The novel sequencing technologies enable the study of biological processes that were not available before. For example, the chromatin structure in a cell is typically studied with the use of HiC technology, in which pairs of sequencing reads link two genomic regions, thus defining the 3D organization of chromosomes in a cell. A newer alternative to HiC, called poreC, enables scientists to detect such 3D organization with longer reads, and additionally provides methylation information. Moreover, instead of pairwise contacts, poreC offers the possibility to detect higher-order interactions (multiple genomic loci interacting together), although pipelines for such statistical processing currently do not exist. The goal of the thesis will be to develop a poreC pipeline that takes these higher-order interactions into account, and reports on the quality of the sequencing libraries using multiple biologically relevant metrics and visualizations, such as a heatmap of contacts derived from poreC sequencing reads. The final pipeline must be easy to install and easy to run for a biologist, for example, in the form of a NextFlow pipeline. The developed code will be publicly available under the MIT license.