Dataset Information

Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO.

ABSTRACT: Background:Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets. Methods:Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discordant samples, and both an identity check for fingerprinting sample donors and a measure of sample contamination based on probes querying high-frequency SNPs. These checks were tested on 80 datasets comprising 8327 samples run on the 450K microarray from the GEO repository. Results:Nine hundred forty samples were flagged by at least one control metric and 133 samples from 20 datasets were assigned the wrong sex. In a dataset in which a subset of samples appear contaminated with a single source of DNA, we demonstrate that our measure based on outliers among SNP probes was strongly correlated (>?0.95) with another independent measure of contamination. Conclusions:A more complete examination of samples that may be mislabeled, contaminated, or have poor performance due to technical problems will improve downstream analyses and replication of findings. We demonstrate that quality control problems are prevalent in a public repository of DNA methylation data. We advocate for a more thorough quality control workflow in epigenome-wide association studies and provide a software package to perform the checks described in this work. Reproducible code and supplementary material are available at 10.5281/zenodo.1172730.

SUBMITTER: Heiss JA

PROVIDER: S-EPMC5984806 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO.

Heiss Jonathan A JA Just Allan C AC

Clinical epigenetics 20180601

<h4>Background</h4>Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets.<h4>Methods</h4>Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discord ...[more]

PMID: 29881472

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Project description:MotivationGlycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein-carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis.ResultsThe current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures.Availability and implementationhttp://www.glycanstructure.org/glymdb.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO.

Publications

Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO.

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