DNA-methylation-analysis

papers

• MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions
• Tumor purity and differential methylation in cancer epigenomics
• Strategies for analyzing bisulfite sequencing data now published in http://www.sciencedirect.com/science/article/pii/S0168165617315936?via%3Dihub
• Nature Genetics review: Statistical and integrative system-level analysis of DNA methylation data

Illumina is phasing out 450k and introducing the new 850k as in the end of 2015

450k/850k array analysis

• bioconductor missMethyl
• 450k-analysis-guide A bit dated, but still useful.
• 450k with minif bioc workflow
• Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi
• paper: Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data
• bigmelon bioc package. Illumina methylation array analysis for large experiments
• bioconductor MEAL MEAL aims to facilitate the analysis of Illumina Methylation 450K chips
• an R package for normalization of DNA methylation data when there are multiple cell or tissue types.
• normalize450K bioconductor
• A cross-package Bioconductor workflow for analysing methylation array data. F1000 research paper.
• paper: Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array minif package extended to 850k.
• paper:Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis

The Beta-value method has a direct biological interpretation - it corresponds roughly to the percentage of a site that is methylated. This makes the Beta-value very attractive when modeling the underlying biological effect. However, this interpretation is an approximation [22], especially when the data has not been properly preprocessed and normalized. From an analytical and statistical standpoint, the Beta-value method has severe heteroscedasticity outside the middle methylation range, which imposes serious challenges in applying many statistic models. In comparison, the M-value method is more statistically valid in differential and other statistic analysis as it is approximately homoscedastic. Although the M-value statistic does not have an intuitive biological meaning, it is possible to provide an accurate estimation of methylation status by modeling the distribution of the M-value statistic. In differential methylation analysis, we recommend using M-value because we can directly apply most statistical analysis methods designed for expression microarrays and it is easy to implement a difference threshold adjustment to improve the TPR. And the difference of M-value can be interpreted as the fold-change in the non-log scale. Although both Beta-value and M-value methods have some limitations, the two statistics are inter-convertible using Equation 3, enabling the use of the most appropriate method. We recommend using the M-value method for differential methylation analysis and also including the Beta-value statistic in final reports due to its intuitive biological interpretation.

• Comparison of clustering methods for investigation of genome-wide methylation array data

The results of simulations suggest that the hierarchical Ward–Manhattan approach provides a consistent approach and that the Manhattan distance appears to be the best metric to separate clusters based on beta-values. However, this result is not absolute with some conditions particularly under low decisive data conditions resulting in inconsistency.

• Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies InfiniumPurify V1.1

RRBS

• M3D: Statistical Testing for RRBS Data Sets

• methylkit deals with RRBS and other data. A recent release note. Many new features are implemented including segmentation of DNA methylation data similar to CNV analysis. Now it is in bioconductor.

• BiSeq RRBS centric.

Whole genome BS-seq

• MethGo: a comprehensive tool for analyzing whole-genome bisulfite sequencing data
• bismark-pipeline.pdf bismark doc
• Post-Processing Bismark Bisulphite Sequencing Data
• The Binomial Distribution, Python and Bisulphite Sequencing
• Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Bedgraph files generated by BS pipelines often come in various flavors. Critical downstream step requires aggregation of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions.

Nanopore

• Detecting DNA Methylation using the Oxford Nanopore Technologies MinION sequencer
• Cytosine Variant Calling with High-throughput Nanopore Sequencing

Batch effect

• BEclear: Batch Effect Detection and Adjustment in DNA Methylation Data

Clustering

• Clustering of (epigenetic) DNA methylation data using a variational Bayes NMF algorithm:EpiCluster bioconductor
• densityCut: an efficient and versatile topological approach for automatic clustering of biological data

Motifs

• Discovering DNA motifs and genomic variants associated with DNA methylation

Databases

• MethHC: a database of DNA methylation and gene expression in human cancers
• MethyCancer: a database of DNA Methylation and Cancer
• MethDB: DNA methylation and environmental epigenetic effects
• MethylomeDB: The Brain Methylome Database provides DNA methylation profiles from humans and mice
• DiseaseMeth: Methylomes of human disease
• NGSmethDB: Whole-genome bisulfite sequencing (WGBS) database for many different tissues, pathological conditions, and species
• MethBase: Hundreds of methylomes from well studied organisms

Visualization

• TCGA Wanderer: an interactive viewer to explore DNA methylation and gene expression data in human cancer
• MEXPRESS: a data visualization tool designed for the easy visualization of TCGA expression, DNA methylation and clinical data