Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Meffil: efficient normalization and analysis of very large DNA methylation datasets

Josine L. Min, Gibran Hemani, George Davey Smith, Caroline L. Relton, Matthew Suderman

Bioinformatics · 2018

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Summary

Motivation: DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data. Results: We have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete re-implementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed

Subject
Other / interdisciplinary
Source type
Peer-reviewed study
System type
Other
DOI
10.1093/bioinformatics/bty476
Catalogue ID
BFmommpgti-6wiri8
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