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SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update
The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an ef...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145695/ https://www.ncbi.nlm.nih.gov/pubmed/31665520 http://dx.doi.org/10.1093/nar/gkz949 |
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author | Licata, Luana Lo Surdo, Prisca Iannuccelli, Marta Palma, Alessandro Micarelli, Elisa Perfetto, Livia Peluso, Daniele Calderone, Alberto Castagnoli, Luisa Cesareni, Gianni |
author_facet | Licata, Luana Lo Surdo, Prisca Iannuccelli, Marta Palma, Alessandro Micarelli, Elisa Perfetto, Livia Peluso, Daniele Calderone, Alberto Castagnoli, Luisa Cesareni, Gianni |
author_sort | Licata, Luana |
collection | PubMed |
description | The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an effect (up/down-regulation) and to the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the up/down-regulation of the target entity. Since its first release, SIGNOR has undergone a significant content increase and the number of annotated causal interactions have almost doubled. SIGNOR 2.0 now stores almost 23 000 manually-annotated causal relationships between proteins and other biologically relevant entities: chemicals, phenotypes, complexes, etc. We describe here significant changes in curation policy and a new confidence score, which is assigned to each interaction. We have also improved the compliance to the FAIR data principles by providing (i) SIGNOR stable identifiers, (ii) programmatic access through REST APIs, (iii) bioschemas and (iv) downloadable data in standard-compliant formats, such as PSI-MI CausalTAB and GMT. The data are freely accessible and downloadable at https://signor.uniroma2.it/. |
format | Online Article Text |
id | pubmed-7145695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71456952020-04-13 SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update Licata, Luana Lo Surdo, Prisca Iannuccelli, Marta Palma, Alessandro Micarelli, Elisa Perfetto, Livia Peluso, Daniele Calderone, Alberto Castagnoli, Luisa Cesareni, Gianni Nucleic Acids Res Database Issue The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an effect (up/down-regulation) and to the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the up/down-regulation of the target entity. Since its first release, SIGNOR has undergone a significant content increase and the number of annotated causal interactions have almost doubled. SIGNOR 2.0 now stores almost 23 000 manually-annotated causal relationships between proteins and other biologically relevant entities: chemicals, phenotypes, complexes, etc. We describe here significant changes in curation policy and a new confidence score, which is assigned to each interaction. We have also improved the compliance to the FAIR data principles by providing (i) SIGNOR stable identifiers, (ii) programmatic access through REST APIs, (iii) bioschemas and (iv) downloadable data in standard-compliant formats, such as PSI-MI CausalTAB and GMT. The data are freely accessible and downloadable at https://signor.uniroma2.it/. Oxford University Press 2020-01-08 2019-10-29 /pmc/articles/PMC7145695/ /pubmed/31665520 http://dx.doi.org/10.1093/nar/gkz949 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Licata, Luana Lo Surdo, Prisca Iannuccelli, Marta Palma, Alessandro Micarelli, Elisa Perfetto, Livia Peluso, Daniele Calderone, Alberto Castagnoli, Luisa Cesareni, Gianni SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update |
title | SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update |
title_full | SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update |
title_fullStr | SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update |
title_full_unstemmed | SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update |
title_short | SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update |
title_sort | signor 2.0, the signaling network open resource 2.0: 2019 update |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145695/ https://www.ncbi.nlm.nih.gov/pubmed/31665520 http://dx.doi.org/10.1093/nar/gkz949 |
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