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Parallel CRISPR-Cas9 screens clarify impacts of p53 on screen performance

Cite this dataset

Bowden, A Ramsay et al. (2020). Parallel CRISPR-Cas9 screens clarify impacts of p53 on screen performance [Dataset]. Dryad.


CRISPR-Cas9 genome engineering has revolutionised high-throughput functional genomic screens. However, recent work has raised concerns regarding the performance of CRISPR-Cas9 screens using TP53 wild-type human cells due to a p53-mediated DNA damage response (DDR) limiting the efficiency of generating viable edited cells. To directly assess the impact of cellular p53 status on CRISPR-Cas9 screen performance, we carried out parallel CRISPR-Cas9 screens in wild-type and TP53 knockout human retinal pigment epithelial cells using a focused dual guide RNA library targeting 852 DDR-associated genes. Our work demonstrates that although functional p53 status negatively affects identification of significantly depleted genes, optimal screen design can nevertheless enable robust screen performance. Through analysis of our own and published screen data, we highlight key factors for successful screens in both wild-type and p53-deficient cells.


Primarily the dataset contains the results of CRISPR screens. RPE-1 cells that are TP53-null or wild-type were infected with pooled plasmid libraries targeting selected genes and grown for up to 19 days. Samples were taken at 3, 15 & 19 days. Samples were havested and DNA extracted. PCR was used to isolate and amplify barcodes on plasmids in the CRISPR library. The barcode abundances were mapped to plasmids targeting specific genomic loci, and these abundances are included here. Abundances from other screens were downloaded from MAGeCK ( was used to obtain significance values for gene enrichment/depletion from the abundances, and the output files are included. Further analysis was performed using Python, and a Python notebook (.ipynb) is included.

Usage notes

Python version >= 3.6 is required to run the notebook (it uses f-strings); and the packages: scipy, matplotlib, pandas, statsmodels, seaborn & jupyter (plus dependencies).


  • analysis_and_charts.ipynb – Python notebook that produces all the figures in the paper.
  • all_magecks.csv – Table of significance and log2 fold change values of genes, calculated by mageck.
  • *.counts.tsv – Guide/barcode abundances from different screens.
  • sample_replicate_details.xlsx – Details about samples in our screen.
  • transomics_ddr_v2-1.csv – Details of the library used in our screen.
  • OR_gene_list.txt – Names of olfactory receptor genes.
  • mageck_results/* – files output by MAGeCK