ChromaFold predicts the 3D contact map from single-cell chromatin accessibility
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1035004 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26639283_1.fastq.gz | Fastqsanger.gz | |||
| SRR26639283_2.fastq.gz | Fastqsanger.gz | |||
| SRR26639284_1.fastq.gz | Fastqsanger.gz | |||
| SRR26639284_2.fastq.gz | Fastqsanger.gz | |||
| SRR26639285_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 12 |
Similar Datasets
Project description:The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of celltype- specific interactions in settings where 3C-based assays are infeasible.
2024-08-31 | GSE246859 | GEO
Project description:The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible.
| S-EPMC10402156 | biostudies-literature
Project description:Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that a logic of cis-acting DNA sequence features can predict the majority of chromatin accessibility at high spatial resolution. We develop a new type of high-dimensional machine learning model, the Cooperative Chromatin Model (CCM), that is capable of predicting a large fraction of genome-widepromoters chromatin accessibility at basepair-resolution in a range of human and mouse cell types from DNA sequence alone. We confirm that a CCM accurately predicts chromatin accessibility, even of a vast array of synthetic DNA sequences, with a novel CrispR-based method of highly efficient site-specific DNA library integration. CCMs are directly interpretable and reveal that a logic based on local, non-specific cooperation, largely among pioneer TFs, is sufficient to predict a large fraction of cellular chromatin accessibility in a wide variety of cell types.
2016-07-20 | GSE80105 | GEO
Project description:This project is based on visualizing the chemical and microbiological composition of individuals of Colostethus panamensis (Dendrobatidae), through the creation of a topographic map of the surface of their skin in 3D
2017-03-06 | MSV000080602 | MassIVE
Project description:3D structure of chromatin is thought to be critical for the regulation of gene expression during development. Here we employ the Micro-C assay to achieve 100 bp resolution for the genome organization of Drosophila melanogaster throughout the first half of embryogenesis. The resulting contact maps enable the identification of fine-scaled structures such as individual loops and boundaries delineating TADs. We observe that 3D structures form prior to zygotic genome activation in precellular embryos, and many of these organizational features persist during successive mitotic cycles. 3D structures are classified through the use of 149 ChIP-seq datasets. We present evidence for specialized elements associated with housekeeping genes that are enriched for BEAF-32 but not CTCF. We also characterize a distinct class of elements associated with developmental gene regulation that are enriched with GAF and Zld binding. This binding is maintained during mitotic cycles and exhibits evolutionary conservation of both sequence and 3D structures. In sum, this work provides a comprehensive genome-wide characterization at unprecedented resolution of the role of 3D chromatin organization in gene regulation during development. We propose that the 3D organization of the pre-cellular embryo facilitates deployment of the developmental control genes defining the Drosophila fate map.
2024-12-06 | GSE265818 | GEO
Project description:Synergistic model of chromatin predicts Dnase-I accessibility
| PRJNA318029 | ENA
Project description:Hi-C contact map of the Jurkat T cell genome
| PRJNA506997 | ENA
Project description:3D Chromatin Structures Foreshadow the Drosophila Fate Map
| PRJNA1104257 | ENA
Project description:This project is based on visualizing the chemical and microbiological composition of individuals of Colostethus panamensis (Dendrobatidae), through the creation of a topographic map of the surface of their skin in 3D
2017-03-06 | MSV000080602 | GNPS
Project description:In this study, we used a forebrain 3D spheroid model to map chromatin accessibility and gene expression changes throughout a dense time course spanning 20 months of neuronal and glial maturation.
2020-01-23 | GSE132403 | GEO