Software

We develop bioinformatic methods and software that address the needs of wet-lab researchers working on epigenetics / epigenomics (see http://www.computational-epigenetics.de for details). Your feedback on the existing tools (see below) as well as requests for novel methods and tools are highly appreciated (please contact Christoph Bock for further discussion).

	BiQ Analyzer (http://biq-analyzer.bioinf.mpi-inf.mpg.de/) is a software tool for easy visualization and quality control of DNA methylation data. With more than 2,000 downloads so far, BiQ Analyzer has become a standard tool for processing DNA methylation data from bisulfite sequencing. BiQ Analyzer has been selected by ABI to be part of the Applied Biosystems Software Community Program.
	EpiGRAPH (http://epigraph.mpi-inf.mpg.de/) enables biologists to analyze genome and epigenome datasets with powerful statistical and machine learning methods. In a typical workflow, the user uploads a set of genomic regions of interest (e.g. experimentally mapped enhancers, hotspots of epigenetic regulation or sites exhibiting disease-specific alterations), and EpiGRAPH searches a large database of (epi-) genomic attributes for significant overlap and correlation with the regions in the input dataset. Furthermore, EpiGRAPH can predict the status of genomic regions that were not included in the input dataset.
	MethMarker (http://methmarker.mpi-inf.mpg.de/) facilitates the design of DNA methylation assays for COBRA, bisulfite SNuPE, bisulfite pyrosequencing, MethyLight and MSP. It also implements a systematic workflow for design, optimization and (computational) validation of DNA methylation biomarkers. This workflow starts from a preselected differentially methylated region (DMR) and results in an optimized DNA methylation assay that is ready to be tested in a large-scale clinical trial.
	The Epigenome Pipeline Package (EPP) provides a collection of scripts for large-scale, automated epigenome data analysis and identification of DNA methylation alterations (e.g. between cancer cells and normal tissue). Biologically, it is based on the assumption that the user has a relatively complete (and potentially large) set of genomic regions that he or she is interested in, and all analyses are performed based on such lists of regions of interest. Typical candidate regions are all promoters, CpG islands, enhancers and other functional elements as annotated by the ENCODE consortium.

A broad overview of bioinformatic tools that are useful for epigenetic research is provided in our recent review on computational epigenetics: http://bioinformatics.oxfordjournals.org/cgi/content/full/24/1/1.