HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data.

ABSTRACT:

Motivation

High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammalian cells, which may eventually generate billions of paired-end reads with a high sequencing cost. Therefore, it will be important and helpful if we can enhance the resolutions of Hi-C data by computational methods.

Results

We developed a new computational method named HiCNN that used a 54-layer very deep convolutional neural network to enhance the resolutions of Hi-C data. The network contains both global and local residual learning with multiple speedup techniques included resulting in fast convergence. We used mean squared erro

SUBMITTER: Liu T 

PROVIDER: S-EPMC6821373 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature