Design of genomic signatures for pathogen identification and characterization

Slezak, Tom; Hart, Bradley R.; Jaing, Crystal

doi:10.1016/b978-0-12-815379-6.00020-9

Cited by 4 publications

(3 citation statements)

References 38 publications

(30 reference statements)

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“…These alignments can then be analyzed to identify the divergent genomic regions that constitute the discriminating motifs. However, the use of multiple alignment approaches has significant limitations, particularly when applied to viral genomes [12]. First, alignment-based approaches are generally computationally-and time-intensive and are therefore less well suited to dealing with very large viral sequence datasets that February 5, 2022 2/17 are increasingly available [17].…”

Section: Fig 1 Sars-cov-2 Genome Organizationmentioning

confidence: 99%

“…These signatures are defined as species or variant-specific motifs that are pervasive throughout the viral genome [9]. In the context of SARS-CoV-2, the identification of this type of signature can contribute to taxonomic [10] and phylogenetic [11] studies to differentiate distinct groups of variants, provide an explanation for their evolutionary history [9], as well as to facilitate mechanistic studies to elucidate the functional basis of variant-specific differences in virulence [12].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures

Lebatteux

Diallo

Gantt

et al. 2022

Preprint

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Machine learning has proven to be a powerful tool for the identification of distinctive genomic signatures among viral sequences. Such signatures are motifs present in the viral genome that differentiate species or variants. In the context of SARS-CoV-2, the identification of such signatures can contribute to taxonomic and phylogenetic studies, help in recognizing and defining distinct emerging variants, and focus the characterization of functional properties of polymorphic gene products. Here, we study KEVOLVE, an approach based on a genetic algorithm with a machine learning kernel, to identify several genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE performed better in identifying variant-discriminative signatures than several gold-standard reference statistical tools. Subsequently, these signatures were characterized to highlight potential biological functions. The majority were associated with known mutations among the different variants, with respect to functional and pathological impact based on available literature. Notably, we found show evidence of new motifs, specifically in the Omicron variant, some of which include silent mutations, indicating potentially novel, variant-specific virulence determinants. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.

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Section: Fig 1 Sars-cov-2 Genome Organizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures

Lebatteux

Diallo

Gantt

et al. 2022

Preprint

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Indexing and searching petabyte-scale nucleotide resources

Shiryev¹,

Agarwala²

2023

Preprint

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Searching vast and rapidly growing sets of nucleotide content in data resources, such as runs in Sequence Read Archive and assemblies for whole genome shotgun sequencing projects in GenBank, is currently impractical in any reasonable amount of time or resources available to most researchers. We present Pebblescout, a tool that navigates such content by providing indexing and search capabilities. Indexing uses dense sampling of the sequences in the resource. Search finds subjects that have short sequence matches to a user query with well-defined guarantees. Reported subjects are ranked using a score that considers the informativeness of the matches. Six databases that index over 3.5 petabases were created and used to illustrate the functionality of Pebblescout. Here we show that Pebblescout provides new research opportunities and a data-driven way for finding relevant subsets of large nucleotide resources for analysis, some of which are missed when relying only on sample metadata or tools using pre-defined reference sequences. For two computationally intensive published studies, we show that Pebblescout rejects a significant number of runs analyzed without changing the conclusions of these studies and finds additional relevant runs. A pilot web service for interactively searching the six databases is freely available at https://pebblescout.ncbi.nlm.nih.gov/

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