Recent advances in high-throughput chromosome conformation capture(Hi-C)techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures,thereby shedding light on the ...Recent advances in high-throughput chromosome conformation capture(Hi-C)techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures,thereby shedding light on the principles of genome architecture and functions.However,statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains(TADs)are still lacking.Here,we proposed a new statistical method,DiffGR,for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps.We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions.We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions.Through simulation studies,we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions.Furthermore,we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets,and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods.The DiffGR R package is published under the GNU General Public License(GPL)≥2 license and is publicly available at https://github.com/wmalab/DiffGR.展开更多
基金supported by the National Science Foundation,USA(Grant No.DBI-1751317)the National Institute of Health,USA(Grant No.R35GM133678).
文摘Recent advances in high-throughput chromosome conformation capture(Hi-C)techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures,thereby shedding light on the principles of genome architecture and functions.However,statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains(TADs)are still lacking.Here,we proposed a new statistical method,DiffGR,for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps.We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions.We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions.Through simulation studies,we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions.Furthermore,we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets,and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods.The DiffGR R package is published under the GNU General Public License(GPL)≥2 license and is publicly available at https://github.com/wmalab/DiffGR.
