ArticleNucleic acids research2013
Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.Briefings in bioinformatics · 2019Pooled it
- Article
- T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.Computational and structural biotechnology journal · 2024Article
- KaruBioNet: a network and discussion group for a better collaboration and structuring of bioinformatics in Guadeloupe (French West Indies).Bioinformatics advances · 2022Article
- The super repertoire of type IV effectors in the pangenome of Ehrlichia spp. provides insights into host-specificity and pathogenesis.PLoS computational biology · 2021Article
- Article
- Biostatistical prediction of genes essential for growth of Anaplasma phagocytophilum in a human promyelocytic cell line using a random transposon mutant library.Pathogens and disease · 2021Article
- iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles.Molecules (Basel, Switzerland) · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- Subcellular Location ofMicroorganisms · 2020Article
- Article
- T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.Frontiers in microbiology · 2020Article
- Stenotrophomonas maltophilia Encodes a VirB/VirD4 Type IV Secretion System That Modulates Apoptosis in Human Cells and Promotes Competition against Heterologous Bacteria, Including Pseudomonas aeruginosa.Infection and immunity · 2019Article
- Searching algorithm for Type IV effector proteins (S4TE) 2.0: Improved tools for Type IV effector prediction, analysis and comparison in proteobacteria.PLoS computational biology · 2019Article
- Mutational analysis of gene function in the Anaplasmataceae: Challenges and perspectives.Ticks and tick-borne diseases · 2019Article
- Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila.PloS one · 2019Article
- Prediction of T4SS Effector Proteins forFrontiers in microbiology · 2019Article
- Quantitative Yeast Genetic Interaction Profiling of Bacterial Effector Proteins Uncovers a Role for the Human Retromer in Salmonella Infection.Cell systems · 2018Article
- Comparative Transcriptome Profiling of Virulent and AttenuatedFrontiers in cellular and infection microbiology · 2018Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Type IV effectors (T4Es) are proteins produced by pathogenic bacteria to manipulate host cell gene expression and processes, divert the cell machinery for their own profit and circumvent the immune responses. T4Es have been characterized for some bacteria but many remain to be discovered. To help biologists identify putative T4Es from the complete genome of α- and γ-proteobacteria, we developed a Perl-based command line bioinformatics tool called S4TE (searching algorithm for type-IV secretion system effectors). The tool predicts and ranks T4E candidates by using a combination of 13 sequence characteristics, including homology to known effectors, homology to eukaryotic domains, presence of subcellular localization signals or secretion signals, etc. S4TE software is modular, and specific motif searches are run independently before ultimate combination of the outputs to generate a score and sort the strongest T4Es candidates. The user keeps the possibility to adjust various searching parameters such as the weight of each module, the selection threshold or the input databases. The algorithm also provides a GC% and local gene density analysis, which strengthen the selection of T4E candidates. S4TE is a unique predicting tool for T4Es, finding its utility upstream from experimental biology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.