Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Cell type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

ABSTRACT: Cell type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

PROVIDER: PRJNA893608 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR22033836_1.fastq.gz	Fastqsanger.gz
	SRR22033836_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 2 of 2

Similar Datasets

Cell type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

Project description:Experimental methods for measuring 3D chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a deep neural network model that performs de novoprediction of cell type–specific chromatin organization using as inputs the DNA sequence and two cell type–specific genomic features — chromatin accessibility and CTCF binding. C.Origami predicts chromatin organization within a 2 mega-base window and enables in silico experiments to examine the impact of genetic perturbations on chromatin interactions in pathologies such as cancer. We assess how individual DNA elements contribute to the organization of 3D chromatin organization and identify a compendium of cell type–specific trans-regulators across multiple cell types. We demonstrate that cell type–specific in silico genetic perturbation and screening, enabled by C.Origami, can be used to systematically discover chromatin regulatory mechanisms in both normal and disease-related biological systems.

2022-10-31 | GSE216430 | GEO

Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening.

Project description:Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features-CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans-acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.

| S-EPMC10329734 | biostudies-literature

Prediction, screening and characterisation of novel bioactive, tetra-peptide matrikines 3

Project description:Small peptides with amino acid sequences similar to native skin proteins can beneficially affect clinical appearance (wrinkles) and architecture (collagen and elastic fibre deposition and epidermal thickness). However, the discovery of new cosmetic peptides has not been underpinned by any guiding hypothesis. As endogenous extracellular matrix (ECM)-derived peptides (matrikines) produced during tissue remodelling can influence cell activity, we hypothesised that protease cleavage site prediction can identify putative novel matrikines. This RNA-seq data is the in vivo test part of an in silico to in vivo discovery pipeline, which enables the prediction and characterisation of peptide matrikines with therapeutic potential. We use this pipeline to identify two novel ECM peptides (GPKG and LSVD) which, in combination, act in vitro to enhance the transcription of ECM organisation and cell proliferation genes and in vivo to promote epithelial and dermal remodelling. This pipeline approach can both identify new matrikines and provide insights into the mechanisms underpinning tissue repair.

2023-11-15 | E-MTAB-12704 | biostudies-arrayexpress

Prediction, screening and characterisation of novel bioactive, tetra-peptide matrikines 1

Project description:Small peptides with amino acid sequences similar to native skin proteins can beneficially affect clinical appearance (wrinkles) and architecture (collagen and elastic fibre deposition and epidermal thickness). However, the discovery of new cosmetic peptides has not been underpinned by any guiding hypothesis. As endogenous extracellular matrix (ECM)-derived peptides (matrikines) produced during tissue remodelling can influence cell activity, we hypothesised that protease cleavage site prediction can identify putative novel matrikines. This RNA-seq data is the in vitro testing part of an in silico to in vivo discovery pipeline, which enables the prediction and characterisation of peptide matrikines with therapeutic potential. We use this pipeline to identify two novel ECM peptides (GPKG and LSVD) which, in combination, act in vitro to enhance the transcription of ECM organisation and cell proliferation genes and in vivo to promote epithelial and dermal remodelling. This pipeline approach can both identify new matrikines and provide insights into the mechanisms underpinning tissue repair.

2023-12-15 | E-MTAB-12711 | biostudies-arrayexpress

Prediction, screening and characterisation of novel bioactive, tetra-peptide matrikines 2

Project description:Small peptides with amino acid sequences similar to native skin proteins can beneficially affect clinical appearance (wrinkles) and architecture (collagen and elastic fibre deposition and epidermal thickness). However, the discovery of new cosmetic peptides has not been underpinned by any guiding hypothesis. As endogenous extracellular matrix (ECM)-derived peptides (matrikines) produced during tissue remodelling can influence cell activity, we hypothesised that protease cleavage site prediction can identify putative novel matrikines. This RNA-seq data is the in vitro testing part of an in silico to in vivo discovery pipeline, which enables the prediction and characterisation of peptide matrikines with therapeutic potential. We use this pipeline to identify two novel ECM peptides (GPKG and LSVD) which, in combination, act in vitro to enhance the transcription of ECM organisation and cell proliferation genes and in vivo to promote epithelial and dermal remodelling. This pipeline approach can both identify new matrikines and provide insights into the mechanisms underpinning tissue repair.

2023-12-15 | E-MTAB-12710 | biostudies-arrayexpress

in silico drug target prediction for melanoma

Project description:The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available and in-house large scale transcriptomic data, metabolic models for melanoma were reconstructed, enabling the prediction of 28 candidate drugs and estimating their respective efficacy.

| EGAS00001006463 | EGA

Validation of human microRNA target pathways enables evaluation of target prediction tools

Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools

| PRJNA557590 | ENA

In silico prediction of SARS protease inhibitors by virtual high throughput screening.

Project description:A structure-based in silico virtual drug discovery procedure was assessed with severe acute respiratory syndrome coronavirus main protease serving as a case study. First, potential compounds were extracted from protein-ligand complexes selected from Protein Data Bank database based on structural similarity to the target protein. Later, the set of compounds was ranked by docking scores using a Electronic High-Throughput Screening flexible docking procedure to select the most promising molecules. The set of best performing compounds was then used for similarity search over the 1 million entries in the Ligand.Info Meta-Database. Selected molecules having close structural relationship to a 2-methyl-2,4-pentanediol may provide candidate lead compounds toward the development of novel allosteric severe acute respiratory syndrome protease inhibitors.

| S-EPMC7188353 | biostudies-literature

Validation of human microRNA target pathways enables evaluation of target prediction tools [miRNA]

Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools [miRNA]

| PRJNA557593 | ENA

Validation of human microRNA target pathways enables evaluation of target prediction tools [mRNA]

Project description:Validation of human microRNA target pathways enables evaluation of target prediction tools [mRNA]

| PRJNA557595 | ENA