bio-crispr-screens-prime-editing-screens
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and in
npx skills add BioTender-max/awesome-bio-agent-skills --skill prime-editing-screens --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: PRIDICT2 v1.0+ (https://github.com/uzh-dqbm-cmi/PRIDICT2), CRISPResso2 2.2.14+, pandas 2.2+, biopython 1.83+, numpy 1.26+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `python pridict2_pegRNA_design.py single --help`; `python pridict2_pegRNA_design.py batch --help` - Web: PRIDICT2 web interface at https://pridict.it/ If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Prime-Editing Screen Analysis **"Design or analyze a pooled prime-editor screen"** -> Design pegRNAs (spacer + scaffold + PBS + RTT) for intended edits, predict efficiency with PRIDICT2, filter pre-synthesis to efficient candidates, install variants in the screen, quantify intended-edit vs scaffold-incorporation vs indel via CRISPResso2, and aggregate to per-variant fitness scores. - Python: `PRIDICT2` for pegRNA efficiency prediction - CLI: `CRISPResso --prime_editing_pegRNA_*` for amplicon-level analysis - Workflow: pegRNA library design -> PRIDICT2 filtering -> screen execution -> CRISPResso2 quantification -
- Version Compatibility
- Prime-Editing Screen Analysis
- Prime Editor Chemistry Comparison
- pegRNA Architecture
- PRIDICT and PRIDICT2 pegRNA Efficiency Prediction
- PRIME Pooled Screen Methodology
- MOSAIC In Situ Saturation Mutagenesis
- Run PRIDICT2 on a Custom pegRNA Library
- Cross-Validate PE with Base Editor Screens
- CRISPResso2 for PE Quantification
- Failure Modes
- Low pegRNA efficiency despite high PRIDICT prediction
- High scaffold incorporation
- PE2 cell line lacks RT expression
PRIDICT2 is invoked via CLI: pridict2_pegRNA_design.py Single sequence input: python pridict2_pegRNA_design.py single \ Batch input from CSV: python pridict2_pegRNA_design.py batch \ Step 1: prepare batch input CSV (sequence_name, sequence with (REF/ALT) edit notation) cat > variants.csv <<EOF Step 2: run PRIDICT2 batch CRISPResso \
What does the bio-crispr-screens-prime-editing-screens skill do?
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and in
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill prime-editing-screens --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.