Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE116297 | GEO | 2018/12/20

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks.

Project description:Here, we present Perturb-ATAC, a method that combines multiplexed CRISPR interference or knockout with genome-wide chromatin accessibility profiling in single cells based on the simultaneous detection of CRISPR guide RNAs and open chromatin sites by assay of transposase-accessible chromatin with sequencing (ATAC-seq). We applied Perturb-ATAC to transcription factors (TFs), chromatin-modifying factors, and noncoding RNAs (ncRNAs) in ?4,300 single cells, encompassing more than 63 genotype-phenotype relationships. Perturb-ATAC in human B lymphocytes uncovered regulators of chromatin accessibility, TF occupancy, and nucleosome positioning and identified a hierarchy of TFs that govern B cell state, variation, and disease-associated cis-regulatory elements. Perturb-ATAC in primary human epidermal cells revealed three sequential modules of cis-elements that specify keratinocyte fate. Combinatorial deletion of all pairs of these TFs uncovered their epistatic relationships and highlighted genomic co-localization as a basis for synergistic interactions. Thus, Perturb-ATAC is a powerful strategy to dissect gene regulatory networks in development and disease.

| S-EPMC6329648 | biostudies-literature

Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks

Project description:Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks

| PRJNA478043 | ENA

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [scATAC-seq]

Project description:Here we present Perturb-ATAC, a method which combines multiplexed CRISPR interference or knockout with genome-wide chromatin accessibility profiling in single cells, based on the simultaneous detection of CRISPR guide RNAs and open chromatin sites by Assay of Transposase-accessible Chromatin with sequencing (ATAC-seq). We applied Perturb-ATAC to transcription factors (TFs), chromatin-modifying factors, and noncoding RNAs (ncRNAs) in ~3,700 single cells, encompassing more than 75 unique genotype-phenotype relationships.

2018-12-20 | GSE116248 | GEO

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Perturb-ATAC (ATAC)]

2018-12-20 | GSE116249 | GEO

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Bulk ATAC-seq]

2018-12-20 | GSE116247 | GEO

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Perturb-ATAC (GBC/sgRNA)]

2018-12-20 | GSE116285 | GEO

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.

Project description:Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

| S-EPMC7223477 | biostudies-literature

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [scATAC-seq]

Project description:Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [scATAC-seq]

| PRJNA478037 | ENA

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Bulk ATAC-seq]

Project description:Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Bulk ATAC-seq]

| PRJNA478036 | ENA

Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Perturb-ATAC (ATAC)]

Project description:Coupled single-cell epigenome editing and profiling reveals causal gene regulatory networks [Perturb-ATAC (ATAC)]

| PRJNA478045 | ENA