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Enhancing Hi-C resolution data with computational construction of three-dimensional chromatin...

Angel, J.C., B.Sc. (Columbia University, New York Genome Center) El Amraoui, N., M.Sc. (New York Genome Center) Gürsoy, G., Ph.D. (Columbia University, New York Genome Center)

Poster # 19

While Hi-C has emerged as a pivotal technique to study the three-dimensional genome organization, its inherent shortcomings, such as high sequencing cost, compromise its resolution. This restricts a comprehensive analysis, resulting in the loss of potential biological insights such as detection of gene-enhancer interactions. Here we introduce a novel and efficient algorithm that utilizes importance sampling to transform low-resolution interaction frequencies into high-resolution ensembles of three-dimensional chromatin chains. This is achieved by generating chromatin chains one monomer at a time by ensuring that the imposed physical constraints from chromatin interactions are fully satisfied. We demonstrate the efficacy of our method in reconstructing high-resolution Hi-C data from as few as 1% of the original Hi-C matrix interactions. Moreover, our algorithm demonstrates a superior performance over existing deep learning methods, achieving over 95% of accuracy in reconstructing high-resolution chromatin maps, thus providing a cost-effective and efficient solution to study the intricacies of genome organization and the processes it influences.

LIGHTNING TALK - 2023 MidAtlantic Bioinformatics Conference

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