Evolution of the SARS-CoV-2 Mutational Spectrum

Bloom, Jesse D.; Beichman, Annabel C.; Neher, Richard A.; Harris, Kelley

doi:10.1093/molbev/msad085

Cited by 40 publications

(30 citation statements)

References 67 publications

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“…The PHATE visualization of the 296,437 retained de novo iSNVs by genomic position display no clustering according to the mutational pattern, underscoring the optimal curation of the dataset (Figure 6A). Additionally, the substitution spectrum of this curated set shows a prevalence of C > T and G > T substitutions (Figure 6B), aligning with consensus iSNV patterns and known SARS-CoV-2 mutational trends (Moshiri et al 2023; Fumagalli et al 2023; Bloom et al 2023; Saldivar-Espinoza et al 2023).…”

Section: Resultssupporting

confidence: 71%

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

Mostefai,

Grenier,

Poujol

et al. 2024

Preprint

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Understanding the evolution of viral genomes is essential for elucidating how viruses adapt and change over time. Analyzing intra-host single nucleotide variants (iSNVs) provides key insights into the mechanisms driving the emergence of new viral lineages, which is crucial for predicting and mitigating future viral threats. Despite the potential of next-generation sequencing (NGS) to capture these iSNVs, the process is fraught with challenges, particularly the risk of capturing sequencing artifacts that may result in false iSNVs. To tackle this issue, we developed a two-step workflow designed to enhance the reliability of iSNV detection in large heterogeneous collections of NGS libraries. We use over 130,000 publicly available SARS-CoV-2 NGS libraries to show how our comprehensive workflow effectively distinguishes emerging viral mutations from sequencing errors. This approach incorporates rigorous bioinformatics protocols, stringent quality control metrics, and innovative usage of dimensionality reduction methods to generate insightful representations of this high-dimensional dataset. We identified and mitigated notable batch effects linked to specific sequencing centers around the world and introduced quality control metrics such as the Strand Bias Likelihood that considers strand coverage imbalance, enhancing iSNV reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how closely related groups of data points are within a two-dimensional space, enhancing our ability to explain clustering patterns based on their shared genetic characteristics. Our workflow not only sheds light on the complexities of viral genomic analysis with state-of-the-art sequencing technologies but also advances the detection of accurate intra-host mutations, opening the door for an enhanced understanding of viral adaptation mechanisms.

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Section: Resultssupporting

confidence: 71%

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

Mostefai,

Grenier,

Poujol

et al. 2024

Preprint

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“…Nevertheless, these mutations may be the prelude for further mutations in the course of the evolution of the new sub-lineage. Therefore, monitoring the periodical changes in the virus dynamics is important [37]. Through the combination of high-throughput sequencing technologies with phylogenetic analysis, it is possible to assess parallel patterns of evolution driving significant phenotypic shifts.…”

Section: Discussionmentioning

confidence: 99%

Emergence of SARS-CoV-2 Omicron Variant JN.1 in Tamil Nadu, India - Clinical Characteristics and Novel Mutations

Selvavinayagam,

Sankar,

Yong

et al. 2024

Preprint

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In December 2023, we observed a notable shift in the COVID-19 landscape, when the JN.1 emerged as a predominant SARS-CoV-2 variant with a 95% incidence. We characterized the clinical profile, and genetic changes in JN.1, an emerging SARS-CoV-2 variant of interest. Whole genome sequencing was performed on SARS-CoV-2 positive samples, followed by sequence analysis. Mutations within the spike protein sequences were analyzed and compared with the previous lineages and sublineages of SARS-CoV-2, to identify the potential impact of these unique mutations on protein structure and possible functionality. Several unique and dynamic mutations were identified herein. Our data provides key insights into the emergence of newer variants of SARS-CoV-2 in our region and highlights the need for robust and sustained genomic surveillance of SARS-CoV-2.

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“…While C29095T is a mutation towards the most closely related viruses sampled in bats, the A + C29095T haplotype is rejected as the root of the SARS-CoV-2 phylogenetic tree in humans by Pekar et al (2022). Methods in Bloom (2021b) neglect that C→T mutations are by far the most frequent type of mutation during the pandemic (Azgari et al, 2021;De Maio et al, 2021;Bloom et al, 2023;Ruis et al, 2023), and that the C29095T mutation, specifically, occurs much more frequently than expected for a typical C→T mutation (Bloom and Neher, 2023, supplementary data nt_fitness.csv). This mutation regularly reappeared in multiple lineages during the last four years of SARS-CoV-2 evolution in humans (Figure S3).…”

Section: No Evidence Of a Widespread Undetected Circulation Of Virus ...mentioning

confidence: 99%

A critical reexamination of recovered SARS-CoV-2 sequencing data

Débarre,

Hensel

2024

Preprint

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SARS-CoV-2 genomes collected at the onset of the Covid-19 pandemic are valuable because they could help understand how the virus entered the human population. In 2021, Jesse Bloom reported on the recovery of a sequencing dataset that had been removed from the NCBI SRA database at the request of the data generators, a scientific team at Wuhan University. Bloom suggested that the data may have been removed in order to obfuscate the origin of SARS-CoV-2, questioning the generating authors' statements that the samples had been collected on and after January 30, 2020. Here, we show that sample collection dates were published in 2020 together with the sequencing data, and match the dates given by the authors in 2021. We examine mutations in these sequences and confirm that they are entirely consistent with the previously known genetic diversity of SARS-CoV-2 of late January 2020. Finally, we explain how an apparent phylogenetic rooting paradox described by Bloom was resolved by subsequent analysis. Our reanalysis demonstrates that allegations of cover-up or metadata manipulation were unwarranted.

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Evolution of the SARS-CoV-2 Mutational Spectrum

Cited by 40 publications

References 67 publications

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

Emergence of SARS-CoV-2 Omicron Variant JN.1 in Tamil Nadu, India - Clinical Characteristics and Novel Mutations

A critical reexamination of recovered SARS-CoV-2 sequencing data

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