ArticleBMC research notes2024
Design and implementation of a metagenomic analytical pipeline for respiratory pathogen detection.
Article in BMC research notes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Lung microbiome signatures and explainable predictive modeling of glucocorticoid response in severe community acquired pneumonia.Frontiers in microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveWe developed an in-house bioinformatics pipeline to improve the detection of respiratory pathogens in metagenomic sequencing data. This pipeline addresses the need for short-time analysis, high accuracy, scalability, and reproducibility in a high-performance computing environment.
resultsWe evaluated our pipeline using ninety synthetic metagenomes designed to simulate nasopharyngeal swab samples. The pipeline successfully identified 177 out of 204 respiratory pathogens present in the compositions, with an average processing time of approximately 4 min per sample (processing 1 million paired-end reads of 150 base pairs). For the estimation of all the 470 taxa included in the compositions, the pipeline demonstrated high accuracy, identifying 420 and achieving a correlation of 0.9 between their actual and predicted relative abundances. Among the identified taxa, 27 were significantly underestimated or overestimated, including only three clinically relevant pathogens. We also validated the pipeline by applying it to a clinical dataset from a study on metagenomic pathogen characterization in patients with acute respiratory infections and successfully identified all pathogens responsible for the diagnosed infections. These findings underscore the pipeline's effectiveness in pathogen detection and highlight its potential utility in respiratory pathogen surveillance.
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Registered trials
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