Epigenetic marks, such as DNA methylation and histone modifications, are important regulatory mechanisms that allow a single genomic sequence to give rise to a complex multicellular organism. When studying mechanisms of epigenetic regulation, the analyses depend on the experimental technologies and the available data. Recent advancements in sequencing technologies allow for the efficient extraction of genome-wide maps of epigenetic marks. A number of large-scale mapping projects, such as ENCODE and IHEC, intensively produce data for different tissues and cell cultures. The increasing quantity of data highlights a major bottleneck in bioinformatic research, namely the lack of bioinformatic tools for analyzing these data. To date, there are bioinformatics tools for detailed (mostly visual) inspection of single genomic loci, allowing biologists to focus research on regions of interest. Also, efficient tools for manipulation and analysis of the data have been published, but often they require computer science abilities. Furthermore, the available tools provide solutions to only already well formulated biological questions. What is missing, in our opinion, are tools (or pipelines of tools) to explore the data interactively, in a process that would facilitate a trained biologist to recognize interesting aspects and pursue them further until concrete hypotheses are formulated. A possible solution stems from the best practices in the fields of information retrieval and exploratory search. In this thesis, I propose EpiExplorer, a paradigm for integration of state-of-the-art information retrieval methods and indexing structures, applied to offer instant interactive exploration of large epigenetic datasets. The algorithms we use are developed for semi-structured text data, but we apply them on bioinformatic data through clever textual mapping of biological properties. We demonstrate the power of EpiExplorer in a series of studies that address interesting biological problems. We also present in this manuscript EpiGRAPH, a bioinformatic software that we developed with colleagues. EpiGRAPH helps identify and model significant biological associations among epigenetic and genetic properties for sets of regions. Using EpiExplorer and EpiGRAPH, independently or in a pipeline, provides the bioinformatic community with access to large databases of annotations, allows for exploratory visualizations or statistical analysis and facilitates reproduction and sharing of results.

MOTIVATIONRecurrent DNA breakpoints in cancer genomes indicate the presence of critical functional elements for tumor development. Identifying them can help determine new therapeutic targets. High-dimensional DNA microarray experiments like arrayCGH afford the identification of DNA copy number breakpoints with high precision, offering a solid basis for computational estimation of recurrent breakpoint locations.RESULTSWe introduce a method for identification of recurrent breakpoints (consensus breakpoints) from copy number aberration datasets. The method is based on weighted kernel counting of breakpoints around genomic locations. Counts larger than expected by chance are considered significant. We show that the consensus breakpoints facilitate consensus segmentation of the samples. We apply our method to three arrayCGH datasets and show that by using consensus segmentation we achieve significant dimension reduction, which is useful for the task of prediction of tumor phenotype based on copy number data. We use our approach for classification of neuroblastoma tumors from different age groups and confirm the recent recommendation for the choice of age cut-off for differential treatment of 18 months. We also investigate the (epi)genetic properties at consensus breakpoint locations for seven datasets and show enrichment in overlap with important functional genomic regions.AVAILABILITYImplementation in R of our approach can be found at http://www.mpi-inf.mpg.de/ ∼laura/FeatureGrouping.html.CONTACTlaura@mpi-inf.mpg.de.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.

This chapter describes bioinformatic tools for analyzing epigenome differences between species and in diseased versus normal cells. We illustrate the interplay of several Web-based tools in a case study of CpG island evolution between human and mouse. Starting from a list of orthologous genes, we use the Galaxy Web service to obtain gene coordinates for both species. These data are further analyzed in EpiGRAPH, a Web-based tool that identifies statistically significant epigenetic differences between genome region sets. Finally, we outline how the use of the statistical programming language R enables deeper insights into the epigenetics of human diseases, which are difficult to obtain without writing custom scripts. In summary, our tutorial describes how Web-based tools provide an easy entry into epigenome data analysis while also highlighting the benefits of learning a scripting language in order to unlock the vast potential of public epigenome datasets.

Epigenome mapping consortia are generating resources of tremendous value for studying epigenetic regulation. To maximize their utility and impact, new tools are needed that facilitate interactive analysis of epigenome datasets. Here we describe EpiExplorer, a web tool for exploring genome and epigenome data on a genomic scale. We demonstrate EpiExplorer's utility by describing a hypothesis-generating analysis of DNA hydroxymethylation in relation to public reference maps of the human epigenome. All EpiExplorer analyses are performed dynamically within seconds, using an efficient and versatile text indexing scheme that we introduce to bioinformatics. EpiExplorer is available at http://epiexplorer.mpi-inf.mpg.de.

Modern life sciences are becoming increasingly data intensive, posing a significant challenge for most researchers and shifting the bottleneck of scientific discovery from data generation to data analysis. As a result, progress in genome research is increasingly impeded by bioinformatic hurdles. A new generation of powerful and easy-to-use genome analysis tools has been developed to address this issue, enabling biologists to perform complex bioinformatic analyses online - without having to learn a programming language or downloading and manually processing large datasets. In this tutorial paper, we describe the use of EpiGRAPH (http://epigraph.mpi-inf.mpg.de/) and Galaxy (http://galaxyproject.org/) for genome and epigenome analysis, and we illustrate how these two web services work together to identify epigenetic modifications that are characteristics of highly polymorphic (SNP-rich) promoters. This paper is supplemented with video tutorials (http://tinyurl.com/yc5xkqq), which provide a step-by-step guide through each example analysis.

The EpiGRAPH web service http://epigraph.mpi-inf.mpg.de/ enables biologists to uncover hidden associations in vertebrate genome and epigenome datasets. Users can upload sets of genomic regions and EpiGRAPH will test multiple attributes (including DNA sequence, chromatin structure, epigenetic modifications and evolutionary conservation) for enrichment or depletion among these regions. Furthermore, EpiGRAPH learns to predictively identify similar genomic regions. This paper demonstrates EpiGRAPH's practical utility in a case study on monoallelic gene expression and describes its novel approach to reproducible bioinformatic analysis.

Abstract EpiGRAPH•regression: A toolkit for (epi-)genomic correlation analysis and prediction of quantitative attributes Konstantin Halachev Master of Science

EpiGRAPH•regression : A toolkit for (epi-)genomic correlation analysis and prediction of quantitative attributes