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Bigmelon: tools for analysing large DNA methylation datasets
MOTIVATION: The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result th...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419913/ https://www.ncbi.nlm.nih.gov/pubmed/30875430 http://dx.doi.org/10.1093/bioinformatics/bty713 |
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author | Gorrie-Stone, Tyler J Smart, Melissa C Saffari, Ayden Malki, Karim Hannon, Eilis Burrage, Joe Mill, Jonathan Kumari, Meena Schalkwyk, Leonard C |
author_facet | Gorrie-Stone, Tyler J Smart, Melissa C Saffari, Ayden Malki, Karim Hannon, Eilis Burrage, Joe Mill, Jonathan Kumari, Meena Schalkwyk, Leonard C |
author_sort | Gorrie-Stone, Tyler J |
collection | PubMed |
description | MOTIVATION: The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data. RESULTS: Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data. We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform. AVAILABILITY AND IMPLEMENTATION: The bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6419913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-64199132019-03-20 Bigmelon: tools for analysing large DNA methylation datasets Gorrie-Stone, Tyler J Smart, Melissa C Saffari, Ayden Malki, Karim Hannon, Eilis Burrage, Joe Mill, Jonathan Kumari, Meena Schalkwyk, Leonard C Bioinformatics Original Papers MOTIVATION: The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data. RESULTS: Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data. We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform. AVAILABILITY AND IMPLEMENTATION: The bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-03-15 2018-08-23 /pmc/articles/PMC6419913/ /pubmed/30875430 http://dx.doi.org/10.1093/bioinformatics/bty713 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Gorrie-Stone, Tyler J Smart, Melissa C Saffari, Ayden Malki, Karim Hannon, Eilis Burrage, Joe Mill, Jonathan Kumari, Meena Schalkwyk, Leonard C Bigmelon: tools for analysing large DNA methylation datasets |
title | Bigmelon: tools for analysing large DNA methylation datasets |
title_full | Bigmelon: tools for analysing large DNA methylation datasets |
title_fullStr | Bigmelon: tools for analysing large DNA methylation datasets |
title_full_unstemmed | Bigmelon: tools for analysing large DNA methylation datasets |
title_short | Bigmelon: tools for analysing large DNA methylation datasets |
title_sort | bigmelon: tools for analysing large dna methylation datasets |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419913/ https://www.ncbi.nlm.nih.gov/pubmed/30875430 http://dx.doi.org/10.1093/bioinformatics/bty713 |
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