AIMC Topic: Chromatin
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Enhancing R-loop prediction with high-throughput sequencing data.
NAR genomics and bioinformatics
Jun 11, 2025
R-loops are three-stranded RNA and DNA hybrid structures that often occur in the genome and play important roles in a variety of cellular processes from bacteria to mammals. Sequencing methods profiling R-loops genome-wide have revealed that they can...
Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex.
Cell genomics
May 21, 2025
Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Sp...
TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.
Nature communications
Apr 16, 2025
It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale mult...
High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH.
Cell
Apr 15, 2025
Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or r...
Pathogenomic fingerprinting to identify associations between tumor morphology and epigenetic states.
European journal of cancer (Oxford, England : 1990)
Apr 14, 2025
INTRODUCTION: Measuring the chromatin state of a tumor provides a powerful map of its epigenetic commitments; however, as these are generally bulk measurements, it has not yet been possible to connect changes in chromatin accessibility to the patholo...
CGLoop: a neural network framework for chromatin loop prediction.
BMC genomics
Apr 5, 2025
BACKGROUND: Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patter...
DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.
BMC bioinformatics
Apr 1, 2025
BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainst...
scCorrect: Cross-modality label transfer from scRNA-seq to scATAC-seq using domain adaptation.
Analytical biochemistry
Mar 27, 2025
Cell type annotation in single-cell chromatin accessibility sequencing (scATAC-seq) is crucial for enabling researchers to identify subpopulations of cells associated with specific diseases, elucidate gene regulatory networks, and discover markers in...
Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.
Cell genomics
Feb 27, 2025
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...
A multi-modal transformer for cell type-agnostic regulatory predictions.
Cell genomics
Jan 29, 2025
Sequence-based deep learning models have emerged as powerful tools for deciphering the cis-regulatory grammar of the human genome but cannot generalize to unobserved cellular contexts. Here, we present EpiBERT, a multi-modal transformer that learns g...
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