2. This specificity suggests a benefit to exploring relationships between duration of the prodromic phase, proportions of asymptomatic cases 7 , proportions of severe cases, rates of recovery, among other infection attributes 8 , that define temporal progression of compartmental epidemic models, starting with SIR (Susceptible-Infected-Recovered) models 9 .…”

Section: Introductionmentioning

confidence: 99%

Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails

Platt

Parida

Zalloua

2020

Preprint

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An opportunity exists in exploring epidemic modeling as a novel way to determine physiological and demic parameters for genetic association studies on a population/environmental (quasi) epidemiological study level. First, the spread of SARS-COV-2 has produced population specific lineages; second, epidemic spread model parameters are tied directly to these physiological and demic rates (e. g. incubation time, recovery time, transmission rate); and third, these parameters may serve as novel phenotypes to associate with region-specific genetic mutations as well as demic characteristics (e. g. age structure, cultural observance of personal space, crowdedness). Therefore, we sought to understand whether the parameters of epidemic models could be determined from the trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the quality and comparability of data between jurisdictions reporting their statistics necessary for the analysis of model parameters across populations. We found that, analytically, the pre-peak growth of an epidemic is limited by a subset of the model variates, and that the rate limiting variables are dominated by the expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth rate, which determines the doubling time. There are 9 parameters and 4 independent components in the eigenmode, leaving 5 undetermined parameters. Those parameters can be strikingly population dependent, and can have significant impact on estimates of hospital loads downstream. Without a sound framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates to uneven counting and reporting of relevant values. From the standpoint of phenotype parameters, this means that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models that accurately predict critical quantities such as hospitalization loads.

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Phylogenetic network analysis of SARS-CoV-2 genomes

Cited by 955 publications

References 8 publications

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

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

MINERVA: A facile strategy for SARS-CoV-2 whole genome deep sequencing of clinical samples

Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails

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