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Fig. 8 | Genome Biology

Fig. 8

From: Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Fig. 8

Predicted chromatin interactions in CLL samples using GM12878 Hi-C model. a Summary of the predicted chromatin interactions in the 84 CLL samples and the differential chromatin interactions between uCLL and mCLL samples. b Conservation analysis of predicted chromatin interactions in the CLL samples. All pairs, all possible pairs used for prediction; y-axis, the proportion of total chromatin interactions that can be found in a particular number of samples. c Uniqueness analysis of open chromatin regions that overlap with Hi-C peaks from GM12878 cells in the CLL samples. All, all open chromatin regions; y-axis, the proportion of total chromatin interactions that can be found in a particular number of samples. d Distribution of differential Hi-C chromatin interactions based on whether both anchors (both), one anchor (one-side), or neither anchors (neither) showed the same level of differences between uCLL and mCLL samples as the associated chromatin interaction. e Association of differences in chromatin interactions between uCLL and mCLL samples with differentially expressed genes identified from a set of microarray samples. IFC, the fold change of the average number of chromatin interactions observed at the gene promoter in uCLL samples over that in mCLL samples. p-values were calculated using the Kruskal-Wallis test. f, g Examples of genes, ZBTB20 and LPL, whose different connectivity are associated with differences in distal regions. The red bars and curves indicate significantly different open chromatin regions and chromatin interactions based on Fisher’s exact test

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