ArticleMicroorganisms2019
Optimizing a Whole-Genome Sequencing Data Processing Pipeline for Precision Surveillance of Health Care-Associated Infections.
Article in Microorganisms, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Leveraging real-time genomic surveillance to combat infectious diseases and antimicrobial resistance in cancer patients.Frontiers in public health · 2026Article
- Multi-omics decodes host-specific and environmental microbiome interactions in sepsis.Frontiers in microbiology · 2025Review
- Local Genomic Surveillance of InvasiveInternational journal of molecular sciences · 2024Article
- Intestinal Microbiota of Grass Carp Fed Faba Beans: A Comparative Study.Microorganisms · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The surveillance of health care-associated infection (HAI) is an essential element of the infection control program. While whole-genome sequencing (WGS) has widely been adopted for genomic surveillance, its data processing remains to be improved. Here, we propose a three-level data processing pipeline for the precision genomic surveillance of microorganisms without prior knowledge: species identification, multi-locus sequence typing (MLST), and sub-MLST clustering. The former two are closely connected to what have widely been used in current clinical microbiology laboratories, whereas the latter one provides significantly improved resolution and accuracy in genomic surveillance. Comparing to a broadly used reference-dependent alignment/mapping method and an annotation-dependent pan-/core-genome analysis, we implemented our reference- and annotation-independent, k-mer-based, simplified workflow to a collection of
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Registered trials
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