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New insights in Rett syndrome using pathway analysis for transcriptomics data
The analysis of transcriptomics data is able to give an overview of cellular processes, but requires sophisticated bioinformatics tools and methods to identify the changes. Pathway analysis software, like PathVisio, captures the information about biological pathways from databases and brings this to...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005393/ https://www.ncbi.nlm.nih.gov/pubmed/27517371 http://dx.doi.org/10.1007/s10354-016-0488-4 |
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author | Ehrhart, Friederike Coort, Susan L. M. Cirillo, Elisa Smeets, Eric Evelo, Chris T. Curfs, Leopold |
author_facet | Ehrhart, Friederike Coort, Susan L. M. Cirillo, Elisa Smeets, Eric Evelo, Chris T. Curfs, Leopold |
author_sort | Ehrhart, Friederike |
collection | PubMed |
description | The analysis of transcriptomics data is able to give an overview of cellular processes, but requires sophisticated bioinformatics tools and methods to identify the changes. Pathway analysis software, like PathVisio, captures the information about biological pathways from databases and brings this together with the experimental data to enable visualization and understanding of the underlying processes. Rett syndrome is a rare disease, but still one of the most abundant causes of intellectual disability in females. Cause of this neurological disorder is mutation of one single gene, the methyl-CpG-binding protein 2 (MECP2) gene. This gene is responsible for many steps in neuronal development and function. Although the genetic mutation and the clinical phenotype are well described, the molecular pathways linking them are not yet fully elucidated. In this study we demonstrate a workflow for the analysis of transcriptomics data to identify biological pathways and processes which are changed in a Mecp2(-/y) mouse model. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1007/s10354-016-0488-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5005393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-50053932016-09-15 New insights in Rett syndrome using pathway analysis for transcriptomics data Ehrhart, Friederike Coort, Susan L. M. Cirillo, Elisa Smeets, Eric Evelo, Chris T. Curfs, Leopold Wien Med Wochenschr Main Topic The analysis of transcriptomics data is able to give an overview of cellular processes, but requires sophisticated bioinformatics tools and methods to identify the changes. Pathway analysis software, like PathVisio, captures the information about biological pathways from databases and brings this together with the experimental data to enable visualization and understanding of the underlying processes. Rett syndrome is a rare disease, but still one of the most abundant causes of intellectual disability in females. Cause of this neurological disorder is mutation of one single gene, the methyl-CpG-binding protein 2 (MECP2) gene. This gene is responsible for many steps in neuronal development and function. Although the genetic mutation and the clinical phenotype are well described, the molecular pathways linking them are not yet fully elucidated. In this study we demonstrate a workflow for the analysis of transcriptomics data to identify biological pathways and processes which are changed in a Mecp2(-/y) mouse model. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1007/s10354-016-0488-4) contains supplementary material, which is available to authorized users. Springer Vienna 2016-08-12 2016 /pmc/articles/PMC5005393/ /pubmed/27517371 http://dx.doi.org/10.1007/s10354-016-0488-4 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Main Topic Ehrhart, Friederike Coort, Susan L. M. Cirillo, Elisa Smeets, Eric Evelo, Chris T. Curfs, Leopold New insights in Rett syndrome using pathway analysis for transcriptomics data |
title | New insights in Rett syndrome using pathway analysis for transcriptomics data |
title_full | New insights in Rett syndrome using pathway analysis for transcriptomics data |
title_fullStr | New insights in Rett syndrome using pathway analysis for transcriptomics data |
title_full_unstemmed | New insights in Rett syndrome using pathway analysis for transcriptomics data |
title_short | New insights in Rett syndrome using pathway analysis for transcriptomics data |
title_sort | new insights in rett syndrome using pathway analysis for transcriptomics data |
topic | Main Topic |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005393/ https://www.ncbi.nlm.nih.gov/pubmed/27517371 http://dx.doi.org/10.1007/s10354-016-0488-4 |
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