# C2c: Predicting Micro-C from Hi-C

## TL;DR

This paper introduces C2c, a computational tool that predicts high-resolution Micro-C data from existing Hi-C datasets, improving the detection of chromatin loops and gene regulation features.

## Contribution

The novel contribution is a deep learning method to convert Hi-C into Micro-C data computationally, avoiding the need for expensive and technically challenging Micro-C experiments.

## Key findings

- C2c predicted Micro-C contact matrices reveal more chromatin loops than Hi-C data.
- Loops detected from predicted Micro-C better match promoter–enhancer interactions compared to Hi-C.
- Predicted Micro-C data lead to more accurate TAD-boundary detection than Hi-C.

## Abstract

Motivation: High-resolution Hi-C data, capable of detecting chromatin features below the level of Topologically Associating Domains (TADs), significantly enhance our understanding of gene regulation. Micro-C, a variant of Hi-C incorporating a micrococcal nuclease (MNase) digestion step to examine interactions between nucleosome pairs, has been developed to overcome the resolution limitations of Hi-C. However, Micro-C experiments pose greater technical challenges compared to Hi-C, owing to the need for precise MNase digestion control and higher-resolution sequencing. Therefore, developing computational methods to derive Micro-C data from existing Hi-C datasets could lead to better usage of a large amount of existing Hi-C data in the scientific community and cost savings. Results: We developed C2c (“high” or upper case C to “micro” or lower case c), a computational tool based on a residual neural network to learn the mapping between Hi-C and Micro-C contact matrices and then predict Micro-C contact matrices based on Hi-C contact matrices. Our evaluation results show that the predicted Micro-C contact matrices reveal more chromatin loops than the input Hi-C contact matrices, and more of the loops detected from predicted Micro-C match the promoter–enhancer interactions. Furthermore, we found that the mutual loops from real and predicted Micro-C better match the ChIA-PET data compared to Hi-C and real Micro-C loops, and the predicted Micro-C leads to more TAD-boundaries detected compared to the Hi-C data. The website URL of C2c can be found in the Data Availability Statement.