SIGNOR: a database of causal relationships between biological entities. Issue Volume 44:Issue D1(2016) (13th October 2015)

• Record Type: Journal Article
• Authors: Perfetto, Livia; Briganti, Leonardo; Calderone, Alberto; Perpetuini, Andrea Cerquone; Iannuccelli, Marta; Langone, Francesca; Licata, Luana; Marinkovic, Milica; Mattioni, Anna; Pavlidou, Theodora; Peluso, Daniele; Petrilli, Lucia Lisa; Pirrò, Stefano; Posca, Daniela; Santonico, Elena; Silvestri, Alessandra; Spada, Filomena; Castagnoli, Luisa; Cesareni, Gianni
• Is Part Of: Nucleic acids research. Volume 44:Issue D1(2016)
• Page Start: D548
• Page End: D554
• Publication Date: 2015-10-13
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkv1048
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract : Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it ), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12 000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.