Deciphering the regulome of the heart

Within a given cell or tissue, the functionally required gene transcription levels are established through a repertoire of interacting genomic elements, such as transcriptional enhancers, repressors, insulators and tissue-specific switches. Collectively, the whole set of regulatory components in a cell is sometimes referred to as the regulome. Correctly identifying and describing all regions in the genome involved in gene regulation is a colossal task. These regulatory DNA elements are predominantly located in non-coding sequences, which constitute 98% of the human genome, resulting in a search space of billions of base pairs of DNA. Variations in non-coding sequences can result in increased disease risk, as demonstrated by an ever-increasing number of studies. This underlines the importance of learning as much about the regulome as we can. To this end, a plethora of technologies have been developed over the last decades. Many of these techniques rely on so-called next-generation sequencing, e.g. ChIP-seq, ATAC-seq and RNA-seq. In this thesis we use those tools and the datasets that were generated with them. Correct interpretation of these datasets requires the application of several computational analysis techniques. In this thesis we have optimized parts of the computational analysis pipeline in order to further facilitate studies that are aimed at deciphering the regulome.