ArticleQuantitative biology (Beijing, China)2022
Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens.
Article in Quantitative biology (Beijing, China), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Orobas: A computational approach for scoring and analysis of quantitative chemical-genetic interactions from CRISPR-Cas9 screens.STAR protocols · 2026Article
- Genome scale CRISPRi reveals both shared and strain-specific vulnerabilities in genetically diverse drug-resistant strains of Mycobacterium tuberculosis.Nature communications · 2026Article
- The Hsp40 cochaperone DNAJC7 regulates polyglutamine aggregation and exhibits context-dependent effects on polyglycine aggregation.The Journal of biological chemistry · 2026Article
- nf-core/crisprseq: a versatile pipeline for comprehensive analysis of CRISPR gene editing and screening assays.NAR genomics and bioinformatics · 2026Article
- Revealing genetic drivers of ovarian cancer and chemoresistance: insights from whole-genome CRISPR-knockout library screens.Cellular oncology (Dordrecht, Netherlands) · 2025Review
- Perturbomics: CRISPR-Cas screening-based functional genomics approach for drug target discovery.Experimental & molecular medicine · 2025Review
- Transforming cancer treatment: integrating patient-derived organoids and CRISPR screening for precision medicine.Frontiers in pharmacology · 2025Review
- A scalable, high-throughput neural development platform identifies shared impact of ASD genes on cell fate and differentiation.bioRxiv : the preprint server for biology · 2024Article
- The rise and future of CRISPR-based approaches for high-throughput genomics.FEMS microbiology reviews · 2024Review
- Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.Biomolecules · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Background: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects. Results: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens. Conclusion: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.
Indexed as
Identifiers
36937794PMC10019185What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.