ArticlePLoS computational biology2022
DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- TM-Loop: Transformer multi-omics hierarchical detection of chromatin loop.Scientific reports · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.Genome biology · 2026Article
- CNNKSCEC: a deep learning-based framework for chromatin loop prediction with multi-source feature integration.Frontiers in genetics · 2026Article
- Chrombus-XMBD: a graph convolution model predicting 3D-genome from chromatin features.Briefings in bioinformatics · 2025Article
- Review
- CGLoop: a neural network framework for chromatin loop prediction.BMC genomics · 2025Article
- DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.BMC bioinformatics · 2025Article
- Interpretable online network dictionary learning for inferring long-range chromatin interactions.PLoS computational biology · 2024Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.
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