STRING

• Map identifiers to STRINGTurn gene names, synonyms and UniProt accessions into STRING's protein IDs, with each protein's name and description. Use the IDs in the other STRING steps: they answer faster and mean exactly one protein. Identifiers STRING doesn't know are listed rather than failing the step.
• Get interaction networkGet the interactions among a set of proteins, each with STRING's confidence score and the evidence behind it (experiments, databases, text mining, co-expression...). Choose functional, physical or regulatory interactions and a minimum confidence, and optionally widen the network with the closest partners.
• Get interaction partnersGet the proteins that interact with each of a set, from the whole of the organism's proteins, most confident first, with the score and evidence for each.
• Get network imageGet links to a picture of the proteins' network (PNG, high resolution PNG or SVG), ready to put in an email, a message or a report, and to the network's page on string-db.org.
• Run functional enrichmentFind the functions, pathways and other terms a set of proteins has more of than chance would give: GO processes, functions and components, KEGG, Reactome and WikiPathways, diseases, tissues, domains and papers. Each term comes with its FDR, strength and the proteins that carry it, most significant first. Use two or more proteins: with one, STRING adds its 10 closest partners.
• Get functional annotationGet every term STRING assigns to a set of proteins (GO terms, pathways, keywords, domains, tissues, diseases...), whether or not it is enriched, with the proteins carrying each. Papers are included only when their category is picked.
• Run interaction enrichmentTest whether a set of proteins has more interactions among itself than a random set of the same size would (PPI enrichment): a sign they work together. Returns the counts, the p-value and whether it is significant.
• Describe a gene setGet up to three short descriptions of what a set of proteins is about, chosen by STRING from its enriched terms, such as 'Cellular response to gamma radiation'.
• Find proteins for a termFind the proteins an organism has for a pathway, process, disease, tissue or domain, by the term's ID (hsa04115, GO:0006915) or by words in its name (melanoma). Best match first; each term lists up to about 100 proteins.
• Get similarity scoresGet how similar the proteins of a set are to one another, as Smith-Waterman bit scores (50 or more), to spot paralogs. Each protein is also scored against itself, the most it can score.
• Find closest homologs in other organismsFind each protein's most similar protein in other organisms, such as the mouse counterpart of a human gene, with its name, STRING ID and bit score.
• Find speciesFind organisms in STRING by name or taxon ID, to get the taxon ID the other steps take. Core organisms, whose networks draw on every kind of evidence, come first.
• Get STRING versionGet STRING's current release and the release Lodol's STRING steps read, and whether they are the same.

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