A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks.

ABSTRACT: The organisation of the genome in nuclear space is an important frontier of biology. Chromosome conformation capture methods such as Hi-C and Micro-C produce genome-wide chromatin contact maps that provide rich data containing quantitative and qualitative information about genome architecture. Most conventional approaches to genome-wide chromosome conformation capture data are limited to the analysis of pre-defined features, and may therefore miss important biological information. One constraint is that biologically important features can be masked by high levels of technical noise in the data. Here we introduce a replicate-based method for deep learning from chromatin conformation contact maps. Using a Siamese network configuration our approach learns to distinguish technical noise from b

SUBMITTER: Al-Jibury E 

PROVIDER: S-EPMC10435498 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature