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ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

ABSTRACT: ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

PROVIDER: PRJNA1035004 | ENA |

REPOSITORIES: ENA

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			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

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ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

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.

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Synergistic model of chromatin predicts Dnase-I accessibility

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.

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Panama Rocket Frog 3D Map

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

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Synergistic model of chromatin predicts Dnase-I accessibility

Project description:Synergistic model of chromatin predicts Dnase-I accessibility

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Hi-C contact map of the Jurkat T cell genome

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Chromatin accessibility dynamics in a model of human forebrain development

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.

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OPUS-X: an open-source toolkit for protein torsion angles, secondary structure, solvent accessibility, contact map predictions and 3D folding.

Project description:MotivationThe development of an open-source platform to predict protein 1D features and 3D structure is an important task. In this paper, we report an open-source toolkit for protein 3D structure modeling, named OPUS-X. It contains three modules: OPUS-TASS2, which predicts protein torsion angles, secondary structure and solvent accessibility; OPUS-Contact, which measures the distance and orientation information between different residue pairs; and OPUS-Fold2, which uses the constraints derived from the first two modules to guide folding.ResultsOPUS-TASS2 is an upgraded version of our previous method OPUS-TASS. OPUS-TASS2 integrates protein global structure information and significantly outperforms OPUS-TASS. OPUS-Contact combines multiple raw co-evolutionary features with protein 1D features predicted by OPUS-TASS2, and delivers better results than the open-source state-of-the-art method trRosetta. OPUS-Fold2 is a complementary version of our previous method OPUS-Fold. OPUS-Fold2 is a gradient-based protein folding framework based on the differentiable energy terms in opposed to OPUS-Fold that is a sampling-based method used to deal with the non-differentiable terms. OPUS-Fold2 exhibits comparable performance to the Rosetta folding protocol in trRosetta when using identical inputs. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to any source-code-level modification.Availabilityand implementationThe code and pre-trained models of OPUS-X can be downloaded from https://github.com/OPUS-MaLab/opus_x.Supplementary informationSupplementary data are available at Bioinformatics online.

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Hi-C contact map of the Jurkat T cell genome

Project description:We performed Hi-C on untreated Jurkat T cells growing in culture. We obtained contact maps with a final resolution of 5 kb where Topologically Associated Domains (TADs) are clearly visible. We identified 3D sub-compartments, characterized by enriched Hi-C contacts within themselves compared to contacts with other compartments. Our results suggest that the A compartment, associated with ongoing transcription, consists of two distinct sub-compartments called A1 and A2; and that the B compartment, associated with lack of transcription, consists of three sub-compartments called B1, B2 and B3. Genes in the A1 and A2 compartments are typically expressed at the same level, but chromatin marks associated with transcription, such as H3K4me2 or H3K27ac, are typically more abundant in A1 than in A2. The B1, B2 and B3 sub-compartments correspond to Polycomb, HP1 and lamina-associated chromatin, which were previously identified types of silent chromatin. Overall, these results suggest that spatial neighborhoods of the Jurkat nucleus correspond to homogeneous chromatin types, and that transcriptionally active regions are in fact two distinct types.

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Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data

Project description:Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data

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