Design and implementation of a metagenomic analytical pipeline for respiratory pathogen detection

• Published in: BMC research notes Vol. 17; no. 1; pp. 291 - 6
• Main Authors: Viana, Pablo Alessandro B, Tschoeke, Diogo Antonio, de Moraes, Laise, Santos, Luciane Amorim, Barral-Netto, Manoel, Khouri, Ricardo, Ramos, Pablo Ivan P, Meirelles, Pedro Milet
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.10.2024; BMC
• Subjects: Bioinformatics pipeline; Computational Biology - methods; Diagnosis; Genetic aspects; Genetic screening; Humans; Medical research; Medicine, Experimental; Metagenome - genetics; Metagenomics; Metagenomics - methods; Nasopharynx - microbiology; Nasopharynx - virology; Pathogenic microorganisms; Physiological aspects; Reproducibility of Results; Respiratory pathogens; Respiratory tract diseases; Respiratory Tract Infections - diagnosis; Respiratory Tract Infections - microbiology; Brazil; Bioinformatics pipeline; Metagenomics; Respiratory pathogens
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/s13104-024-06964-9

We 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. We 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.