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A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis.

ABSTRACT: DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling. To address this issue, we developed a cross-platform algorithm-Bayesian tool for methylation analysis (Batman)-for analyzing methylated DNA immunoprecipitation (MeDIP) profiles generated using oligonucleotide arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). We developed the latter approach to provide a high-resolution whole-genome DNA methylation profile (DNA methylome) of a mammalian genome. Strong correlation of our data, obtained using mature human spermatozoa, with those obtained using bisulfite sequencing suggest that combining MeDIP-seq or MeDIP-chip with Batman provides a robust, quantitative and cost-effective functional genomic strategy for elucidating the function of DNA methylation.

SUBMITTER: Down TA

PROVIDER: S-EPMC2644410 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis.

Down Thomas A TA Rakyan Vardhman K VK Turner Daniel J DJ Flicek Paul P Li Heng H Kulesha Eugene E Gräf Stefan S Johnson Nathan N Herrero Javier J Tomazou Eleni M EM Thorne Natalie P NP Bäckdahl Liselotte L Herberth Marlis M Howe Kevin L KL Jackson David K DK Miretti Marcos M MM Marioni John C JC Birney Ewan E Hubbard Tim J P TJ Durbin Richard R Tavaré Simon S Beck Stephan S

Nature biotechnology 20080701 7

DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profili ...[more]

PMID: 18612301

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Project description:BackgroundThe processes that compose expression of a given gene are far more complex than previously thought presenting unprecedented conceptual and mechanistic challenges that require development of new tools. Chromatin structure, which is regulated by DNA methylation and histone modification, is at the center of gene regulation. Immunoprecipitations of chromatin (ChIP) and methylated DNA (MeDIP) represent a major achievement in this area that allow researchers to probe chromatin modifications as well as specific protein-DNA interactions in vivo and to estimate the density of proteins at specific sites genome-wide. Although a critical component of chromatin structure, DNA methylation has often been studied independently of other chromatin events and transcription.ResultsTo allow simultaneous measurements of DNA methylation with other genomic processes, we developed and validated a simple and easy-to-use high throughput microplate-based platform for analysis of DNA methylation. Compared to the traditional beads-based MeDIP the microplate MeDIP was more sensitive and had lower non-specific binding. We integrated the MeDIP method with a microplate ChIP assay which allows measurements of both DNA methylation and histone marks at the same time, Matrix ChIP-MeDIP platform. We illustrated several applications of this platform to relate DNA methylation, with chromatin and transcription events at selected genes in cultured cells, human cancer and in a model of diabetic kidney disease.ConclusionThe high throughput capacity of Matrix ChIP-MeDIP to profile tens and potentially hundreds of different genomic events at the same time as DNA methylation represents a powerful platform to explore complex genomic mechanism at selected genes in cultured cells and in whole tissues. In this regard, Matrix ChIP-MeDIP should be useful to complement genome-wide studies where the rich chromatin and transcription database resources provide fruitful foundation to pursue mechanistic, functional and diagnostic information at genes of interest in health and disease.

Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.

Dataset Information

A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis.

Publications

A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis.

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