The Oxford English Dictionary dates the word ‘genome’

to 1926 but has not yet seen fit to include the term ‘epigenome ’ in its illustrious pages (where it would reside handsomely between ‘epigenist ’ and ‘epigenous’). This differential treatment reflects a state of affairs in which the epigenome has yet to infiltrate the popular imagination, but belies the recent explosion of epigenomics in the scientific literature, where as many as 70 % of all abstracts featuring ‘epigenome ’ have been published within the last three years. The present-day proliferation in epigenomics, in the exploration of the dynamic regulatory layers that insulate the genome’s static DNA sequence, has been enabled by novel high-throughput techniques for interrogating the positioning of DNA (hydroxy)methylation, histone marks and open chromatin. The ready availability of genomic data, without which we could not map the location of these features, has provided the essential context needed to make biological sense of the high-throughput data, and so propel epigenomics to the forefront of mainstream biology. In this special issue, Genome Biology presents a collection of articles that describe a diverse range of novel insights into epigenomes, from human disease to ciliate reproduction to the containment of endogenous retroviruses. We also include a number of methods that will improve and simplify the study of epigenomics, in particular the computational steps that follow data generation. Finally, a selection of review and comment articles give an overview of current and future directions in epigenomics research. A new software toolbox The availability of new high-throughput methods creates a demand for software tools to process and analyze the overwhelming flow of unintelligible raw data that will inevitably be produced. Genome Biology has a proud history of publishing the most popular examples of such tools, with high profile examples including Bowtie [1] (next-generation sequencing data), MACS [2] (ChIP-seq data) and DEseq [3] (RNA-seq data). The challenge of designing bioinformatics tools for the ever expanding number of DNA methylation genome-wide profiling methods has been taken up by many bioinformatics labs [4]. In the past few months, for example, Genome Biology has published SWAN [5], a method for reducing technical variation in data from the cutting-edge Illumin