Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction
BI

bio-genome-engineering-grna-design

Maintainer FreedomIntelligence · Last updated April 1, 2026

Design guide RNAs for CRISPR-Cas9/Cas12a experiments using CRISPRscan and local scoring algorithms. Score guides for on-target activity using Rule Set 2 and Azimuth models. Use when designing sgRNAs for gene knockout, activation, or repression experiments.

OpenClawNanoClawAnalysisReproductionbio-genome-engineering-grna-design🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epidemiological & causal genomicsdesign

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-genome-engineering-grna-design

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Python: custom PAM scanning with Bio.Seq, CRISPRscan scoring models.
  • Design guide RNAs for my CRISPR knockout experiment" → Scan a target gene sequence for PAM sites, extract candidate spacer sequences, and score them for on-target activity using Rule Set 2 or CRISPRscan algorithms. Python: custom PAM scanning with Bio.Seq, CRISPRscan scoring models.
  • GC content 40-70%: optimal range (outside this = penalty).
  • Position-specific nucleotide preferences.
  • No poly-T stretches (terminates Pol III transcription).

Source Doc

Excerpt From SKILL.md

but works well across species for Cas9

def crisprscan_score(guide_35mer): '''Score using CRISPRscan model

Input: 35-mer (6bp upstream + 20bp guide + 3bp PAM + 6bp downstream)
Output: Activity score 0-100

Requires the crisprscan package:
pip install crisprscan
'''
try:
    import crisprscan
    return crisprscan.score(guide_35mer)
except ImportError:
    # Fallback to simplified scoring
    return score_guide_activity(guide_35mer[6:26]) * 100

## Design Workflow

**Goal:** Design the top N guide RNAs for a target gene, optionally restricted to coding exon regions.

**Approach:** Scan both strands for PAM sites, optionally filter to guides within exon coordinates, score each guide for on-target activity using GC content and position-weight criteria, and return the highest-scoring candidates.

Use cases

  • Use when designing sgRNAs for gene knockout, activation, or repression experiments.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • genome-engineering/off-target-prediction - Check off-targets after design
  • crispr-screens/library-design - Pool multiple guides for screens
  • primer-design/primer-basics - Design flanking primers for validation

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