HiCPlus: Resolution Enhancement of Hi-C interaction heatmap

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/HiCPlus

Download data

• Downloaded 2,128 times
• Download rankings, all-time:
  • Site-wide: 5,971
  • In bioinformatics: 733
• Year to date:
  • Site-wide: 63,819
• Since beginning of last month:
  • Site-wide: 92,291

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide