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DNA methylation profiles of human active and inactive X chromosomes.

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TLDR
This study provides a detailed analysis of the epigenetic profile of active and inactive X chromosomes, observing a global correlation between CGI methylation and the evolutionary age of X-chromosome strata, and that genes escaping XCI show increased methylation within gene bodies.
Abstract
X-chromosome inactivation (XCI) is a dosage compensation mechanism that silences the majority of genes on one X chromosome in each female cell. To characterize epigenetic changes that accompany this process, we measured DNA methylation levels in 45,X patients carrying a single active X chromosome (X(a)), and in normal females, who carry one X(a) and one inactive X (X(i)). Methylated DNA was immunoprecipitated and hybridized to high-density oligonucleotide arrays covering the X chromosome, generating epigenetic profiles of active and inactive X chromosomes. We observed that XCI is accompanied by changes in DNA methylation specifically at CpG islands (CGIs). While the majority of CGIs show increased methylation levels on the X(i), XCI actually results in significant reductions in methylation at 7% of CGIs. Both intra- and inter-genic CGIs undergo epigenetic modification, with the biggest increase in methylation occurring at the promoters of genes silenced by XCI. In contrast, genes escaping XCI generally have low levels of promoter methylation, while genes that show inter-individual variation in silencing show intermediate increases in methylation. Thus, promoter methylation and susceptibility to XCI are correlated. We also observed a global correlation between CGI methylation and the evolutionary age of X-chromosome strata, and that genes escaping XCI show increased methylation within gene bodies. We used our epigenetic map to predict 26 novel genes escaping XCI, and searched for parent-of-origin-specific methylation differences, but found no evidence to support imprinting on the human X chromosome. Our study provides a detailed analysis of the epigenetic profile of active and inactive X chromosomes.

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The human epidermal growth factor receptor 2 (HER2) in the breast cancer : from measurement to targeted treatment

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Dissertation

Acquisition and maintenance of transient imprinted dmrs in the human placenta and their role in development

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Dissertation

Genetic polymorphisms, DNA methylation and antipsychotic induced weight gain in schizophrenia patients.

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Journal ArticleDOI

Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays.

TL;DR: In this paper , the authors compared data quality acquired from paired bisulfite (BS) and oxBS-treated samples and found that oxBS treated samples had a significantly lower call-rate.
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Nucleic acid silencing sequences

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References
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Journal ArticleDOI

Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI

Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

TL;DR: The hierarchical model of Lonnstedt and Speed (2002) is developed into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples and the moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom.
Journal ArticleDOI

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

TL;DR: Three methods of performing normalization at the probe intensity level are presented: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure and the simplest and quickest complete data method is found to perform favorably.
Proceedings Article

Fitting a mixture model by expectation maximization to discover motifs in biopolymers.

TL;DR: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences.
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