Guide assignment in single-cell CRISPR screens using crispat
Pooled single-cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step in the analysis of the resulting data is the assignment of cells to gRNAs corresponding to a specific genetic intervention. However, this ste...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
September 2024
|
| In: |
Bioinformatics
Year: 2024, Volume: 40, Issue: 9, Pages: 1-6 |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btae535 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/bioinformatics/btae535 Verlag, kostenfrei, Volltext: https://academic.oup.com/bioinformatics/article/40/9/btae535/7750392?login=true 
|
| Author Notes: | Jana M. Braunger and Britta Velten |
| Summary: | Pooled single-cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step in the analysis of the resulting data is the assignment of cells to gRNAs corresponding to a specific genetic intervention. However, this step is challenging due to a lack of systematic benchmarks and accessible software to apply and compare different guide assignment strategies. To address this, we here propose crispat (CRISPR guide assignment tool), a Python package to facilitate the choice of a suitable guide assignment strategy for single-cell CRISPR screens.We demonstrate the package on four single-cell CRISPR interference screens at low multiplicity of infection from two studies, where crispat identifies strong differences in the number of assigned cells, downregulation of the target genes and number of discoveries across different guide assignment strategies, highlighting the need for a suitable guide assignment strategy to obtain optimal power in single-cell CRISPR screens.crispat is implemented in python, the source code, installation instructions and tutorials can be found at https://github.com/velten-group/crispat and it can be installed from PyPI (https://pypi.org/project/crispat/). Code to reproduce all findings in this paper is available at https://github.com/velten-group/crispat_analysis, as well as at https://zenodo.org/records/13373265. |
|---|---|
| Item Description: | Online verfügbar: 6. September 2024, Artikelversion: 13. September 2024 Gesehen am 12.02.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btae535 |