Bigmelon: Tools for analysing large DNA methylation datasets

Tyler J. Gorrie-Stone; Melissa C. Smart; Ayden Saffari; Karim Malki; Eilis Hannon; Joe Burrage; Jonathan Mill; Meena Kumari; Leonard C. Schalkwyk

doi:10.1093/bioinformatics/bty713

Bigmelon: Tools for analysing large DNA methylation datasets

Tyler J. Gorrie-Stone^*, Melissa C. Smart, Ayden Saffari, Karim Malki, Eilis Hannon, Joe Burrage, Jonathan Mill, Meena Kumari, Leonard C. Schalkwyk

^*Corresponding author for this work

Social Genetic & Developmental Psychiatry

Research output: Contribution to journal › Article › peer-review

32 Citations (Scopus)

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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. copy; 2018 The Author(s). Published by Oxford University Press.

Original language	English
Pages (from-to)	981-986
Number of pages	6
Journal	BIOINFORMATICS
Volume	35
Issue number	6
Early online date	23 Aug 2018
DOIs	https://doi.org/10.1093/bioinformatics/bty713
Publication status	Published - 15 Mar 2019

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Bigmelon: tools for analysing_GORRIE-STONE_Accepted20Aug2018_GOLD VoRFinal published version, 448 KBLicence: CC BY

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title = "Bigmelon: Tools for analysing large DNA methylation datasets",

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. copy; 2018 The Author(s). Published by Oxford University Press.",

author = "Gorrie-Stone, {Tyler J.} and Smart, {Melissa C.} and Ayden Saffari and Karim Malki and Eilis Hannon and Joe Burrage and Jonathan Mill and Meena Kumari and Schalkwyk, {Leonard C.}",

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AU - Saffari, Ayden

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AU - Hannon, Eilis

AU - Burrage, Joe

AU - Mill, Jonathan

AU - Kumari, Meena

AU - Schalkwyk, Leonard C.

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Bigmelon: Tools for analysing large DNA methylation datasets

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