NDLI: PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

Content Provider	Springer Nature : BioMed Central
Author	de Nies, Laura Lopes, Sara Busi, Susheel Bhanu Galata, Valentina Heintz-Buschart, Anna Laczny, Cedric Christian May, Patrick Wilmes, Paul
Abstract	Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets. Results Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases. Conclusion PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu . Video abstract
Related Links	https://microbiomejournal.biomedcentral.com/counter/pdf/10.1186/s40168-020-00993-9.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	20492618
DOI	10.1186/s40168-020-00993-9
Journal	Microbiome
Issue Number	1
Volume Number	9
Language	English
Publisher	BioMed Central
Publisher Date	2021-02-17
Access Restriction	Open
Subject Keyword	Medical Microbiology Bioinformatics Microbial Ecology Microbiology Microbial Genetics and Genomics Virology Virulence factors Bacterial toxins Antimicrobial resistance Mobile genetic elements Metagenomics Microbiome
Content Type	Text
Resource Type	Article
Subject	Microbiology Microbiology (medical)
Journal Impact Factor	13.8/2023
5-Year Journal Impact Factor	17.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data