ArticleScientific reports2020
Mining whole genome sequence data to efficiently attribute individuals to source populations.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- WGS-based surveillance for Campylobacter spp. in human infections and chicken meat production in Italy (2023).BMC microbiology · 2025Article
- Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.The Journal of infection · 2024Article
- A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing.Foodborne pathogens and disease · 2024Review
- The Use of Interdisciplinary Approaches to Understand the Biology ofMicroorganisms · 2022Review
- Unravelling the reservoirs for colonisation of infants withBMJ open · 2022Article
- Core Genome MLST for Source Attribution ofFrontiers in microbiology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.
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