ArticleBioinformatics (Oxford, England)2023
P-DOR, an easy-to-use pipeline to reconstruct bacterial outbreaks using genomics.
Article in Bioinformatics (Oxford, England), 2023. 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.
- Genomic characterization of the emerging multidrug-resistantMicrobiology spectrum · 2026Article
- Genomic Characterization of Candida spp. Highlights a Persistent, Azole-Resistant C. parapsilosis Clone Circulating in a Tertiary Care Hospital During the First COVID-19 Wave.Mycopathologia · 2026Article
- HeterogeneousJournal of medical microbiology · 2025Article
- Bloodstream Infection Caused byMicroorganisms · 2024Article
- Clinical isolates of ST131Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024Article
- Combining Genome Surveillance and Metadata To Characterize the Diversity of Staphylococcus aureus Circulating in an Italian Hospital over a 9-Year Period.Microbiology spectrum · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
summaryBacterial Healthcare-Associated Infections (HAIs) are a major threat worldwide, which can be counteracted by establishing effective infection control measures, guided by constant surveillance and timely epidemiological investigations. Genomics is crucial in modern epidemiology but lacks standard methods and user-friendly software, accessible to users without a strong bioinformatics proficiency. To overcome these issues we developed P-DOR, a novel tool for rapid bacterial outbreak characterization. P-DOR accepts genome assemblies as input, it automatically selects a background of publicly available genomes using k-mer distances and adds it to the analysis dataset before inferring a Single-Nucleotide Polymorphism (SNP)-based phylogeny. Epidemiological clusters are identified considering the phylogenetic tree topology and SNP distances. By analyzing the SNP-distance distribution, the user can gauge the correct threshold. Patient metadata can be inputted as well, to provide a spatio-temporal representation of the outbreak. The entire pipeline is fast and scalable and can be also run on low-end computers. AVAILABILITY AND IMPLEMENTATION: P-DOR is implemented in Python3 and R and can be installed using conda environments. It is available from GitHub https://github.com/SteMIDIfactory/P-DOR under the GPL-3.0 license.
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Registered trials
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