Change point statistics is lively and effective in various fields, particularly next generation sequencing and identification of biomarkers. Here, we show its application for studying the nuclear organization with emerging Hi-C data on physical interactions between and within chromosomes. Starting from a commonly used wavelet change point for detecting changes in Poisson observations, we explore different flavours particularly suitable for mapping genomic sequence patterns into spatial information. This algorithm supports advanced comparison of different cells, bridging the gap between organismic and cellular levels, with one genome and many cellular functions and shapes. Moreover, our efforts suggest a new class of algorithms will be needed in the future to implement multi parameter evidence synthesis from different sources of (spatially or sequentially distributed) omics.