Learn to use STRING, a database of known and predicted protein-protein interactions. Predictions are based on a series of evidences such as gene neighborhood, patterns of co-expression, text-mining of the literature and more. STRING allows the user to view data from individual evidences of interaction or view a 'network' of interactions among a collection of proteins. Learn to use this resource and find interaction predictions to facilitate characterization of gene and protein function.
You will learn:
This tutorial is a part of the tutorial group Text-related tools. You might find the other tutorials in the group interesting:
PubMatrix: PubMatrix, an on-line tool for multiplex literature mining of the PubMed database.
iHOP: Information Hyperlinked Over Proteins text mining resource
Textpresso: Text-mining the biological literature
Gene Ontology: Gene Ontology controlled vocabularies in biology
XplorMed: eXploring Medline abstracts
GoMiner: Ascribe biological significance to large lists of genes by annotating them with their corresponding GO categories
Controlled Vocabularies: Standardized term lists that can enhance interactions with biological databases
DAVID: A tool that analyzes large lists of genes to provide biological meaning
Entrez Overview: Overview of NCBI's Entrez Search Resource
PubMed: PubMed access to biomedical research literature
Literature and Text Mining : Tools which are related to scientific literature. Repositories, query tools, and mining resources are included.
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
Amberkar S Sandeep et al., An integrative approach for a network based meta-analysis of viral RNAi screens. Algorithms for Molecular Biology (2015) doi:10.1186/s13015-015-0035-7
Kern Ursula et al., Lysosomal protein turnover contributes to the acquisition of TGFβ-1 induced invasive properties of mammary cancer cells. Molecular Cancer (2015) doi:10.1186/s12943-015-0313-5
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Peng Jiajie et al., Measuring semantic similarities by combining gene ontology annotations and gene co-function networks Knowledge-based analysis. BMC Bioinformatics (2015) doi:10.1186/s12859-015-0474-7
Francescatto Margherita et al., Highlights from the Third European International Society for Computational Biology (ISCB) Student Council Symposium 2014 Highlights from the Third International Society for Computational Biology (ISCB) European Student Council Symposium 2014 Third International Society for Computational Biology (ISCB) European Student Council Symposium 2014. BMC Bioinformatics (2015) doi:10.1186/1471-2105-16-S3-A1