Hicberg : Prediction of omics signals from repeated elements
Date:
Repeated genomic elements (like retrotransposons) are poorly mapped by standard NGS pipelines, resulting in a significant loss of information regarding biological functions and genomic structures. To overcome this, we developed Hicberg, an algorithm that uses statistical inference trained on unambiguous genome regions to predict the positions of reads from these repeated sequences. Hicberg successfully reconstructs complete genomic tracks from various paired-end omics data, including Hi-C and ChIP-seq, significantly improving data completeness and interpretability.
Applying Hicberg to Saccharomyces cerevisiae led to key discoveries in genome organization. The analysis showed that certain retrotransposons influence the positioning of cohesin (essential for chromatin loops) and identified these sequences as contact hot points for the yeast 2-micron episomal molecule. This work demonstrates Hicberg’s power to uncover novel molecular relationships, offering a more comprehensive view of genome plasticity. The method is broadly applicable, allowing researchers to revisit existing public datasets to unveil previously overlooked features.
See poster 157