SIGNOR: A Database of Causal Relationships Between Biological Entities—A Short Guide to Searching and Browsing

Prisca Lo Surdo1, Alberto Calderone2, Gianni Cesareni1, Livia Perfetto1

1 Department of Biology, University of Rome Tor Vergata, Rome, 2 IBBE‐CNR at the Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome Tor Vergata, Rome
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 8.23
DOI:  10.1002/cpbi.28
Online Posting Date:  June, 2017
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Abstract

SIGNOR (http://signor.uniroma2.it), the SIGnaling Network Open Resource, is a database designed to store experimentally validated causal interactions, i.e., interactions where a source entity has a regulatory effect (up‐regulation, down‐regulation, etc.) on a second target entity. SIGNOR acts both as a source of signaling information and a support for data analysis, modeling, and prediction. A user‐friendly interface features the ability to search entries for any given protein or group of proteins and to display their interactions graphically in a network view. At the time of writing, SIGNOR stores approximately 16,000 manually curated interactions connecting more than 4,000 biological entities (proteins, chemicals, protein complexes, etc.) that play a role in signal transduction. SIGNOR also offers a collection of 37 signaling pathways. SIGNOR can be queried by three search tools: “single‐entity” search, “multiple‐entity” search, and “pathway” search. This manuscript describes two basic protocols detailing how to navigate and search the SIGNOR database and how to download the annotated dataset for local use. Finally, the support protocol reviews the utilities of the graphic visualizer. © 2017 by John Wiley & Sons, Inc.

Keywords: navigation; search; signor; signal transduction; visualization

     
 
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Table of Contents

  • Introduction
  • Basic Protocol 1: Single‐Entity Search
  • Support Protocol 1: Multiple‐Entity Search—Using the “All” Option
  • Support Protocol 2: Multiple‐Entity Search—Using the “Connect” Option
  • Basic Protocol 2: Pathway Browser
  • Alternate Protocol 1: The Graphic Visualizer
  • Basic Protocol 3: Downloading Data
  • Guidelines for Understanding Results
  • Commentary
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

Basic Protocol 1: Single‐Entity Search

  Necessary Resources
  • A computer with an up‐to‐date Web browser

Support Protocol 1: Multiple‐Entity Search—Using the “All” Option

  Necessary Resources
  • A computer with an up‐to‐date Web browser

Support Protocol 2: Multiple‐Entity Search—Using the “Connect” Option

  Necessary Resources
  • A computer with an up‐to‐date Web browser

Basic Protocol 2: Pathway Browser

  Necessary Resources
  • A computer with an up‐to‐date web browser

Alternate Protocol 1: The Graphic Visualizer

  Necessary Resources
  • A computer with an up‐to‐date Web browser

Basic Protocol 3: Downloading Data

  Necessary Resources
  • A computer with an up‐to‐date Web browser
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Figures

Videos

Literature Cited

Literature Cited
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  Calderone, A., Castagnoli, L., & Cesareni, G. (2013). mentha: A resource for browsing integrated protein‐interaction networks. Nature Methods, 10, 690–691. doi:10.1038/nmeth.2561. nmeth.2561 [pii].
  Kholodenko, B., Yaffe, M. B., & Kolch, W. (2012). Computational approaches for analyzing information flow in biological networks. Science Signaling, 5, re1. doi:10.1126/scisignal.2002961.
  Perfetto, L., Briganti, L., Calderone, A., Perpetuini, A. C., Iannuccelli, M., Langone, F., … Cesareni, G. (2016). SIGNOR: A database of causal relationships between biological entities. Nucleic Acids Research, 44(D1), D548–554. doi:10.1093/nar/gkv1048.
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  Turei, D., Korcsmaros, T., & Saez‐Rodriguez, J. (2016). OmniPath: Guidelines and gateway for literature‐curated signaling pathway resources. Nature Methods, 13, 966–967. doi:10.1038/nmeth.4077.
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