HiCNN2: Enhancing the resolution of Hi-C data using an ensemble of convolutional neural networks

Tong Liu, Zheng Wang

Research output: Contribution to journalArticle

Abstract

We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coeffcients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.

Original languageEnglish (US)
Article number862
JournalGenes
Volume10
Issue number11
DOIs
StatePublished - Nov 2019

Keywords

  • 3D genome
  • Convolutional networks
  • Hi-C
  • Resolution enhancement
  • Super-resolution

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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