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Hi-C Resolution Enhancement with Genome Sequence Data

Kriukov, D.; Koritskiy, N.; Kozlovskii, I.; Zaretckii, M.; Bazarevich, M.; Zybin, M.; Khrameeva, E.

2021-10-26 systems biology
10.1101/2021.10.25.465745 bioRxiv
Show abstract

The increasing interest in chromatin conformation inside the nucleus and the availability of genome-wide experimental data make it possible to develop computational methods that can increase the quality of the data and thus overcome the limitations of high experimental costs. Here we develop a deep-learning approach for increasing Hi-C data resolution by appending additional information about genome sequence. In this approach, we utilize two different deep-learning algorithms: the image-to-image model, which enhances Hi-C resolution by itself, and the sequence-to-image model, which uses additional information about the underlying genome sequence for further resolution improvement. Both models are combined with the simple head model that provides a more accurate enhancement of initial low-resolution Hi-C data. The code is freely available in a GitHub repository: https://github.com/koritsky/DL2021_HI-C.

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