Fig. 12

ChIPr outperforms other 3D chromatin prediction methods. A The RAD21 25 kbp contact maps predicted by the three variants of ChIPr yield significantly higher SCC values when compared with the original contact maps than those yielded by 3Dpredictor contact maps. B The 25 kbp Hi-C contact maps predicted by the three variants of ChIPr yield significantly higher SCC values when compared with the original contact maps than those yielded by 3Dpredictor contact maps. C, D The original contact map of an 8 Mbp region in Chr9 (C) looks visually similar to its corresponding GB-ChIPr (D) prediction. E The predictions of the three variants of ChIPr, using the models trained on the IMR90 cell line data, correlate significantly better than control interactions when compared with the original Hi-C interactions for the two cell lines GM12878 and K562. F The predictions of the three variants of ChIPr, using the models trained on K562 and IMR90 cell line data, yield significantly higher Pearson correlation coefficient values than those obtained by C.Origami when comparing the 8 kbp resolution contact maps with the corresponding original ones for 23 randomly selected 2Mbp regions. G The predictions of the three variants of ChIPr, using the models trained on the H1 cell line Micro-C data (Chr1 only), correlate significantly better than control interactions when compared with the original Micro-C interactions for the remaining 22 chromosomes. H The three variants of ChIPr yield significantly higher Pearson correlation coefficient values when comparing the 5 kbp resolution observed contact maps with the original contact maps than those yielded by Orca 4 kbp contact maps for 16 selected regions in Chrs 8, 9, and 10. ****: p-value < 0.0001, Wilcoxon rank sum test. For SCC calculations in A, B, we set the smoothing window half-size h to “2” and the maximum genomic distance to include in calculations to 1.5 Mbp as used in 3Dpredictor paper. For C, D, visualization of contact maps is done using HiCPlotter [37]