1 - 28 of 28 results


Allows Illumina HumanMethylation BeadChip analysis. ChAMP is an integrated analysis pipeline including functions for (i) filtering low quality probes, adjustment for Infinium I and Infinium II probe design, (ii) batch effect correction, detecting differentially methylated positions (DMPs), (iii) finding differentially methylated regions (DMRs) and (iv) detection of copy number aberrations. The software also allows detection of differentially methylated genomic blocks (DMB) and Gene Set Enrichment Analysis (GSEA).


Allows to accomplish bump hunting in genomic data. bumphunter addresses batch effects, exploits the correlation structure of the microarray data to identify differentially methylated regions (DMRs), and provides a genome-wide measure of uncertainty. It was applied to microarray data and was able to identify epigenomic regions of biological interest. The tool cannot identify single base changes due to the smoothing step. It can be useful to recognize genomic regions of biological interest in large epidemiological studies.


A suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. minfi provides methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. Several preprocessing algorithms are available and the infrastructure provides a convenient way for developers to easily implement their techniques as Bioconductor tools. By making SNP annotation available, users can choose to be cautious about probes that may behave unexpectedly due to the inclusion of a SNP in the probe sequence. minfi is unique in that it provides both bump hunting and block finding capabilities, and the assessment of statistical significance for the identified regions. Finally, because the package is implemented in Bioconductor, it gives users access to the countless analysis and visualization tools available in R.

COHCAP / City of Hope CpG Island Analysis Pipeline

Provides tools for analysing single-nucleotide resolution methylation data. COHCAP is a pipeline that covers most user needs for differential methylation and integration with gene expression data. The software includes quality control metrics, defining differentially methylated CpG sites, defining differentially methylated CpG islands and visualization of methylation data. It contains two different methods of CpG island analysis. COHCAP has been shown scalable for high-quality integrative analysis of cell line data as well as large heterogeneous patient samples.

DM-BLD / Differential Methylation detection using a hierarchical Bayesian model exploiting Local Dependency

Detectes differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation changes in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables.

DMRMark / DMR detection based on non-homogeneous hidden Markov model

Models methylation status and detects differentially methylated regions (DMRs) from methylation array data. DMRMark is based on non-homogeneous hidden Markov model (HMM). It can systematically pool the information from individual array probes for better DMR detection. The tool models the biological meaning of different methylation status of paired M-values. It can be extended to handle more complexity of real scenarios.

DiMmeR / Discovery of Multiple Differentially Methylated Regions

Guides scientists the whole way through EWAS data analysis. DiMmer offers parallelized statistical methods for identifying DMRs in both Illumina 450K and 850K EPIC chip data and also methylated regions in the human genome. DiMmeR can directly process Ilummina IDAT raw files. It performs background correction, normalization, blood cell composition estimation, single CpG analysis with multiple testing correction, and DMR search with statistical significance computation.


A method for identifying differentially methylated regions. First, the data is divided into smaller segments based on genomic distance between consecutive probes. Then, each of these segments is divided into regions with consistent differential methylation patterns. For this, all possible segmentations are considered and the optimal one is chosen according to the minimum description length (MDL) principle. Finally, the significance of differential methylation in each region is assessed using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches.

GAMP / Global Analysis of Methylation Profiles

Investigates methylation profiles in globality across the epigenome or restricted to a large number of CpGs. GAMP sums the methylation profile up for each individual. It is thus able to try out for association between the overall methylation distribution and an outcome variable. It can make adjustments for additional covariates by testing the spline coefficients. This tool can be applied to data obtained through high throughput array or sequencing-based technology.


Fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model. DMRcate provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. It includes bedGraph generation, GRanges generation and plotting functions.

comb-p / combined-pvalues

A command-line tool and a python library that manipulates BED files of possibly irregularly spaced P-values and (1) calculates auto-correlation, (2) combines adjacent P-values, (3) performs false discovery adjustment, (4) finds regions of enrichment (i.e. series of adjacent low P-values) and (5) assigns significance to those regions. In addition, tools are provided for visualization and assessment. The comb-p software is useful in contexts where auto-correlated P-values are generated across the genome. Because the library accepts input in a simple, standardized format and is unaffected by the origin of the P-values, it can be used for a wide variety of applications.

ADMIRE / Analysis of DNA methylation in genomic regions

Allows to analyze and visualize differential methylation in genomic regions. ADMIRE is a semi-automatic pipeline that features five different normalization methods and performs two one-sided two-sample rank tests (Mann–Whitney U tests). The software features arbitrary experimental settings, quality control, automatic filtering, normalization, multiple testing, differential analyses on arbitrary genomic regions. It additionally implements a gene set enrichment procedure.

ABC.RAP / Array Based CpG Region Analysis Pipeline

Identifies candidate genes that are “differentially methylated” between cases and controls. ABC.RAP applies Student’s t-test and delta beta analysis to identify candidate genes containing multiple “CpG sites”. It can annotate each filtered probe with gene name, chromosome number, probe location, distance from transcription start site (TSS), and relation to CpG islands. The tool offers option to specify probes where the average beta value of the cases or controls is greater than a high_meth cutoff value or less than a low_meth cutoff value.