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Published February 1, 2014 | Supplemental Material + Published
Journal Article Open

Large-scale quality analysis of published ChIP-seq data


ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique in PubMed as of December 2012. Individually and in aggregate these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia Of DNA Elements (ENCODE) project, developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1st 2012. The majority (55%) of datasets scored as highly successful, but a substantial minority (20%) were of apparently poor quality, and another ~25% were of intermediate quality. We discuss how different uses of ChIP-Seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e. no-immunoprecipitation and mock-immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.

Additional Information

© 2014 Marinov et al. Manuscript received September 29, 2013; accepted for publication November 21, 2013; published Early Online December 17, 2013. This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We thank members of the ENCODE consortium and members of the Wold laboratory for helpful discussions, and Henry Amrhein, Diane Trout, and Sean Upchurch for computational assistance. G.K.M. and B.J.W. are supported by the Beckman Foundation, the Donald Bren Endowment, and National Institutes of Health grants U54 HG004576 and U54 HG006998. Communicating editor: T. R. Hughes

Attached Files

Published - 209.full.pdf

Supplemental Material - 008680SI.pdf

Supplemental Material - FigureS1.pdf

Supplemental Material - FigureS10.pdf

Supplemental Material - FigureS11.pdf

Supplemental Material - FigureS2.pdf

Supplemental Material - FigureS3.pdf

Supplemental Material - FigureS4.pdf

Supplemental Material - FigureS5.pdf

Supplemental Material - FigureS6.pdf

Supplemental Material - FigureS7.pdf

Supplemental Material - FigureS8.pdf

Supplemental Material - FigureS9.pdf

Supplemental Material - TableS1.pdf

Supplemental Material - TableS2.pdf


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August 22, 2023
October 25, 2023