MethylCap-seq is a robust procedure for genome-wide profiling of DNA methylation. The approach consists of the capture of methylated DNA using the MBD domain of MeCP2, and subsequent next-generation sequencing of eluted DNA. Elution of the captured methylated DNA is done in steps using a salt gradient, which stratifies the genome into fractions with different CpG density. The enrichment reached within the individual eluates allows for cost-effective deep sequence coverage. The profiles together yield a detailed genome-wide map of methylated regions and readily allows detection of DNA methylation in known and novel regions.
A methodology based on classical population genetic theory, i.e. the Hardy-Weinberg theorem, that combines methylomic data from MethylCap-seq with associated SNP profiles to identify monoallelically methylated loci. The developed pipeline first compares enrichment-based sequencing data of multiple samples to the public NCBI Single Nucleotide Polymorphism (SNP)-archive (dbSNP) in order to screen the obtained non-duplicate, uniquely mappable sequence reads for SNPs. Only SNP loci with an adequately coverage and allele frequency are retained and the effect of sequencing errors is further reduced by comparing the chance of a sequencing error with the chance of detecting genuine SNPs. For each single SNP locus, the Hardy-Weinberg theorem is then applied to evaluate whether the observed frequency of samples featured by a biallelic event is lower than randomly expected.
An R package for detecting differentially methylated regions (DMRs) from enrichment-based techniques for sequencing DNA methylation genome-wide. These techniques include MBD-isolated Genome Sequencing (MiGS), MBD-seq, and MeDIP-seq. MethylAction provides a pre-processing function, a DMR calling function and visualization functions. For pre-processing, MethylAction generates read counts in non-overlapping windows genome-wide. DMR calling involves initial filtering, stage one testing, stage two testing, frequency calling and bootstrapping (Figure 1). Visualization is achieved via export of tracks in BED or BigWig format for the UCSC Genome Browser, karyograms plotted by ggbio and heatmaps. Compared to existing tools that are limited to two-group comparisons, MethylAction detects more DMRs with strong differential methylation measurements confirmed by whole genome bisulfite sequencing and offers a better balance between precision and recall in cross-cohort comparisons.
A computational method that computes nucleotide-resolution methylation values from capture-based data by incorporating fragment length profiles into a model of methylation analysis. The described method was used to produce the methylation data used in tandem with gene expression to produce a novel and clinically significant gene signature in acute myeloid leukemia.
Identifies chromatin accessibility from nucleosome occupancy and methylome sequencing (NOMe-Seq). CAME uses a seed extension approach and non-parametric mixture model to identify open and closed chromatin regions. It first identifies seeds that are very likely GCHs in closed chromatin region (CCR), next extends seeds as long as the average of GCH methylation scores are smaller than a threshold, and finally decides the end point of the extended seeds using the predicted mean and standard deviation of methylation scores based on non-parametric mixture model. CAME also has function to correlate predicted chromatin accessibility to DNA methylation.
Provides several test statistics useful for detecting differentially methylated regions based on MethylCap-seq data. MethylCapSig provides five such test statistics to test equality of mean vectors in the two-sample case under high dimensional setting. The four multivariate tests and one univariate test all provide test statistics and p-values based on asymptotic distributions.
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