bio-crispr-screens-in-vivo-screens

npx skills add BioTender-max/awesome-bio-agent-skills --skill in-vivo-screens --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

## Version Compatibility Reference examples tested with: MAGeCK 0.5.9+, MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `mageck --version` - Reference focused libraries: Manguso 2017, Chen 2015, public Addgene aliquots If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## In Vivo CRISPR Screen Analysis **"Design or analyze an in vivo CRISPR screen"** -> Account for the dramatic bottleneck during animal implantation and tumor growth; use focused libraries; recover DNA from tumor explants; analyze with bottleneck-adjusted hit calling. - CLI: `mageck count` + `mageck test` for standard analysis - Special handling: bottleneck-adjusted coverage thresholds; per-tissue per-animal replicate structure ## The In Vivo Bottleneck Problem **Why in vivo screens differ from in vitro:** | Constraint | In vitro | In vivo | |------------|----------|---------| | Cells per condition | 10M-100M (unlimited) | Limited by injection volume (1-5M cells typical) | | Implant -> early tumor cell count | N/A | 10-100x d

Typical PCR + sequencing parameters for in vivo screens
Per-tumor DNA: 0.5-5 mg yield from typical syngeneic tumor
Per-sample sequencing depth: ≥500 reads/sgRNA at endpoint (lower than in vitro 300+)
Multiple animals per condition (n=5-10) to account for clonal variation
mageck count for in vivo
mageck count \
Option A: MAGeCK MLE with batch covariate
cat > in_vivo_design.txt <<EOF
mageck mle \