Databases

PlaD

PlaD is a transcriptomics database for plant defense responses to pathogens. PlaD contains 2444 public gene expression samples from 94 plant pathology-related studies, covering four important plants (Arabidopsis thaliana, Oryza sativa, Triticum aestivum and Zea mays). Each sample was manually curated based on plant tissues, pathogen types and infection stages. All the samples were consistently normalized and the fold change of differential expression was calculated for 522 conditions.

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AraPPISite

AraPPISite is an integrated database presenting fine-grained protein-protein interaction site annotations for Arabidopsis thaliana.

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PPIRA

Ralstonia solanacearum is a plant pathogen which can infect an unusually wide range of hosts. This bacterium and its host Arabidopsis thaliana have become a model system for studying the molecular basis of plant-pathogen interactions. For any phytopathogenic, protein-protein interactions (PPIs) play very important roles in infecting hosts. PPIs between R. solancearum and A. thaliana were constructed by two bioinformatic methods, the interolog and domain-based methods. The predicted PPIs were compiled as a PPI network called PPIRA, which contains 3074 PPIs between R. solancearum and A. thaliana.

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NCPI: Neurospora crassa Protein Interactome Database

NCPI represents a platform mainly to provide predicted protein-protein interactions(PPIs) in model fungus Neurospora crassa. This database, which has an intuitive query interface allowing an easy access to all the features of proteins, was built up using open source technologies (LAMP) and will be freely available at http://protein.cau.edu.cn/ncpi. The predicted protein-protein interactions are obtained using two methods.

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BEAN 2.0

Bacterial Effector Analyzer 2.0(BEAN 2.0) is an integrative web resource for prediction, analysis and storing type-III effectors.

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MPID: Magnaporthe grisea Protein-protein Interaction Database

Protein-protein interaction interactions tend to be evolutionary conserved cross species. Therefore we can predict protein-protein interactions in Magnaporthe grisea using model organism protein interaction datasets. Based on this method, we have built a network of 11674 predicted physical interactions among 3017 M.grisea proteins.

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