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DISNOR: a disease network open resource
DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753342/ https://www.ncbi.nlm.nih.gov/pubmed/29036667 http://dx.doi.org/10.1093/nar/gkx876 |
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author | Lo Surdo, Prisca Calderone, Alberto Iannuccelli, Marta Licata, Luana Peluso, Daniele Castagnoli, Luisa Cesareni, Gianni Perfetto, Livia |
author_facet | Lo Surdo, Prisca Calderone, Alberto Iannuccelli, Marta Licata, Luana Peluso, Daniele Castagnoli, Luisa Cesareni, Gianni Perfetto, Livia |
author_sort | Lo Surdo, Prisca |
collection | PubMed |
description | DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage. DISNOR can be freely accessed at http://DISNOR.uniroma2.it/ where >3700 disease-networks, linking ∼2600 disease genes, can be explored. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred ‘patho-pathways’ at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes—either annotated in DisGeNET or user-defined—DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function offers additional information on disease-associated cellular pathways and disease similarity. |
format | Online Article Text |
id | pubmed-5753342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57533422018-01-05 DISNOR: a disease network open resource Lo Surdo, Prisca Calderone, Alberto Iannuccelli, Marta Licata, Luana Peluso, Daniele Castagnoli, Luisa Cesareni, Gianni Perfetto, Livia Nucleic Acids Res Database Issue DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage. DISNOR can be freely accessed at http://DISNOR.uniroma2.it/ where >3700 disease-networks, linking ∼2600 disease genes, can be explored. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred ‘patho-pathways’ at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes—either annotated in DisGeNET or user-defined—DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function offers additional information on disease-associated cellular pathways and disease similarity. Oxford University Press 2018-01-04 2017-10-03 /pmc/articles/PMC5753342/ /pubmed/29036667 http://dx.doi.org/10.1093/nar/gkx876 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Database Issue Lo Surdo, Prisca Calderone, Alberto Iannuccelli, Marta Licata, Luana Peluso, Daniele Castagnoli, Luisa Cesareni, Gianni Perfetto, Livia DISNOR: a disease network open resource |
title | DISNOR: a disease network open resource |
title_full | DISNOR: a disease network open resource |
title_fullStr | DISNOR: a disease network open resource |
title_full_unstemmed | DISNOR: a disease network open resource |
title_short | DISNOR: a disease network open resource |
title_sort | disnor: a disease network open resource |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753342/ https://www.ncbi.nlm.nih.gov/pubmed/29036667 http://dx.doi.org/10.1093/nar/gkx876 |
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