Using statistical profiling to decipher hidden chromatin contacts resulting from repeated sequences

Genomes are not static entities; they evolve and acquire various repeated elements Repetitive genomic elements, crucial yet challenging for NGS analysis, are addressed by Hicberg, an algorithm employing statistical inference to accurately map their reads in diverse sequencing data and, in yeast, reveals novel interactions between retrotransposons and the enigmatic 2-micron plasmid. Applied to yeast, Hicberg further unveils the influence of retrotransposons on chromatin organization and identifies other new genomic interactions, offering a more comprehensive understanding of genome structure and dynamics.