De novo identification of replication-timing domains in the human genome by deep learning

Motivation: The de novo identification of the initiation and termination zones—regions that replicate earlier or later than their upstream and downstream neighbours, respectively—remains a key challenge in DNA replication. Results: Building on advances in deep learning, we developed a novel hybrid architecture combining a pre-trained, deep neural network and a hidden Markov model (DNN-HMM) for the de novo identification of replication domains using replication timing profiles. Our results demonstrate that DNN-HMM can significantly outperform strong, discriminatively trained Gaussian mixture model–HMM (GMM-HMM) systems and other six reported methods that can be applied to this challenge. We applied our trained DNN-HMM to identify distinct replication domain types, namely the early replication domain (ERD), the down transition zone (DTZ), the late replication domain (LRD) and the up transition zone (UTZ), using newly replicated DNA sequencing (Repli-Seq) data across 15 human c...

• Type: Journal
• Year: 2016
• Where: BIOINFORMATICS
• Authors: Feng Liu, Chao Ren, Hao Li, Pingkun Zhou, Xiaochen Bo, Wenjie Shu
• Keywords: BIOINFORMATICS 2016