Predicting the pathogenicity of bacterial genomes using widely spread protein families

Shaked Naor-Hoffmann, Dina Svetlitsky, Neta Sal-Man, Yaron Orenstein, Michal Ziv-Ukelson

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background: The human body is inhabited by a diverse community of commensal non-pathogenic bacteria, many of which are essential for our health. By contrast, pathogenic bacteria have the ability to invade their hosts and cause a disease. Characterizing the differences between pathogenic and commensal non-pathogenic bacteria is important for the detection of emerging pathogens and for the development of new treatments. Previous methods for classification of bacteria as pathogenic or non-pathogenic used either raw genomic reads or protein families as features. Using protein families instead of reads provided a better interpretability of the resulting model. However, the accuracy of protein-families-based classifiers can still be improved. Results: We developed a wide scope pathogenicity classifier (WSPC), a new protein-content-based machine-learning classification model. We trained WSPC on a newly curated dataset of 641 bacterial genomes, where each genome belongs to a different species. A comparative analysis we conducted shows that WSPC outperforms existing models on two benchmark test sets. We observed that the most discriminative protein-family features in WSPC are widely spread among bacterial species. These features correspond to proteins that are involved in the ability of bacteria to survive and replicate during an infection, rather than proteins that are directly involved in damaging or invading the host.

Original languageEnglish
Article number253
JournalBMC Bioinformatics
Volume23
Issue number1
DOIs
StatePublished - 24 Jun 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Funding

The research of S.N.H, D.S, and M.Z.U was partially supported by the Israel Science Foundation (Grant No. 939/18). The research of N.S.M was partially supported by the Israel Science Foundation (Grant No. 988/19) and the Israel Ministry of Science and Technology (Grant No. 316841).

FundersFunder number
Israel Science Foundation939/18, 988/19
Ministry of science and technology, Israel316841

    Keywords

    • Commensal bacteria
    • Comparative genomics
    • Opportunistic bacteria
    • Pathogenic bacteria
    • Protein families
    • Random forest

    Fingerprint

    Dive into the research topics of 'Predicting the pathogenicity of bacterial genomes using widely spread protein families'. Together they form a unique fingerprint.

    Cite this