Dataset Information

An Integrated and Semiautomated Microscaled Approach to Profile Cis-Regulatory Elements by Histone Modification ChIP-Seq for Large-Scale Epigenetic Studies.

ABSTRACT: Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is the preferred approach to map histone modifications and identify cis-regulatory DNA elements throughout the genome. Multiple methods have been described to increase the efficiency of library preparation and to reduce hands-on time as well as costs. This review describes detailed steps to perform cell fixation, chromatin shearing, immunoprecipitation, and sequencing library preparation for a batch of 48-96 samples with small cell numbers. The protocol implements a semiautomated platform to reduce technical variability and improve signal-to-noise ratio as well as reduce hands-on time, thus allowing large-scale epigenetic studies of clinical samples with limited cell numbers.

SUBMITTER: Youhanna Jankeel D

PROVIDER: S-EPMC6844362 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

An Integrated and Semiautomated Microscaled Approach to Profile Cis-Regulatory Elements by Histone Modification ChIP-Seq for Large-Scale Epigenetic Studies.

Youhanna Jankeel Diana D Cayford Justin J Schmiedel Benjamin Joachim BJ Vijayanand Pandurangan P Seumois Grégory G

Methods in molecular biology (Clifton, N.J.) 20180101

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is the preferred approach to map histone modifications and identify cis-regulatory DNA elements throughout the genome. Multiple methods have been described to increase the efficiency of library preparation and to reduce hands-on time as well as costs. This review describes detailed steps to perform cell fixation, chromatin shearing, immunoprecipitation, and sequencing library preparation for a batch of 48-96 samples with small cell ...[more]

PMID: 29956160

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Project description:BackgroundThe advent of ChIP-seq technology has made the investigation of epigenetic regulatory networks a computationally tractable problem. Several groups have applied statistical computing methods to ChIP-seq datasets to gain insight into the epigenetic regulation of transcription. However, methods for estimating enrichment levels in ChIP-seq data for these computational studies are understudied and variable. Since the conclusions drawn from these data mining and machine learning applications strongly depend on the enrichment level inputs, a comparison of estimation methods with respect to the performance of statistical models should be made.ResultsVarious methods were used to estimate the gene-wise ChIP-seq enrichment levels for 20 histone methylations and the histone variant H2A.Z. The Multivariate Adaptive Regression Splines (MARS) algorithm was applied for each estimation method using the estimation of enrichment levels as predictors and gene expression levels as responses. The methods used to estimate enrichment levels included tag counting and model-based methods that were applied to whole genes and specific gene regions. These methods were also applied to various sizes of estimation windows. The MARS model performance was assessed with the Generalized Cross-Validation Score (GCV). We determined that model-based methods of enrichment estimation that spatially weight enrichment based on average patterns provided an improvement over tag counting methods. Also, methods that included information across the entire gene body provided improvement over methods that focus on a specific sub-region of the gene (e.g., the 5' or 3' region).ConclusionThe performance of data mining and machine learning methods when applied to histone modification ChIP-seq data can be improved by using data across the entire gene body, and incorporating the spatial distribution of enrichment. Refinement of enrichment estimation ultimately improved accuracy of model predictions.

Project description:BackgroundDespite the advent of Chromatin Immunoprecipitation Sequencing (ChIP-seq) having revolutionised our understanding of the mammalian genome's regulatory landscape, many challenges remain. In particular, because of their repetitive nature, the sequencing reads derived from transposable elements (TEs) pose a real bioinformatics challenge, to the point that standard analysis pipelines typically ignore reads whose genomic origin cannot be unambiguously ascertained.ResultsWe show that discarding ambiguously mapping reads may lead to a systematic underestimation of the number of reads associated with young TE families/subfamilies. We also provide evidence suggesting that the strategy of randomly permuting the location of the read mappings (or the TEs) that is often used to compute the background for enrichment calculations at TE families/subfamilies can result in both false positive and negative enrichments. To address these problems, we present the Transposable Element Enrichment Estimator (T3E), a tool that makes use of ChIP-seq data to characterise the epigenetic profile of associated TE families/subfamilies. T3E weights the number of read mappings assigned to the individual TE copies of a family/subfamily by the overall number of genomic loci to which the corresponding reads map, and this is done at the single nucleotide level. In addition, T3E computes ChIP-seq enrichment relative to a background estimated based on the distribution of the read mappings in the input control DNA. We demonstrated the capabilities of T3E on 23 different ChIP-seq libraries. T3E identified enrichments that were consistent with previous studies. Furthermore, T3E detected context-specific enrichments that are likely to pinpoint unexplored TE families/subfamilies with individual TE copies that have been frequently exapted as cis-regulatory elements during the evolution of mammalian regulatory networks.ConclusionsT3E is a novel open-source computational tool (available for use at: https://github.com/michelleapaz/T3E ) that overcomes some of the pitfalls associated with the analysis of ChIP-seq data arising from the repetitive mammalian genome and provides a framework to shed light on the epigenetics of entire TE families/subfamilies.

Dataset Information

An Integrated and Semiautomated Microscaled Approach to Profile Cis-Regulatory Elements by Histone Modification ChIP-Seq for Large-Scale Epigenetic Studies.

Publications

An Integrated and Semiautomated Microscaled Approach to Profile Cis-Regulatory Elements by Histone Modification ChIP-Seq for Large-Scale Epigenetic Studies.

Similar Datasets

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