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SMART / Specific Methylation Analysis and Report Tool

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Detects the cell type-specific methylation marks by integrating multiple methylomes from human cell lines and tissues. SMART is an entropy-based framework focused on integrating of a large number of DNA methylomes for the de novo identification of cell type-specific MethyMarks. To facilitate the specific methylation analysis, this method dynamically integrates multiple methylomes and identifies the cell type-specific methylation marks.


A package based on a statistical method for detecting DMRs from WGBS (Whole Genome Bisulfite Sequencing) data without replicates. A key feature of DSS-single is to estimate biological variation when replicated data are not available. The method takes advantage of the spatial correlation of methylation levels: since the methylation levels from nearby CpG sites are similar, we can use nearby CpG sites as ‘pseudo-replicates’ to estimate dispersion. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results.


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.


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An R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. methylKit is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.


Identifies differentially methylated regions (DMRs) within whole genome and targeted sequencing data with unrivaled specificity and sensitivity. A binary segmentation algorithm combined with a two-dimensional statistical test allows to detect DMRs in large methylation experiments with multiple groups of samples in minutes rather than days using off-the-shelf hardware. metilene outperforms other state-if-the-art tools for low coverage data, and can estimate missing data. Hence, metilene is a versatile tool to study the effect of epigenetic modifications in differentiation/development, tumorigenesis and systems biology on a global, genome-wide level.

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.

MACAU / Mixed model Association for Count data via data Augmentation

Allows to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. MACAU is a binomial mixed model and an efficient, sampling-based algorithm for approximate parameter estimation and p-value computation. This software is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations.


A comprehensive tool for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy.

bicycle / BIsulfite-based methylCYtosine CalLEr

Analyzes whole genome bisulfite sequencing (WGBS) data. bicycle is a next-generation sequencing (NGS) bioinformatic pipeline that can process data from directional (Lister) and non-directional (Cokus) bisulfite sequencing protocols and from single-end and paired-end sequencing. It also performs methylation calls for cytosines in CG and non-CG contexts (CHG and CHH). It provides statistical methylcytosine calling and offers several filters to screen reads.


A statistical method to detect differentially methylated loci (DML) for BS-seq data under general experimental design. At each CpG site, the count data are modeled by a beta-binomial regression with “arcsine” link function. Model fitting is based on the transformed methylation levels by applying generalized least square (GLS) procedure, which provides estimates for regression coefficients and their covariance. Hypothesis testing is achieved using a Wald test at each CpG site, and the significance levels can be used for DML calling. Simulation and real data analyses demonstrate that our method is accurate, powerful, robust and computationally efficient.


A non-parametric, kernel-based method, M3D, to detect higher order changes in methylation profiles, such as shape, across pre-defined regions. The test statistic explicitly accounts for differences in coverage levels between samples, thus handling in a principled way a major confounder in the analysis of methylation data. Empirical tests on real and simulated datasets show an increased power compared to established methods, as well as considerable robustness with respect to coverage and replication levels.


Detects differentially methylated (DM) regions from bisulfite sequencing (BS) data generated from different protocols, such as whole-genome bisulfite sequencing (WGBS) and reduced representative BS. HMM-DM is an approach that first uses a hidden Markov model (HMM) to identify DM CG sites accounting for spatial correlation across CG sites and variation across samples, and then summarizes identified sites into regions. The main methodological contributions of this method are: (i) it can robustly identify DM regions with various lengths and DM singletons, (ii) methylation variation across samples in the same group is well accounted for and (iii) it is suitable for both whole genome and targeted bisulfite methylation sequencing data.


A statistical method to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS) datasets. GetisDMR utilizes the beta-binomial regression model to account for confounding effects, as well as biological and sampling variations. It further uses a local Getis-Ord statistic to combine information from nearby CpG sites to detect DMRs. The region-wise overall test statistic allows for the detection of DMRs directly, which reduces the number of hypothesis being tested and increases the power of the proposed method. Through extensive simulations and an application to two mouse datasets, we demonstrate that GetisDMR achieves better sensitivities, positive predictive values, more exact locations and better agreement of DMRs with current biological knowledge.


Uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity.

SMAP / Streamlined Methylation Analysis Pipeline

Allows to extract multiple types of information (such as DMCs, DMRs, SNPs and ASM) from various types of RRBS and Bis-seq data. SMAP is designed to be an easy-to-use, one-stop and sophisticated package for methylation analyses. The pipeline consists of seven operational stages: (i) reference preparation, (ii) read preparation, (iii) alignment, (iv) calculation of methylation rate, (v) differentially methylated regions (DMR) detection, (vi) single nucleotide polymorphism (SNP) and allele-specific DNA methylation (ASM) calling and (vii) summarization.


Detects candidate regions and evaluates statistical significance at the region level. dmrseq is a two-stage approach that calculates a statistic for each candidate differentially methylated region (DMR) that takes into account variability between biological replicates and spatial correlation among neighboring loci. With this tool, significance of each region is assessed via a permutation procedure which uses a pooled null distribution that can be generated from as few as two biological replicates.

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.


Identifies differentially methylated cytosines and regions. Hidden Markov model (HMM)-Fisher is efficient and robust in identifying heterogeneous differentially methylated (DM) regions. This method outperforms the current available methods to which we have compared. It involves using a hidden Markov model to incorporate methylation information from neighboring CG sites. It also can capture the unique biological features of methylation sequencing data and remove the impact of sequencing errors. The HMM-Fisher method is designed to identify both differentially methylated regions and cytosines (DMRs and DMCs respectively), and it is suitable for both RRBS and whole-genome bisulfite sequencing (WGBS) data.


A software tool that not only fulfills the core data analysis requirements (e.g. sequence alignment, differential methylation analysis, etc.) but also provides useful tools for methylation data annotation and visualization. Specifically, Methy-Pipe uses Burrow-Wheeler Transform (BWT) algorithm to directly align bisulfite sequencing reads to a reference genome and implements a novel sliding window based approach with statistical methods for the identification of differentially methylated regions (DMRs). The capability of processing data parallelly allows it to outperform a number of other bisulfite alignment software packages.

WBSA / Web Service for Bisulfite Sequencing Data Analysis

A free web application for analysis of whole-genome bisulfite-sequencing (WGBS) and genome-wide reduced representation bisulfite sequencing (RRBS) data. WBSA not only focuses on CpG methylation, but also allows CHG and CHH analysis. BWA is incorporated as its mapping software. WBSA can be applied to DNA methylation researches for animals and plants and it provides advanced analysis for both WGBS and RRBS. It can also identify differently methylated regions (DMRs) in different strings. WBSA includes six modules: Home, WGBS, RRBS, DMR, Documents and Downloads, and provides the executable package for downloads and local installation. WGBS and RRBS modules include four main steps: pre-processing of reads and reference, alignment to the reference, identification of methylcytosines and annotation. DMR module includes DMRs identification and annotation of the correlative genes.


Takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Qvalue generates various plots automatically, allowing one to make sensible significance cut-offs. It can be applied to problems in genomics, brain imaging, astrophysics, and data mining.

RADMeth / Regression Analysis of Differential Methylation

A software package for computing individual differentially methylated sites and genomic regions in whole genome bisulfite sequencing (WGBS) data. For rapid differential methylation analysis, RADMeth should be run on a computing cluster with a few hundred available nodes, in which case it takes approximately a few hours to process a dataset consisting of 30-50 WGBS samples. RADMeth can also be used on a personal workstation, in which case differential methylation analysis will take significantly longer. Note that the actual processing time depends on the coverage of each sample, the number of sites analyzed, and the number of samples in the dataset.


A comprehensive genome-scale DNA methylation analysis server based on RRBS data. RRBS-Analyser can assess sequencing quality, generate detailed statistical information, align the bisulfite-treated short reads to reference genome, identify and annotate the methylcytosines (5mCs) and associate them with different genomic features in CG, CHG, and CHH content. RRBS-Analyser supports detection, annotation, and visualization of differentially methylated regions (DMRs) for multiple samples from nine reference organisms. Moreover, RRBS-Analyser provides researchers with detailed annotation of DMR-containing genes, which will greatly aid subsequent studies. The input of RRBS-Analyser can be raw FASTQ reads, generic SAM format, or self-defined format containing individual 5mC sites. RRBS-Analyser can be widely used by researchers wanting to unravel the complexities of DNA methylome in the epigenetic community.