Bigmelon: tools for analysing large DNA methylation datasets.
Bioinformatics
; 35(6): 981-986, 2019 03 15.
Article
in En
| MEDLINE
| ID: mdl-30875430
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. 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
/
DNA Methylation
Type of study:
Observational_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2019
Type:
Article
Affiliation country:
United kingdom