Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering

Name: Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering
Published: 2024-02-20
License: https://creativecommons.org/licenses/by/4.0/

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Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering

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Description:	The dataset comprises a collection of Salmonella genomes associated with 58 historical outbreaks curated from the GenomeTrakr database and peer-reviewed PubMed articles. The outbreaks are linked to a diverse set of lineages that can be subdivided into 10 different serovars. The genomic data is accompanied by detailed contextual information describing the collection date, geographical origin and isolation source of the outbreak cases. The dataset was compiled to train classifiers to predict outbreak cluster labels from genomic data and infer a parsimonious set of outbreak predictive markers from the resulting classifiers.
Authors:	Liu, Chao Chun; Simon Fraser University; 0000-0002-8645-2598 Hsiao, William; Simon Fraser University; 0000-0002-1342-4043
Keywords:	Bacterial genomics Salmonella Whole genome sequencing Enteric outbreaks Infectious diseases
Field of Research:	Biological sciences > Microbiology > Microbial genetics
Publication Date:	2024-02-20
Publisher:	Federated Research Data Repository / dépôt fédéré de données de recherche
Funder:	Genome British Columbia; 286GET Mitacs; MITACS Accelerate
URI:	https://doi.org/10.20383/103.0884
Related Identifiers:	This dataset cites https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336196/ This dataset cites https://journals.asm.org/doi/10.1128/jcm.01280-15 This dataset cites https://doi.org/10.1371/currents.outbreaks.aa5372d90826e6cb0136ff66bb7a62fc This dataset cites https://doi.org/10.1128/JCM.02200-15 This dataset cites https://journals.asm.org/doi/10.1128/jcm.03235-14 This dataset cites https://journals.asm.org/doi/10.1128/jcm.02332-14 This dataset cites https://journals.asm.org/doi/10.1128/jcm.00081-16 This dataset is derived from https://www.ncbi.nlm.nih.gov/genbank/ This dataset is derived from https://www.fda.gov/food/whole-genome-sequencing-wgs-program/genometrakr-network This dataset is derived from https://www.ncbi.nlm.nih.gov/pmc/
Geographic Coverage:	Country Argentina Country Australia Country Bangladesh Country Belgium Country Canada Country Ecuador Country France Country Mauritius Country Mexico Country Turkey Country Vietnam State New Jersey Country United States State Minnesota Country United States State Ohio Country United States
Appears in Collections:	SFU Research Data

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Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Citation

Liu, C., Hsiao, W. (2024). Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering. Federated Research Data Repository. https://doi.org/10.20383/103.0884

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Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering

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