PathoSeq-QC: a decision support bioinformatics workflow for robust genomic surveillance

Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health threats. Coupled with bioinformatics and other data types (e.g., epidemiological information), genomics is used to obtain knowledge on health...

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Published in:Bioinformatics (Oxford, England) Vol. 41; no. 4
Main Authors: Leoni, Gabriele, Petrillo, Mauro, Ruiz-Serra, Victoria, Querci, Maddalena, Coecke, Sandra, Wiesenthal, Tobias
Format: Journal Article
Language:English
Published: England Oxford University Press 29.03.2025
Subjects:
Applications Note
Computational Biology - methods
COVID-19 - epidemiology
COVID-19 - virology
Decision Support Techniques
Genomics - methods
High-Throughput Nucleotide Sequencing - methods
Humans
SARS-CoV-2 - genetics
Software
Workflow
ISSN:1367-4811, 1367-4803, 1367-4811
Online Access:Get full text
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Summary:Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health threats. Coupled with bioinformatics and other data types (e.g., epidemiological information), genomics is used to obtain knowledge on health pathogenic threats and insights on their evolution, to monitor pathogens spread, and to evaluate the effectiveness of countermeasures. From a decision-making policy perspective, it is essential to ensure the entire process's quality before relying on analysis results as evidence. Available workflows usually offer quality assessment tools that are primarily focused on the quality of raw NGS reads but often struggle to keep pace with new technologies and threats, and fail to provide a robust consensus on results, necessitating manual evaluation of multiple tool outputs. We present PathoSeq-QC, a bioinformatics decision support workflow developed to improve the trustworthiness of genomic surveillance analyses and conclusions. Designed for SARS-CoV-2, it is suitable for any viral threat. In the specific case of SARS-CoV-2, PathoSeq-QC: (i) evaluates the quality of the raw data; (ii) assesses whether the analysed sample is composed by single or multiple lineages; (iii) produces robust variant calling results via multi-tool comparison; (iv) reports whether the produced data are in support of a recombinant virus, a novel or an already known lineage. The tool is modular, which will allow easy functionalities extension. PathoSeq-QC is a command-line tool written in Python and R. The code is available at https://code.europa.eu/dighealth/pathoseq-qc.
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ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btaf102