RT Journal Article
SR Electronic
T1 HiCPlus: Resolution Enhancement of Hi-C interaction heatmap
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 112631
DO 10.1101/112631
A1 Yan Zhang
A1 Lin An
A1 Ming Hu
A1 Jijun Tang
A1 Feng Yue
YR 2017
UL http://biorxiv.org/content/early/2017/03/01/112631.abstract
AB Motivation The Hi-C technology has become an efficient tool to measure the spatial organization of the genome. With the recent advance of 1Kb resolution Hi-C experiment, some of the essential regulatory features have been uncovered. However, most available Hi-C datasets are in coarse-resolution due to the extremely high cost for generating high-resolution data. Therefore, a computational method to maximum the usage of the current available Hi-C data is urgently desired.Results Inspired by the super-resolution image technique, we develop a computational approach to impute the high-resolution Hi-C data from low-resolution Hi-C data using the deep convolutional neural network. We hypothesize that the Hi-C interaction heatmap contains the repeating features, and develop an end-to-end framework to map these features from low-resolution Hi-C heatmap to high-resolution Hi-C heatmap at the feature level. Our approach successfully reconstructs the high-resolution Hi-C interaction map from the low-resolution counterpart, which also proves that the Hi-C interaction matrix is a combination of the regional features. Besides, our approach is highly expandable, and we can also increase prediction accuracy by incorporating ChIA-PET data.Availability Source code is publicly available at https://github.com/zhangyan32/HiCPlusContact jtang{at}cse.sc.edu, fyue{at}hmc.psu.edu