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A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.

ABSTRACT: Most approaches for analyzing ChIP-Seq data are focused on inferring exact protein binding sites from a single library. However, frequently multiple ChIP-Seq libraries derived from differing cell lines or tissue types from the same individual may be available. In such a situation, a separate analysis for each tissue or cell line may be inefficient. Here, we describe a novel method to analyze such data that intelligently uses the joint information from multiple related ChIP-Seq libraries. We present our method as a two-stage procedure. First, separate single cell line analysis is performed for each cell line. Here, we use a novel mixture regression approach to infer the subset of genes that are most likely to be involved in protein binding in each cell line. In the second step, we combine the separate single cell line analyses using an Empirical Bayes algorithm that implicitly incorporates inter-cell line correlation. We demonstrate the usefulness of our method using both simulated data, as well as real H3K4me3 and H3K27me3 histone methylation libraries.

SUBMITTER: Ferguson JP

PROVIDER: S-EPMC3770480 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.

Ferguson John P JP Cho Judy H JH Zhao Hongyu H

Statistical applications in genetics and molecular biology 20120210 3

Most approaches for analyzing ChIP-Seq data are focused on inferring exact protein binding sites from a single library. However, frequently multiple ChIP-Seq libraries derived from differing cell lines or tissue types from the same individual may be available. In such a situation, a separate analysis for each tissue or cell line may be inefficient. Here, we describe a novel method to analyze such data that intelligently uses the joint information from multiple related ChIP-Seq libraries. We pres ...[more]

PMID: 22499701

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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.

Dataset Information

A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.

Publications

A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.

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