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.
Video Tip of the Week: TargetMine, Data Warehouse for Drug Discovery: Browsing around genomic regions, layering on lots of associated data, and beginning to explore new data types I might come across are things that really fire up my brain. For me, visualization is key t...
Bioinformatics tools extracted from a typical mammalian genome project [supplement]: This is Table 1 that accompanies the full blog post: Bioinformatics tools extracted from a typical mammalian genome project. See the main post for the details and explanation. The table is too long to ...
Many Protein Resources Have Recently Announced Updates: In our ongoing pursuit of up-to-date tutorials, I've been tracking changes that are occurring at resources and planning our updates accordingly. Protein resources are especially going to keep ...
Tip of the Week: PathCase for pathway data: We spend a lot of time exploring genomic data, variations, and annotations. But of course a linear perspective on the genes and sequences is not the only way to examine the data. Understanding the pat...
Tip of the Week: The Cancer Genome Workbench: In today's tip I'd like to introduce you to the Cancer Genome Workbench, or CGWB. The workbench gathers cancer information from a wide variety of projects including Johns Hopkins University and GlaxoS...
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
Kaz M. Andrew et al., Global DNA methylation patterns in Barrett’s esophagus, dysplastic Barrett’s, and esophageal adenocarcinoma are associated with BMI, gender, and tobacco use Cancer epigenetics and diagnostics. Clinical Epigenetics (2016) doi:10.1186/s13148-016-0273-7
Nazeen Sumaiya et al., Integrative analysis of genetic data sets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities. Genome Biology (2016) doi:10.1186/s13059-016-1084-z
McKenzie T. Andrew et al., DGCA: A comprehensive R package for Differential Gene Correlation Analysis. BMC Systems Biology (2016) doi:10.1186/s12918-016-0349-1
Wang Xin et al., A network-pathway based module identification for predicting the prognosis of ovarian cancer patients. Journal of Ovarian Research (2016) doi:10.1186/s13048-016-0285-0