HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data

Summary Chromatin is composed of DNA and a variety of modified histones and non-histone proteins, which impact cell differentiation, gene regulation and other key cellular processes. We present a genome-wide chromatin landscape for Drosophila melanogaster based on 18 histone modifications, summarized by 9 prevalent combinatorial patterns. Integrative analysis with other data (non-histone chromatin proteins, DNaseI hypersensitivity, GRO-seq reads produced by engaged polymerase, short/long RNA products) reveals discrete characteristics of chromosomes, genes, regulatory elements, and other functional domains. We find that active genes display distinct chromatin signatures that are correlated with disparate gene lengths, exon patterns, regulatory functions, and genomic contexts. We also demonstrate a diversity of signatures among Polycomb targets that include a subset with paused polymerase. This systematic profiling and integrative analysis of chromatin signatures provides insights into how genomic elements are regulated, and will serve as a resource for future experimental investigations of genome structure and function.

In many genomic studies, one works with genome-position-dependent data, e.g. ChIP-chip or ChIP-Seq scores. Using conventional tools, it can be difficult to get a good feel for the data, especially the distribution of features. This article argues that the so-called Hilbert curve visualization can complement genome browsers and help to get further insights into the structure of one's data. This is demonstrated with examples from different use cases. An open-source application, called HilbertVis, is presented that allows the user to produce and interactively explore such plots. Availability: http://www.ebi.ac.uk/huber-srv/hilbert/ Contact: sanders@fs.tum.de Supplementary information: Supplementary Data are available at Bioinformatics online.