• MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions
• Tumor purity and differential methylation in cancer epigenomics

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

• 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.

• 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.

• 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

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

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

• 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

• Discovering DNA motifs and genomic variants associated with DNA methylation