dc.contributor.author | Gorrie-Stone, TJ | |
dc.contributor.author | Smart, MC | |
dc.contributor.author | Saffari, A | |
dc.contributor.author | Malki, K | |
dc.contributor.author | Hannon, E | |
dc.contributor.author | Burrage, J | |
dc.contributor.author | Mill, J | |
dc.contributor.author | Kumari, M | |
dc.contributor.author | Schalkwyk, LC | |
dc.date.accessioned | 2018-09-18T13:57:40Z | |
dc.date.issued | 2018-08-23 | |
dc.description.abstract | 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 | en_GB |
dc.description.sponsorship | Understanding Society is funded by the Economic and Social Research Council (ES/N00812X/1). MK is funded by Essex University and ESRC (ES/M008592/1). MS is funded by the ESRC (ES/M008592/1). LCS and JM are funded by Medical Research Council (MR/K013807/1). TGS is funded by Essex University. | en_GB |
dc.identifier.citation | Published online 23 August 2018 | en_GB |
dc.identifier.doi | 10.1093/bioinformatics/bty713 | |
dc.identifier.uri | http://hdl.handle.net/10871/34026 | |
dc.language.iso | en | en_GB |
dc.publisher | Oxford University Press (OUP) | en_GB |
dc.rights | © The Author(s) 2018. Published by Oxford University Press.
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. | en_GB |
dc.title | Bigmelon: tools for analysing large DNA methylation datasets | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2018-09-18T13:57:40Z | |
dc.identifier.issn | 1367-4803 | |
dc.description | This is the final version of the article. Available from OUP via the DOI in this record. | en_GB |
dc.description | Availability and implementation: The bigmelon package is available on Bioconductor (http://bio
conductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://
www.understandingsociety.ac.uk/about/health/data upon request. | en_GB |
dc.identifier.journal | Bioinformatics | en_GB |