ArticleDatabase : the journal of biological databases and curation2015
BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.
Article in Database : the journal of biological databases and curation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Type III secretion system diversity in the Pseudomonas fluorescens species complex.The New phytologist · 2026Article
- PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.Briefings in bioinformatics · 2026Article
- Machine learning for the prediction of gram-negative bacterial secreted effectors: advances and challenges.Frontiers in chemistry · 2026Review
- Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.Nature communications · 2025Article
- T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors.Computational and structural biotechnology journal · 2024Article
- POOE: predicting oomycete effectors based on a pre-trained large protein language model.mSystems · 2024Article
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria.Research (Washington, D.C.) · 2023Article
- From prediction to function: Current practices and challenges towards the functional characterization of type III effectors.Frontiers in microbiology · 2023Review
- Genomic and Functional Dissections of Dickeya zeae Shed Light on the Role of Type III Secretion System and Cell Wall-Degrading Enzymes to Host Range and Virulence.Microbiology spectrum · 2022Article
- ProtPlat: an efficient pre-training platform for protein classification based on FastText.BMC bioinformatics · 2022Article
- Natural language processing approach to model the secretion signal of type III effectors.Frontiers in plant science · 2022Article
- DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.NAR genomics and bioinformatics · 2021Article
- BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria.Nucleic acids research · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2021Article
- Article
- ACNNT3: Attention-CNN Framework for Prediction of Sequence-Based Bacterial Type III Secreted Effectors.Computational and mathematical methods in medicine · 2020Article
- Bastion3: a two-layer ensemble predictor of type III secreted effectors.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
Gram-negative pathogenic bacteria inject type III secreted effectors (T3SEs) into host cells to sabotage their immune signaling networks. Because T3SEs constitute a meeting-point of pathogen virulence and host defense, they are of keen interest to host-pathogen interaction research community. To accelerate the identification and functional understanding of T3SEs, we present BEAN 2.0 as an integrated web resource to predict, analyse and store T3SEs. BEAN 2.0 includes three major components. First, it provides an accurate T3SE predictor based on a hybrid approach. Using independent testing data, we show that BEAN 2.0 achieves a sensitivity of 86.05% and a specificity of 100%. Second, it integrates a set of online sequence analysis tools. Users can further perform functional analysis of putative T3SEs in a seamless way, such as subcellular location prediction, functional domain scan and disorder region annotation. Third, it compiles a database covering 1215 experimentally verified T3SEs and constructs two T3SE-related networks that can be used to explore the relationships among T3SEs. Taken together, by presenting a one-stop T3SE bioinformatics resource, we hope BEAN 2.0 can promote comprehensive understanding of the function and evolution of T3SEs.
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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.