ArticleWiener medizinische Wochenschrift (1946)2016
New insights in Rett syndrome using pathway analysis for transcriptomics data.
Article in Wiener medizinische Wochenschrift (1946), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-Analysis IdentifiesInternational journal of molecular sciences · 2022Pooled it
- Early Signs of Molecular Defects in iPSC-Derived Neural Stems Cells from Patients with Familial Parkinson's Disease.Biomolecules · 2022Pooled it
- MECP2 Dysfunction in Rett Syndrome: Molecular Mechanisms, Multisystem Pathology, and Emerging Therapeutic Strategies.International journal of molecular sciences · 2025Review
- Megaconial congenital muscular dystrophy secondary to novel CHKB mutations resemble atypical Rett syndrome.Journal of human genetics · 2021Article
- CyTargetLinker app update: A flexible solution for network extension in Cytoscape.F1000Research · 2018Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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