Abstract
Throughout evolution, genomes have acquired various repetitive elements, such as transposons, ribosomal DNA, duplicated genes, and tandem repeats. These sequences pose significant challenges to current high-throughput sequencing pipelines, which generate short reads that cannot be unambiguously mapped to reference genomes. We present an innovative algorithm, Hicberg, designed to leverage reads typically discarded by standard pipelines. Hicberg employs statistical inference coupled with probability distribution computation to accurately infer missing 3D contacts of repetitive elements within a genome from paired omic data, such as Hi-C data. Our results demonstrate that Hicberg provides new insights into the potential impact of repetitive elements on the spatial organization of genomes. Notably, we observed the positioning of molecular motors that create chromosomal loops associated with certain transposons in yeast. These findings underscore the importance of an integrative approach to better understand genomic complexity and pave the way for deeper explorations of repeated elements.