Evidence map›Paper›PMID 29931280›Full record

ArticleBioinformatics (Oxford, England)2018

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

J L Min, G Hemani, G Davey Smith, C Relton, M Suderman

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 234 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
234citing papers in PubMed, 13 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

234 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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174 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

J L MinMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
G HemaniMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
G Davey SmithMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
C ReltonMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
M SudermanMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Funding

Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_12013/1Medical Research Council MC_UU_12013/1-9Medical Research Council MC_UU_12013/2Wellcome Trust 102215/2/13/2Wellcome Trust 208806/Z/17/Z
6 · The paper itself

Abstract

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 across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies. Availability and implementation: https://github.com/perishky/meffil/. Supplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

DNA MethylationEpigenomicsDatasets as TopicOligonucleotide Array Sequence Analysis

Identifiers

PMID29931280
PMCPMC6247925

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.