AIMC Topic: Chromatin

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The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.

Nature communications
Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expressio...

A multi-omic single-cell landscape reveals transcription and epigenetic regulatory features of t(8;21) AML.

Journal of translational medicine
BACKGROUND: Comprehensive analysis of single-cell transcriptome and chromatin accessibility will contribute to interpret the heterogeneity of acute myeloid leukemia (AML). We hypothesize that integrating single-cell transcriptomic and chromatin acces...

Enhancing R-loop prediction with high-throughput sequencing data.

NAR genomics and bioinformatics
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
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
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
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)
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
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
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
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...