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

Abstract Motivation: Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health. 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 coun-termeasures. From a decision-making policy perspective, information about the quality on the whole procedure are fundamental before using the outcome of an analysis as evidence to reach a conclusion. However, the ma-jority of available tools are limited to the assessment of raw NGS data quality, not sufficiently stressing the evalu-ation of results robustness and trustability. Results: We present PathoSeq-QC, a bioinformatics decision support workflow developed for SARS-CoV-2 but easily adaptable to any viral threat, designed to improve the trustworthiness of genomic surveillance analyses and conclusions. 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; iv) reports whether the produced data are in support of a recombinant virus, a novel or an already known lineage. The tool is highly modular, which will allow easy functionalities extension. Availability: PathoSeq-QC is a command-line tool written in Python and R. The code is available at https://code.europa.eu/dighealth/pathoseq-qc.

LEONI Gabriele;  PETRILLO Mauro;  RUIZ SERRA Victoria;  QUERCI Maddalena;  COECKE Sandra;  WIESENTHAL Tobias; 

2025-04-04

OXFORD UNIV PRESS

JRC138271

1367-4803 (online),   

https://doi.org/10.1093/bioinformatics/btaf102,    https://academic.oup.com/bioinformatics/article-pdf/41/4/btaf102/62340404/btaf102.pdf,    https://publications.jrc.ec.europa.eu/repository/handle/JRC138271,   

10.1093/bioinformatics/btaf102 (online),