Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model

Motivation High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem...

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Published in:Bioinformatics (Oxford, England) Vol. 40; no. Supplement_1; pp. i471 - i480
Main Authors: Fang, Tangqi, Liu, Yifeng, Woicik, Addie, Lu, Minsi, Jha, Anupama, Wang, Xiao, Li, Gang, Hristov, Borislav, Liu, Zixuan, Xu, Hanwen, Noble, William S, Wang, Sheng
Format: Journal Article
Language:English
Published: England Oxford University Press 28.06.2024
Oxford Publishing Limited (England)
Subjects:
Algorithms
Availability
Biological effects
Chromatin
Chromatin - chemistry
Chromatin - metabolism
Chromosomes
Computational Biology - methods
Humans
Image enhancement
Machine Learning
Nucleotide sequence
Probability learning
Software
Source code
ISSN:1367-4803, 1367-4811, 1367-4811
Online Access:Get full text
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Summary:Motivation High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. Results We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Availability and implementation Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btae211