crispr-screen-analysis

To identify positively and negatively selected genes from a CRISPR screen, you can process raw count tables or FASTQ-derived counts using MAGeCK test or mle modes to generate ranked gene hits and publication-quality plots.

Yes, you can run CRISPR screen analysis starting from FASTQ inputs by utilizing the count workflow, which processes FASTQ files alongside an sgRNA library to produce the count tables needed for hit calling.

Yes, the CRISPR screen analysis pipeline works for CRISPRi and CRISPRa experiments, as well as genome-wide knockout screens, supporting both standard treatment versus control comparisons and multi-condition designs.

MAGeCK test compares treatment versus control groups to rank selected genes using RRA, while MAGeCK mle estimates gene selection coefficients from a design matrix for multi-condition CRISPR screen experiments.

If MAGeCK is not installed on your system during CRISPR screen analysis, the pipeline applies a Python-based RRA fallback to ensure gene and sgRNA level summaries, plots, and metadata are always produced.

To generate volcano plots and QC plots from CRISPR screen data, run the count-to-result pipeline which outputs gene-level summaries, rank plots, and a complete figure suite for publication directly from your count table.