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    Bigmelon: tools for analysing large DNA methylation datasets

    Gorrie-Stone, T.J. and Smart, M.C. and Saffari, Ayden and Malki, K. and Hannon, E. and Burrage, J. and Mill, J. and Kumari, M. and Schalkwyk, L.C. (2018) Bigmelon: tools for analysing large DNA methylation datasets. Bioinformatics 35 (6), pp. 981-986. ISSN 1460-2059.

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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.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications
    School: School of Science > Psychological Sciences
    SWORD Depositor: Mr Joe Tenant
    Depositing User: Mr Joe Tenant
    Date Deposited: 03 May 2019 12:36
    Last Modified: 24 Jun 2020 18:09
    URI: https://eprints.bbk.ac.uk/id/eprint/26836

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