UC San Diego

The massive generation of genetic, epigenetic, transcriptomic, and other sources of data, allows us to pursue biological questions at scale while simultaneously adding a systems-level context to hypotheses in biology. Questions about gene expression have driven us to understand various chromatin components, most recently that has lead to the study of chromatin conformation via high-throughput methods such as HiC or HiChIP. To obtain a full understanding of chromatin conformation, integration with genetics variants (e.g. SNPs from GWAS and eQTL studies) and epigenetics signals (e.g. histone acetylation, open chromatin regions, transcription factor binding, etc) is essential. Similarly, complex diseases such as cancer can advance via a system of distinct factors that interact to form a deliberate and potent pathogenic regulatory network. Thus, it is imperative we build the resources and tools necessary to integrate multiomics signals together.

Here, I present three chapters derived from two major works that demonstrate the importance of data integration for a holistic understanding of biology. First, I present a database of HiChIP data for over 1000 samples (chapter 1) with important applications for the analysis of motifs, GWAS and eQTL studies, and network analysis (chapter 2). Second, I showcase and described the nipalsMCIA R package which reduces datasets for a systems level analysis of multiomics data (chapter 3).