Learn to use PID (Pathway Interaction Database). PID is a public resource for the cancer research community and other scientists interested in signaling pathways in human cells. It provides a high quality, structured and curated collection of information about many signaling pathways as well as a user-friendly set of tools to allow visualization, exploration, and mining of the data. As of now, hundreds of pathways and thousands of interactions can be accessed using this database.
You will learn:
This tutorial is a part of the tutorial group Interaction resources. You might find the other tutorials in the group interesting:
MINT: Molecular Interaction Database
Cytoscape: An open-source software platform used for visualization and analysis of molecular interaction and network data
BiologicalNetworks: Analyze and visualize molecular interaction networks
BioSystems: Database of Biological Systems
Reactome Legacy: Older version of the current Reactome knowledgebase of biological processes.
Reactome: Knowledgebase of biological processes
GeneMANIA: GeneMANIA: Fast Gene Function Predictions
GenMAPP: A freely available open source software application for visualizing microarray data in the context of biological pathways.
VisANT: A web-based or downloadable software platform used for visualization and analysis of networks and interaction pathways
InterPro: A comprehensive protein signature resource
IntAct protein interaction database: IntAct is an open source database and analysis resource for protein interaction data
KEGG: KEGG, The Kyoto Encyclopedia of Genes and Genomes
Proteins : Tools that are primarily used in the storage, retrieval, or exploration of amino acid based data. Some tools may also involve nucleotide sequence information.
Pathways and Interactions : Tools that are involved with protein interactions and pathway features. Some tools are primarily repositories and some offer analysis options.
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Recent BioMed Central research articles citing this resource
Kim SungHwan et al., Meta-analytic support vector machine for integrating multiple omics data. BioData Mining (2017) doi:10.1186/s13040-017-0126-8
Oh Kimin et al., Prediction of scaffold proteins based on protein interaction and domain architectures. BMC Bioinformatics (2016) doi:10.1186/s12859-016-1079-5
Bornstein Sophia et al., IL-10 and integrin signaling pathways are associated with head and neck cancer progression Human and rodent genomics. BMC Genomics (2016) doi:10.1186/s12864-015-2359-6
Wang Sharon et al., Targeted Pten deletion plus p53-R270H mutation in mouse mammary epithelium induces aggressive claudin-low and basal-like breast cancer. Breast Cancer Research (2016) doi:10.1186/s13058-015-0668-y
Shifman R. Aaron et al., Cascade: an RNA-seq visualization tool for cancer genomics Transcriptomic methods. BMC Genomics (2016) doi:10.1186/s12864-016-2389-8