Apr 20, 2020

Identifying regulatory and spatial genomic architectural elements using cell type independent machine and deep learning models

BioRxiv : the Preprint Server for Biology
L. D. MartensShamith A. Samarajiwa


Chromosomal conformation capture methods such as Hi-C enables mapping of genome-wide chromatin interactions and is a promising technology to understand the role of spatial chromatin organisation in gene regulation. However, the generation and analysis of these data sets at high resolutions remain technically challenging and costly. We developed a machine and deep learning approach to predict functionally important, highly interacting chromatin regions (HICR) and topologically associated domain (TAD) boundaries independent of Hi-C data in both normal physiological states and pathological conditions such as cancer. This approach utilises gradient boosted trees and convolutional neural networks trained on both Hi-C and histone modification epigenomic data from three different cell types. Given only epigenomic modification data these models are able to predict chromatin interactions and TAD boundaries with high accuracy. We demonstrate that our models are transferable across cell types, indicating that combinatorial histone mark signatures may be universal predictors for highly interacting chromatin regions and spatial chromatin architecture elements.

  • References
  • Citations


  • We're still populating references for this paper, please check back later.
  • References
  • Citations


  • This paper may not have been cited yet.

Mentioned in this Paper

Video Media
Peer-reviewed Scientific Journal
Molecular Biology
Public Health Domain

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Computer Graphics Forum : Journal of the European Association for Computer Graphics
S GratzlMarc Streit
IEEE Computer Graphics and Applications
Bongshin LeeSheelagh Carpendale
IEEE Transactions on Visualization and Computer Graphics
Trevor HoganEva Hornecker
BMJ : British Medical Journal
Zosia Kmietowicz
IEEE Computer Graphics and Applications
Kwan-Liu MaH-N Kostis
© 2020 Meta ULC. All rights reserved