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Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [CRISPR]

ABSTRACT: Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [CRISPR]

PROVIDER: PRJNA1136903 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR29863104.fastq.gz	Fastqsanger.gz
	SRR29863105.fastq.gz	Fastqsanger.gz
	SRR29863106.fastq.gz	Fastqsanger.gz
	SRR29863107.fastq.gz	Fastqsanger.gz
	SRR29863108.fastq.gz	Fastqsanger.gz

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Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [CRISPR]

Project description:Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.

2024-10-08 | GSE272454 | GEO

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [scRNA-seq]

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Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [scRNA-seq]

Project description:Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [scRNA-seq]

| PRJNA1137951 | ENA

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER.

Project description:Single-cell RNA sequencing CRISPR (perturb-seq) screens enable high-throughput investigation of the genome, allowing for characterization of thousands of genomic perturbations on gene expression. Ambient gRNAs, which are contaminating gRNAs, are a major source of noise in perturb-seq experiments because they result in an excess of false-positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNAs in perturb-seq screens. We use these datasets to develop CRISPR Library Evaluation and Ambient Noise Suppression for Enhanced single-cell RNA-seq (CLEANSER), a mixture model that filters ambient gRNAs. CLEANSER includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy across multiple screen formats.

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Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER.

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Amplicon DNA-sequencing of time series of pooled-CRISPR knock-out screens of cell lines using a dual-guide CRISPR library

Project description:This project aims to compare gRNA design algorithms and single- versus dual-targeting using a benchmark CRISPR-knockout library composed of gRNAs targeting essential and non-essential genes.

2025-02-12 | E-MTAB-13981 | biostudies-arrayexpress

Pooled CRISPR screening with single-cell transcriptome read-out

Project description:We combined CRISPR genome editing with single-cell RNA sequencing to assess complex phenotypes in pooled cellular screens. Our method for CRISPR droplet sequencing (CROP-seq) comprises four key components: a gRNA vector that makes individual gRNAs detectable in single-cell transcriptomes, a high-throughput assay for single-cell RNA-seq, a computational pipeline for assigning single-cell transcriptomes to gRNAs, and a bioinformatic method for analyzing and interpreting gRNA-induced transcriptional profiles. CROP-seq allowed us to link gRNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow. Additional information are available from the CROP-seq website http://crop-seq.computational-epigenetics.org

2017-01-18 | GSE92872 | GEO

Amplicon DNA-sequencing of time series of pooled-CRISPR knock-out screens of colon cancer cell lines

Project description:This project aims to compare gRNA design algorithms using a benchmark CRISPR-knockout library composed of gRNAs targeting essential and non-essential genes.

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Amplicon DNA-sequencing of time series of pooled-CRISPR knock-out screens of the HT29 colon cancer cell line

Project description:This project also aims to compare gRNA design algorithms using a benchmark CRISPR-knockout library composed of gRNAs targeting essential and non-essential genes.

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