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MethylMix 2.0: an R package for identifying DNA methylation genes
SUMMARY: DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA me...
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/PMC6129298/ https://www.ncbi.nlm.nih.gov/pubmed/29668835 http://dx.doi.org/10.1093/bioinformatics/bty156 |
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author | Cedoz, Pierre-Louis Prunello, Marcos Brennan, Kevin Gevaert, Olivier |
author_facet | Cedoz, Pierre-Louis Prunello, Marcos Brennan, Kevin Gevaert, Olivier |
author_sort | Cedoz, Pierre-Louis |
collection | PubMed |
description | SUMMARY: DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA methylation assays have been developed generating vast amounts of genome wide DNA methylation measurements. We developed MethylMix, an algorithm implemented in R to identify disease specific hyper and hypomethylated genes. Here we present a new version of MethylMix that automates the construction of DNA-methylation and gene expression datasets from The Cancer Genome Atlas (TCGA). More precisely, MethylMix 2.0 incorporates two major updates: the automated downloading of DNA methylation and gene expression datasets from TCGA and the automated preprocessing of such datasets: value imputation, batch correction and CpG sites clustering within each gene. The resulting datasets can subsequently be analyzed with MethylMix to identify transcriptionally predictive methylation states. We show that the Differential Methylation Values created by MethylMix can be used for cancer subtyping. AVAILABILITY AND IMPLEMENTATION: MethylMix 2.0 was implemented as an R package and is available in bioconductor. https://www.bioconductor.org/packages/release/bioc/html/MethylMix.html |
format | Online Article Text |
id | pubmed-6129298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61292982018-09-12 MethylMix 2.0: an R package for identifying DNA methylation genes Cedoz, Pierre-Louis Prunello, Marcos Brennan, Kevin Gevaert, Olivier Bioinformatics Applications Notes SUMMARY: DNA methylation is an important mechanism regulating gene transcription, and its role in carcinogenesis has been extensively studied. Hyper and hypomethylation of genes is a major mechanism of gene expression deregulation in a wide range of diseases. At the same time, high-throughput DNA methylation assays have been developed generating vast amounts of genome wide DNA methylation measurements. We developed MethylMix, an algorithm implemented in R to identify disease specific hyper and hypomethylated genes. Here we present a new version of MethylMix that automates the construction of DNA-methylation and gene expression datasets from The Cancer Genome Atlas (TCGA). More precisely, MethylMix 2.0 incorporates two major updates: the automated downloading of DNA methylation and gene expression datasets from TCGA and the automated preprocessing of such datasets: value imputation, batch correction and CpG sites clustering within each gene. The resulting datasets can subsequently be analyzed with MethylMix to identify transcriptionally predictive methylation states. We show that the Differential Methylation Values created by MethylMix can be used for cancer subtyping. AVAILABILITY AND IMPLEMENTATION: MethylMix 2.0 was implemented as an R package and is available in bioconductor. https://www.bioconductor.org/packages/release/bioc/html/MethylMix.html Oxford University Press 2018-09-01 2018-04-14 /pmc/articles/PMC6129298/ /pubmed/29668835 http://dx.doi.org/10.1093/bioinformatics/bty156 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/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 | Applications Notes Cedoz, Pierre-Louis Prunello, Marcos Brennan, Kevin Gevaert, Olivier MethylMix 2.0: an R package for identifying DNA methylation genes |
title | MethylMix 2.0: an R package for identifying DNA methylation genes |
title_full | MethylMix 2.0: an R package for identifying DNA methylation genes |
title_fullStr | MethylMix 2.0: an R package for identifying DNA methylation genes |
title_full_unstemmed | MethylMix 2.0: an R package for identifying DNA methylation genes |
title_short | MethylMix 2.0: an R package for identifying DNA methylation genes |
title_sort | methylmix 2.0: an r package for identifying dna methylation genes |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129298/ https://www.ncbi.nlm.nih.gov/pubmed/29668835 http://dx.doi.org/10.1093/bioinformatics/bty156 |
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