1 - 14 of 14 results


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.


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.

QSEA / Quantitative Sequencing Enrichment Analysis

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.


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.


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.


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.


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.