Analyze CRISPR-Cas9 genetic screens — MAGeCK gene-level scores, sgRNA count QC, replicate correlation, hit prioritization, and pathway GSEA on screen output. Use for genome-wide essentiality screens, synthetic-lethality discovery, dropout vs positive-selection screen analysis, target identification,

ToolUniverse CRISPR Screen Analysis RULE ZERO — Check for pre computed results FIRST Before following any instruction below, scan the data folder for: executed.ipynb → read with tu run read executed notebook '{"data folder":"<path ","search":"<keyword "}' and cite its cell outputs as the authoritative answer Pre computed result files (CSV/TSV with names like results , deseq , enrich , stats , simplified.csv ) → read directly and report the requested value Canonical analysis scripts ( analysis.R , run .py , find .R , .Rmd ) → execute as is and read the output Only follow this skill's re analysis recipe below if none of the above exist. Re running from raw data produces different numbers than the published answer and is much slower (often 5 10× turn count). Comprehensive skill for analyzing CRISPR Cas9 genetic screens to identify essential genes, synthetic lethal interactions, and therapeutic targets through robust statistical analysis and pathway enrichment. Overview CRISPR screens enable genome wide functional genomics by systematically perturbing genes and measuring fitness effects. This skill provides an 8 phase workflow for: Processing sgRNA count matrices Quality control and normalization Gene level essentiality scoring (MAGeCK like and BAGEL like approaches) Synthetic lethality detection Pathway enrichment analysis Drug target prioritization with DepMap integration Integration with expression and mutation data Core Workflow Phase 1: Data Import & sgRNA Count Processing Load sgRNA count matrix (MAGeCK format or generic TSV). Expected columns: sgRNA , Gene , plus sample columns. Create experimental design table linking samples to conditions (baseline/treatment) with replicate assignments. Phase 2: Quality Control & Filtering Assess sgRNA distribution quality: Library sizes per sample (total reads) Zero count sgRNAs : Count across samples Low count filtering : Remove sgRNAs below threshold (default: <30 reads in N 2 samples) Gini coefficient : Assess distribution skewness per sample Report filtering recommendations Phase 3: Normalization Normalize sgRNA counts to account for library size differences: Median ratio (DESeq2 like): Calculate geometric mean reference, compute size factors as median of ratios Total count (CPM like): Divide by library size in millions Calculate log2 fold changes (LFC) between treatment and control conditions with pseudocount. Phase 4: Gene Level Scoring Two scoring approaches: MAGeCK like (RRA) : Rank all sgRNAs by LFC, compute mean rank per gene. Lower mean rank = more essential. Includes sgRNA count and mean LFC per gene. BAGEL like (Bayes Factor) : Use reference essential/non essential gene sets to estimate LFC distributions. Calculate likelihood ratio (Bayes Factor) for each gene. Higher BF = more likely essential. Phase 5: Synthetic Lethality Detection Compare essentiality scores between wildtype and mutant cell lines: Merge gene scores, calculate delta LFC and delta rank Filter for genes essential in mutant (LFC < threshold) but not wildtype (LFC 0.5) with large rank change Sort by differential essentiality Query DepMap/literature for known dependencies using PubMed search. Phase 6: Pathway Enrichment Analysis Submit top essential genes to Enrichr for pathway enrichment: KEGG pathways GO Biological Process Retrieve enriched terms with p values and gene lists Phase 7: Drug Target Prioritization Composite scoring combining: Essentiality (50% weight): Normalized mean LFC from CRISPR screen Expression (30% weight): Log2 fold change from RNA seq (if available) Druggability (20% weight): Number of drug interactions from DGIdb Query DGIdb for each candidate gene to find existing drugs, interaction types, and sources. Phase 8: Report Generation Generate markdown report with: Summary statistics (total genes, essential genes, non essential genes) Top 20 essential genes table (rank, gene, mean LFC, sgRNAs, score) Pathway enrichment results (top 10 terms per database) Drug target candidates (rank, gene, essentiality, expression FC, druggability, priority score) Methods section ToolUniverse Tool Integration Key Tools Used : PubMed search articles Literature search for gene essentiality and drug resistance ReactomeAnalysis pathway enrichment Pathway enrichment (param: identifiers newline separated, page size ) enrichr gene enrichment analysis Enrichr enrichment (param: gene list array, libs array) DGIdb get drug gene interactions Drug gene interactions (param: genes as array) DGIdb get gene druggability Druggability categories STRING get network Protein interaction networks kegg search pathway Pathway search by keyword kegg get pathway info Pathway details by ID Cancer Context (essential for drug resistance screens): civic search evidence items Clinical evidence for drug resistance/sensitivity COSMIC get mutations by gene Somatic mutation landscape cBioPortal get mutations Mutations in specific cancer cohorts ChEMBL search targets Structural druggability assessment Expression & Variant Integration : GEO search rnaseq datasets / geo search datasets Expression datasets ClinVar search variants Known pathogenic variants gnomad get gene constraints Gene constraint metrics (pLI, oe lof) UniProt get function by accession Protein function for hit validation Quick Start Domain Reasoning: Hits Are Statistical, Not Biological Screen hits are statistical findings, not direct readouts of biological relevance. A gene scoring as essential might be essential for cell growth in general (housekeeping) or essential specifically for the phenotype you are screening for (interesting). Always compare your screen hits to public essentiality data — use DepMap pan cancer dependency scores to filter genes that are broadly essential across all cell lines. A gene essential only in your specific context, but not pan essential in DepMap, is a better candidate for follow up than one that scores in every screen. LOOK UP DON'T GUESS: DepMap dependency scores, known core essential gene sets (Hart et al., Blomen et al.), and DGIdb druggability data for your top hits. Do not assume a hit is context specific without checking public essentiality databases. Interpretation Framework Evidence Grade Criteria Validation Priority A Strong hit MAGeCK RRA p < 0.001, BAGEL BF 5, =3 sgRNAs with concordant LFC Immediate validation (individual KO, growth assay) B Moderate hit MAGeCK RRA p < 0.01, BAGEL BF 2 5, =2 concordant sgRNAs Secondary validation pool C Weak/ambiguous p 0.01, BF < 2, or discordant sgRNA effects Deprioritize; check for copy number bias or seed effects Interpreting screen results: A gene with mean LFC < 1.0 across replicates and =3 concordant sgRNAs is a robust essentiality hit; single sgRNA effects are more likely off target and should be flagged. Essential gene thresholds are context dependent: core fitness genes (e.g., ribosomal, spliceosomal) should deplete in any screen and serve as positive controls their absence from the hit list indicates a QC problem. Synthetic lethal hits (depleted in mutant but not wildtype) require delta LFC 1.5 and confirmation in an independent cell line before therapeutic target nomination. Synthesis questions to address in the report: 1. Do the top hits cluster in known pathways (Reactome/KEGG), or are they scattered suggesting technical noise? 2. Are known essential genes (Hart et al. reference set) correctly identified, confirming screen quality? The full CEGv2 core essential (~684) and NEGv1 non essential (~928) reference sets are bundled in scripts/ — load them with from reference gene sets import core essential, nonessential, recovery rate . recovery rate(top depleted genes) gives the fraction of core essential genes recovered (a good genome wide screen recovers ~0.8). 3. For drug target candidates: does DGIdb show existing compounds, and does DepMap confirm the dependency across multiple cell lines? References Li W, et al. (2014) MAGeCK enables robust identification of essential genes from genome scale CRISPR/Cas9 knockout screens. Genome Biology Hart T, et al. (2015) High Resolution CRISPR Screens Reveal Fitness Genes and Genotype Specific Cancer Liabilities. Cell Meyers RM, et al. (2017) Computational correction of copy number effect improves specificity of CRISPR Cas9 essentiality screens. Nature Genetics Tsherniak A, et al. (2017) Defining a Cancer Dependency Map. Cell (DepMap) Analysis conventions Replicate Spearman correlation — match the paper's aggregation level Papers differ on how replicate reproducibility is reported: sgRNA level CPM vs gene level summed CPM vs gene level mean CPM. The expected GT is almost always the sgRNA level Spearman (noisier, lower ρ), not the gene level aggregate. If you get ρ ≈ 0.6+ you are probably at gene level; drop to per sgRNA CPM pairs. Reactome GSEA ranking column For GSEA on a MAGeCK output, rank by the neg lfc or equivalent effect size column the paper specifies (not p value). Check the MAGeCK xlsx for a beta / sgRNA effect / neg score column and rank descending. Pathway naming Reactome pathway names in the .gmt bundle are literal (e.g., "cGMP effects" , "Signaling by Hippo" ). Answers that should match a Reactome term must reproduce the exact label — do not paraphrase the pathway. See Also ANALYSIS DETAILS.md Detailed code snippets for all 8 phases USE CASES.md Complete use cases (essentiality screen, synthetic lethality, drug target discovery, expression integration) and best practices EXAMPLES.md Example usage and quick reference QUICK START.md Quick start guide FALLBACK PATCH.md Fallback patterns for API issues

tooluniverse-crispr-screen-analysis