RT Journal Article
SR Electronic
T1 Predicting A/B compartments from histone modifications using deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.04.19.488754
DO 10.1101/2022.04.19.488754
A1 Suchen Zheng
A1 Nitya Thakkar
A1 Hannah L. Harris
A1 Megan Zhang
A1 Susanna Liu
A1 Mark Gerstein
A1 Erez Lieberman-Aiden
A1 M. Jordan Rowley
A1 William Stafford Noble
A1 Gamze Gürsoy
A1 Ritambhara Singh
YR 2022
UL http://biorxiv.org/content/early/2022/04/19/2022.04.19.488754.abstract
AB Genomes in 3D are folded into organizational units that can influence critical biological functions. In particular, the organization of chromatin into A and B compartments segregates its active regions from the inactive regions. Compartments, evident in Hi-C contact matrices, have been used to describe cell-type specific changes in A/B organization. However, obtaining Hi-C data for all cell and tissue types of interest is prohibitively expensive, which has limited the widespread consideration of compartment status. We present a prediction tool called Compartment prediction using Recurrent Neural Network (CoRNN) that models the relationship between the compartmental organization of the genome and histone modification enrichment. Our model predicts A/B compartments, in a cross-cell type setting, with an average area under the ROC score of 90.9%. Our cell type specific compartment predictions show high overlap with known functional elements. We investigate our predictions by systematically removing combinations of histone marks and find that H3K27ac and H3K36me3 are the most predictive marks. We then perform a detailed investigation of loci where compartment status cannot be accurately predicted from these marks.These regions represent chromatin with ambiguous compartmental status, likely due to variations in status within the population of cells. As such these ambiguous loci also show highly variable compartmental status between biological replicates in the same GM12878 cell type. Our software and trained model are publicly available at https://github.com/rsinghlab/CoRNN.Competing Interest StatementThe authors have declared no competing interest.