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MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update)
MAGIA(2) (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394337/ https://www.ncbi.nlm.nih.gov/pubmed/22618880 http://dx.doi.org/10.1093/nar/gks460 |
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author | Bisognin, Andrea Sales, Gabriele Coppe, Alessandro Bortoluzzi, Stefania Romualdi, Chiara |
author_facet | Bisognin, Andrea Sales, Gabriele Coppe, Alessandro Bortoluzzi, Stefania Romualdi, Chiara |
author_sort | Bisognin, Andrea |
collection | PubMed |
description | MAGIA(2) (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA(2) performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA(2) tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target. |
format | Online Article Text |
id | pubmed-3394337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33943372012-07-30 MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) Bisognin, Andrea Sales, Gabriele Coppe, Alessandro Bortoluzzi, Stefania Romualdi, Chiara Nucleic Acids Res Articles MAGIA(2) (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA(2) performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA(2) tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target. Oxford University Press 2012-07 2012-05-21 /pmc/articles/PMC3394337/ /pubmed/22618880 http://dx.doi.org/10.1093/nar/gks460 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Bisognin, Andrea Sales, Gabriele Coppe, Alessandro Bortoluzzi, Stefania Romualdi, Chiara MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) |
title | MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) |
title_full | MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) |
title_fullStr | MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) |
title_full_unstemmed | MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) |
title_short | MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update) |
title_sort | magia(2): from mirna and genes expression data integrative analysis to microrna–transcription factor mixed regulatory circuits (2012 update) |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394337/ https://www.ncbi.nlm.nih.gov/pubmed/22618880 http://dx.doi.org/10.1093/nar/gks460 |
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