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CRISPR and crRNAs

CRISPR and crRNAs

Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...

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Related Experiment Video

Updated: Oct 14, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with

Lingfei Wang^1,2,3

¹Broad Institute of MIT and Harvard, Cambridge, MA, USA. lwang55@mgh.harvard.edu.

Nature Communications

|November 5, 2021

Summary

Normalisr is a new framework for single-cell RNA sequencing (scRNA-seq) data analysis. It improves gene regulation studies by unifying normalization and statistical testing for differential expression, co-expression, and CRISPR screens.

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Last Updated: Oct 14, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

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Published on: September 4, 2019

Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays

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HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries

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Published on: March 31, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) offers powerful insights into gene regulation.
Technical variations and data sparsity pose challenges for scRNA-seq analysis.
Statistical association testing remains difficult in the scRNA-seq context.

Purpose of the Study:

To introduce Normalisr, a novel framework for normalization and statistical association testing in scRNA-seq.
To unify differential expression, co-expression, and CRISPR screen analyses using linear models.
To address limitations in current scRNA-seq statistical methodologies.

Main Methods:

Normalisr employs linear models to integrate various scRNA-seq analyses.
It systematically detects and removes nonlinear confounders related to library size.
The framework is validated across multiple scRNA-seq protocols and experimental conditions.

Main Results:

Normalisr demonstrates high sensitivity, specificity, and speed in scRNA-seq analysis.
It achieves unbiased p-value estimation and generalizability across datasets.
The framework successfully reconstructs gene regulatory networks from large-scale CRISPRi screens.
Normalisr recovers accurate gene-level co-expression networks from conventional scRNA-seq data.

Conclusions:

Normalisr provides a robust and scalable solution for scRNA-seq data analysis.
It enhances the study of gene regulation, including differential expression, co-expression, and CRISPR screens.
The framework's ability to handle technical variations and sparsity improves the reliability of biological insights.