ArticleScientific reports2018
Genome-wide prediction of bacterial effector candidates across six secretion system types using a feature-based statistical framework.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Review
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- Article
- TXSelect: A multi-task learning model to identify secretory effectors.PLoS computational biology · 2025Article
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Bacterial secretion system functions: evidence of interactions and downstream implications.Microbiology (Reading, England) · 2023Article
- Antibody reactive immunomes ofFrontiers in cellular and infection microbiology · 2023Article
- DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria.Research (Washington, D.C.) · 2023Article
- Microbial Effectors: Key Determinants in Plant Health and Disease.Microorganisms · 2022Review
- Article
- DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.NAR genomics and bioinformatics · 2021Article
- Immunoreactive Protein Repertoires of Ehrlichia chaffeensis and E. canis Reveal the Dominance of Hypothetical Proteins and Conformation-Dependent Antibody Epitopes.Infection and immunity · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- Article
- Novel global effector mining from the transcriptome of early life stages of the soybean cyst nematode Heterodera glycines.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Gram-negative bacteria are responsible for hundreds of millions infections worldwide, including the emerging hospital-acquired infections and neglected tropical diseases in the third-world countries. Finding a fast and cheap way to understand the molecular mechanisms behind the bacterial infections is critical for efficient diagnostics and treatment. An important step towards understanding these mechanisms is the discovery of bacterial effectors, the proteins secreted into the host through one of the six common secretion system types. Unfortunately, current prediction methods are designed to specifically target one of three secretion systems, and no accurate "secretion system-agnostic" method is available. Here, we present PREFFECTOR, a computational feature-based approach to discover effector candidates in Gram-negative bacteria, without prior knowledge on bacterial secretion system(s) or cryptic secretion signals. Our approach was first evaluated using several assessment protocols on a manually curated, balanced dataset of experimentally determined effectors across all six secretion systems, as well as non-effector proteins. The evaluation revealed high accuracy of the top performing classifiers in PREFFECTOR, with the small false positive discovery rate across all six secretion systems. Our method was also applied to six bacteria that had limited knowledge on virulence factors or secreted effectors. PREFFECTOR web-server is freely available at: http://korkinlab.org/preffector .
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Registered trials
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