MOTIVATION: Hi-C technology has been the most widely used chromosome conformation capture (3C) experiment that measures the frequency of all paired interactions in the entire genome, which is a powerful tool for studying the 3D structure of the genome. The fineness of the constructed genome structure depends on the resolution of Hi-C data. However, due to the fact that high-resolution Hi-C data require deep sequencing and thus high experimental cost, most available Hi-C data are in low-resolution. Hence, it is essential to enhance the quality of Hi-C data by developing the effective computational methods. RESULTS: In this work, we propose a novel method, so-called DFHiC, which generates the high-resolution Hi-C matrix from the low-resolution Hi-C matrix in the framework of the dilated convolutional neural network. The dilated convolution is able to effectively explore the global patterns in the overall Hi-C matrix by taking advantage of the information of the Hi-C matrix in a way of the longer genomic distance. Consequently, DFHiC can improve the resolution of the Hi-C matrix reliably and accurately. More importantly, the super-resolution Hi-C data enhanced by DFHiC is more in line with the real high-resolution Hi-C data than those done by the other existing methods, in terms of both chromatin significant interactions and identifying topologically associating domains. AVAILABILITY AND IMPLEMENTATION: https://github.com/BinWangCSU/DFHiC.

Topologically associating domains (TADs) are local chromatin interaction domains, which have been shown to play an important role in gene expression regulation. TADs were originally discovered in the investigation of 3D genome organization based on High-throughput Chromosome Conformation Capture (Hi-C) data. Continuous considerable efforts have been dedicated to developing methods for detecting TADs from Hi-C data. Different computational methods for TADs identification vary in their assumptions and criteria in calling TADs. As a consequence, the TADs called by these methods differ in their similarities and biological features they are enriched in. In this work, we performed a systematic comparison of twenty-six TAD callers. We first compared the TADs and gaps between adjacent TADs across different methods, resolutions, and sequencing depths. We then assessed the quality of TADs and TAD boundaries according to three criteria: the decay of contact frequencies over the genomic distance, enrichment and depletion of regulatory elements around TAD boundaries, and reproducibility of TADs and TAD boundaries in replicate samples. Last, due to the lack of a gold standard of TADs, we also evaluated the performance of the methods on synthetic datasets. We discussed the key principles of TAD callers, and pinpointed current situation in the detection of TADs. We provide a concise, comprehensive, and systematic framework for evaluating the performance of TAD callers, and expect our work will provide useful guidance in choosing suitable approaches for the detection and evaluation of TADs.