Dataset Information

Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation.

ABSTRACT: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. Recent advances in sequencing technologies and analyses enable us to compare hundreds of samples simultaneously; such large-scale analysis has potential to reveal the high-dimensional interrelationship level for regulatory elements and annotate novel functional genomic regions de novo. Because many experimental considerations are relevant to the choice of a method in a ChIP-seq analysis, the overall design and quality management of the experiment are of critical importance. This review offers guiding principles of computation and sample preparation for ChIP-seq analyses, highlighting the validity and limitations of the state-of-the-art procedures at each step. We also discuss the latest challenges of single-cell analysis that will encourage a new era in this field.

SUBMITTER: Nakato R

PROVIDER: S-EPMC5444249 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation.

Nakato Ryuichiro R Shirahige Katsuhiko K

Briefings in bioinformatics 20170301 2

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. Recent advances in sequencing technologies and analyses enable us to compare hundreds of samples simultaneously; such large-scale analysis has potential to reveal the high-dimensional interrelationship level for regulatory elements and annotate novel functional genomic regions de novo. Because many experimental considerations are relevant ...[more]

PMID: 26979602

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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

Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation.

Publications

Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation.

Similar Datasets

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