Methylated DNA immunoprecipitation sequencing data analysis software tools

An R package for comprehensive analysis of DNA methylation data obtained with any experimental protocol that provides single-CpG resolution, including Infinium 450K microarray and bisulfite sequencing protocols, but also MeDIP-seq and MBD-seq.

Allows analysis of enrichment-based epigenomic data. Repitools is an R package that provides several functions for quality assessment, visualization, summarization and statistical analysis of epigenomics experiments. The software utilizes aroma.affymetrix and several Bioconductor packages for various preprocessing steps. It includes for instance BayMeth for quantifying methylation. It was tested on Affymetrix and Nimblegen tiling microarrays and Illumina Genome Analyzer sequencing data.

Allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments. MEDME is an experimental and analytical methodology designed to obtain enhanced estimates that better describe the true values of DNA methylation level throughout the genome.

Implements a statistical framework for modelling and transformation of MeDIP-seq enrichment data to absolute methylation levels similar to Bisulfite Sequencing (BS) read-outs. QSEA comprises functionality for data normalization that accounts for the effect of Copy Number Variations (CNVs) on the read-counts as well as for the detection and annotation of differentially methylated regions (DMRs). It is a reliable workflow for detecting aberrant methylation in patient cohorts. Results are strongly correlated with BS-seq data and DMRs can be confirmed by the literature as well as experimental validation.

Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.

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.

Provides functions for the quality control and analysis of data derived from immunoprecipitation (IP)-seq samples. MEDIPS starts with the aligned reads (typically bam files) and can be used for any genome of interest. It allows for an arbitrary number of replicates per group and integrates sophisticated statistical methods for the detection of differential coverage between experimental conditions. This updated version adds novel functionality to MEDIPS, including correlation analysis between samples, and takes advantage of Bioconductor’s annotation databases to facilitate annotation of specific genomic regions.

We present a pipeline for the pre-processing, quality assessment, read distribution and methylation estimation for MeDIP-sequence datasets.

A python package that analyzes genome-wide DNA methylation data produced by the Methyl-MAPS (methylation mapping analysis by paired-end sequencing) method. Methyl-Analyzer processes and integrates sequencing reads from methylated and unmethylated compartments and estimates CpG methylation probabilities at single base resolution. Methyl-MAPS is an enzymatic-based method that uses both methylation-sensitive and -dependent enzymes covering >80% of CpG dinucleotides within mammalian genomes. It combines enzymatic-based approaches with high-throughput next-generation sequencing technology to provide whole genome DNA methylation profiles.

A computational pipeline bringing together numerous software packages to perform a full analysis of MeDIP-seq data, including sequence alignment, quality control (QC), and determination and annotation of DMRs.

Enables to examine the multi-omics integrated analysis and supplies users a way to study their own multi-omics data. It works on the integrated analysis of gene expression, DNA methylation, and genetic variations. BioVLAB-mCpG-SNP-EXPRESS allows user to explore the analysis result at the multiple levels such as the gene, gene set, pathway, and network, and also from the multiple perspectives such as DNA methylation, gene expression, and sequence variation in terms of phenotype differences.

An R script, which as an additional step in MeDIP-Seq data analysis workflow, enables the allocation of strands to methylated DNA regions. DISMISS does this by analyzing the proportions of first mate reads aligning to the methylated locus from the plus and minus strands.

Provides analysis, management and visualization tools for next-generation sequencing (NGS) data. Strand NGS supports extensive workflows for alignment, RNA-seq, small RNA-seq, DNA-seq, Methyl-seq, MeDIP-seq and ChIP-seq experiments. This tool includes standard differential expression analysis for different experimental conditions, as well as differential splicing analysis. It can notice variants in the transcriptome and gene fusion events.

Infers methylation at individual CGs by modelling biases inherent in MethylSeq experiments. MetaMap is a statistical method that first accounts for the biases in MethylSeq data and then generates an analysis of the data that is suitable for use in comparative studies. An additional important feature of the software is the annotation of strongly unmethylated islands (SUMIs) which, as opposed to the current definition of CpG islands, incorporate information from both a reference sequence and genome-scale methylation data.