HaploCoV: unsupervised classification and rapid detection of novel emerging variants of SARS-CoV-2

Chiara, Matteo; Horner, David Stephen; Ferrandi, Erika; Gissi, Carmela; Pesole, Graziano

doi:10.1038/s42003-023-04784-4

Cited by 3 publications

(9 citation statements)

References 48 publications

(50 reference statements)

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“…Monkeypox genome sequences and associated metadata were accessed through the Nexstrain resource 57 , low-quality genome sequences were discarded according to the same criteria used for SARS-CoV-2, and mutations were identified by applying the HaploCoV workflow 27 on a collection of 2,526 high-quality sequences. A total of 4,932 distinct mutations were detected.…”

Section: Resultsmentioning

confidence: 99%

“…The original data included genome sequences in FASTA format and associated metadata (accession ID, collection date, submission date, Pangolin lineage, and collection location). These were processed by the HaploCoV pipeline 28 to derive a large table with the list of mutations and matched metadata for every genome sequence.…”

Section: Methodsmentioning

confidence: 99%

“…As the COVID-19 pandemic progressed, research interests shifted toward the study of mutational signatures and variants associated with increased transmission rates and reduced antigenicity, and possibly hampering testing, treatment, and vaccine development [21][22][23] . A number of methods were proposed to allow automatic early detection of variants [24][25][26][27][28] . Instead, interest in the automatic identification of recombination in SARS-CoV-2 started at a later stage.…”

Section: Mainmentioning

confidence: 99%

“…We considered 15,271,031 SARS-CoV-2 genomes, downloaded from the GISAID database 20 on April 1st, 2023. Genome sequences were aligned to the SARS-CoV-2 reference genome and nucleotide mutations were identified by the HaploCoV pipeline 28 . To mitigate the impacts of sequencing and assembly errors, genome sequences of uncertain/low quality were excluded (i.e., records associated with low coverage, percentage of unknown bases ≥ 2%, unknown length, and incomplete metadata).…”

Section: Mainmentioning

confidence: 99%

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Data-driven recombination detection in viral genomes

Alfonsi

Bernasconi

Chiara

et al. 2023

Preprint

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Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than seventy SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully reproduce manual analyses by experts in the field. We hereby present RecombinHunt, a novel, automated method for the identification of recombinant genomes purely based on a data-driven approach. RecombinHunt compares favorably with other state-of-the-art methods and recognizes recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy, within reduced turn-around times and small discrepancies with respect to the expert manually-curated standard nomenclature. Strikingly, applied to the complete collection of viral sequences from the recent monkeypox epidemic, RecombinHunt identifies recombinant viral genomes in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus. Although RecombinHunt does not substitute manual expert curation based on phylogenetic analysis, we believe that our method represents a breakthrough for the detection of recombinant viral lineages in pandemic/epidemic scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

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Data-driven recombination detection in viral genomes

Alfonsi

Bernasconi

Chiara

et al. 2023

Preprint

Self Cite

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show abstract

“…As the COVID-19 pandemic progressed, research interests shifted toward the study of mutational signatures and variants associated with increased transmission rates and reduced antigenicity, and possibly hampering testing, treatment, and vaccine development 20 – 22 . A number of methods were proposed to allow automatic early detection of variants 23 – 27 . Instead, interest in the automatic identification of recombination in SARS-CoV-2 started at a later stage.…”

Section: Introductionmentioning

confidence: 99%

Data-driven recombination detection in viral genomes

Alfonsi,

Bernasconi,

Chiara

et al. 2024

Nat Commun

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Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than ninety SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully confirm manual analyses by experts in the field. We hereby present RecombinHunt, an original data-driven method for the identification of recombinant genomes, capable of recognizing recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy and within reduced turn-around times. ReconbinHunt shows high specificity and sensitivity, compares favorably with other state-of-the-art methods, and faithfully confirms manual analyses by experts. RecombinHunt identifies recombinant viral genomes from the recent monkeypox epidemic in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus.

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The Opportunity of Data-Driven Services for Viral Genomic Surveillance

Bernasconi

2023

2023 IEEE International Conference on Service-Oriented System Engineering (SOSE)

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The recent COVID-19 pandemic has posed novel challenges to the big data and knowledge management community. The unprecedented availability of viral genomes on public databases has made possible the data-driven exploration of viruses' evolution (especially of SARS-CoV-2, the virus responsible for the disease). Properties of data and knowledge in the genomic and virological domain may fuel data science methods for the identification and possible prediction of critical phenomena, such as the emergence of variants with improved transmissibility/virulence and recombined strains. A number of tools have been produced to explore the variants' trends or suggest hypotheses on the evolutionary mechanisms of the virus. In this perspective, we elaborate on plausible directions of this field of research, which are still applicable to the SARS-CoV-2 virus but may become even more relevant in the context of new outbreaks (e.g., monkeypox, malaria, diphtheria). Expressly, we point to 1) data-driven identification of mutations or variants with potential impact; 2) data-driven identification of recombination events -creating opportunities to overcome selective pressure and adapt to new environments and hosts (e.g., livestock or humans). These directions can be framed within genomic surveillance measures, characterized by the possibility of tracking viruses by using their genome, which is collected, sequenced, and submitted to public databases by laboratories around the world. If successful, genomic surveillance substantially supports the understanding of novel viral pathogens and of their dangerousness in terms of prevalence, infectivity, and transmissibility; the implemented services can be of great utility to decision-makers in healthcare. Here, we draw current trends, challenges, and future directions of data-driven services for genomic surveillance.

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HaploCoV: unsupervised classification and rapid detection of novel emerging variants of SARS-CoV-2

Cited by 3 publications

References 48 publications

Data-driven recombination detection in viral genomes

Data-driven recombination detection in viral genomes

Data-driven recombination detection in viral genomes

The Opportunity of Data-Driven Services for Viral Genomic Surveillance

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