Dataset Information

High-quality ChIP-seq analysis of MBD3 in human breast cancer cells.

ABSTRACT: Chromatin accessibility is tightly regulated by multiple factors/mechanisms to establish different cell type-specific gene expression programs from a single genome. Dysregulation of this process can lead to diseases including cancer. The Mi-2/nucleosome remodeling and deacetylase (NuRD) complex is thought to orchestrate chromatin structure using its intrinsic nucleosome remodeling and histone deacetylase activities. However, the detailed mechanisms by which the NuRD complex regulates chromatin structure in vivo are not yet known. To explore the regulatory mechanisms of the NuRD complex, we mapped genome-wide localization of MBD3, a structural component of NuRD, in a human breast cancer cell line (MDA-MB-231) using a modified ChIP-seq protocol. Our data showed high quality localization information (i.e., high mapping efficiency and low PCR duplication rate) and excellent consistency between biological replicates. The data are deposited in the Gene Expression Omnibus (GSE76116).

SUBMITTER: Shimbo T

PROVIDER: S-EPMC4778645 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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High-quality ChIP-seq analysis of MBD3 in human breast cancer cells.

Shimbo Takashi T Takaku Motoki M Wade Paul A PA

Genomics data 20160106

Chromatin accessibility is tightly regulated by multiple factors/mechanisms to establish different cell type-specific gene expression programs from a single genome. Dysregulation of this process can lead to diseases including cancer. The Mi-2/nucleosome remodeling and deacetylase (NuRD) complex is thought to orchestrate chromatin structure using its intrinsic nucleosome remodeling and histone deacetylase activities. However, the detailed mechanisms by which the NuRD complex regulates chromatin s ...[more]

PMID: 26981404

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Project description: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 appearing 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 1, 2012. The majority (55%) of datasets scored as being 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.

Dataset Information

High-quality ChIP-seq analysis of MBD3 in human breast cancer cells.

Publications

High-quality ChIP-seq analysis of MBD3 in human breast cancer cells.

Similar Datasets

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