MOTIVATION: The genome interacts with itself within the volume of the cell nucleus to process information. These interactions mediate signal integration, gene regulation, and cell identity. The identification of new therapeutic targets from non-coding disease-associated variants relies critically on correctly assigning variants to genes through 3D interactions. Experimental techniques in 3D genomics, such as HiC and HiChIP, allow the mapping of interactions through sequencing. Bioinformatics for 3D genomics contends primarily with contact matrices that contain interaction frequencies for all possible element pairs, and BEDPE files that store element pairs that interact. Whereas the tools available for processing linear genomic data are mature, operating on contact matrices and BEDPE files remains cumbersome, opaque, and error-prone, as researchers have had to shoehorn tools originally designed for linear data. A genome arithmetic designed from the ground up for 3D genomics does not yet exist. RESULTS: We present AQuA Tools, a suite of shell- and R-based command-line tools that provide a set of core operations on contact matrices and BEDPE files motivated by key questions in population genetics, cancer research, and precision medicine. We have designed our core operations to be clear, reliable, intuitive and versatile. Core operations can be chained together along with standard UNIX commands. Our goal is to make AQuA Tools easy for the novice to learn and the go-to choice for power users. We hope our tools will motivate more researchers to use 3D genomic data in their projects. AVAILABILITY AND IMPLEMENTATION: We provide and maintain AQuA Tools at https://github.com/axiotl/aqua-tools.

Epistasis, or gene-gene interaction, is a ubiquitous phenomenon that is inadequately addressed in human genetic studies. There are few tools that can accurately identify high-order epistatic interactions, and there is a lack of general understanding as to how epistatic interactions fit into genetic architecture. Here we approach both problems through the lens of genetic programming (GP). It has recently been proposed that increasing open-endedness of GP will result in more complex solutions that better acknowledge the complexity of human genetic datasets. Moreover, the solutions evolved in open-ended GP can serve as model organisms in which to study general effects of epistasis on phenotype. Here we introduce a prototype computational evolution system that implements an open-ended GP and generates organisms that display epistatic interactions. These interactions are significantly more prevalent and have a greater effect on fitness than epistatic interactions in organisms generated in the absence of selection.