bio-genome-engineering-grna-design

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Summary

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.

SKILL.md

Version Compatibility

Reference examples tested with: BioPython 1.83+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Guide RNA Design

"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

Find PAM Sites

from Bio.Seq import Seq
import re

def find_pam_sites(sequence, pam='NGG', guide_length=20):
    '''Find all PAM sites and extract guide sequences

    PAM patterns:
    - NGG: SpCas9 (most common)
    - TTTN: Cas12a/Cpf1 (5' PAM)
    - NNGRRT: SaCas9 (smaller, for AAV delivery)
    '''
    sequence = sequence.upper()
    guides = []

    # NGG PAM - guide is 20bp upstream of PAM
    if pam == 'NGG':
        for match in re.finditer(r'(?=(.GG))', sequence):
            pos = match.start()
            if pos >= guide_length:
                guide = sequence[pos - guide_length:pos]
                guides.append({
                    'sequence': guide,
                    'pam': sequence[pos:pos + 3],
                    'position': pos - guide_length,
                    'strand': '+'
                })

    # Also search reverse complement
    rc_seq = str(Seq(sequence).reverse_complement())
    for match in re.finditer(r'(?=(.GG))', rc_seq):
        pos = match.start()
        if pos >= guide_length:
            guide = rc_seq[pos - guide_length:pos]
            original_pos = len(sequence) - pos
            guides.append({
                'sequence': guide,
                'pam': rc_seq[pos:pos + 3],
                'position': original_pos,
                'strand': '-'
            })

    return guides

# Rule Set 2 position-weight matrix (Doench et al. 2016)
# Position 0 = PAM-distal, Position 19 = PAM-proximal
# Higher scores indicate preferred nucleotides at each position
RULE_SET_2_WEIGHTS = {
    # Position: {nucleotide: weight}
    0: {'A': 0, 'C': 0, 'G': 0.08, 'T': -0.08},
    1: {'A': 0.02, 'C': -0.06, 'G': 0.06, 'T': -0.02},
    # ... simplified - full matrix has all 20 positions
    18: {'A': -0.07, 'C': 0.13, 'G': -0.01, 'T': -0.05},
    19: {'A': -0.07, 'C': 0.03, 'G': 0.11, 'T': -0.07},
}

def calculate_gc_content(sequence):
    gc = sum(1 for nt in sequence.upper() if nt in 'GC')
    return gc / len(sequence)

def score_guide_activity(guide_seq):
    '''Score guide on-target activity (0-1 scale)

    Scoring criteria:
    - GC content 40-70%: optimal range (outside this = penalty)
    - Position-specific nucleotide preferences
    - No poly-T stretches (terminates Pol III transcription)

    Interpretation:
    - >0.6: High activity expected
    - 0.4-0.6: Moderate activity
    - <0.4: Low activity, consider alternatives
    '''
    guide_seq = guide_seq.upper()
    score = 0.5  # Base score

    # GC content penalty
    gc = calculate_gc_content(guide_seq)
    if gc < 0.4 or gc > 0.7:
        score -= 0.15

    # Poly-T penalty (>=4 consecutive T's)
    if 'TTTT' in guide_seq:
        score -= 0.3

    # Position-specific scoring (simplified)
    for pos, weights in RULE_SET_2_WEIGHTS.items():
        if pos < len(guide_seq):
            nt = guide_seq[pos]
            score += weights.get(nt, 0)

    return max(0, min(1, score))

# CRISPRscan uses a different model optimized for zebrafish
# 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

def design_guides_for_gene(gene_sequence, exon_coords=None, n_guides=5):
    '''Design top N guides for a gene

    Args:
        gene_sequence: Full gene sequence (DNA)
        exon_coords: List of (start, end) tuples for coding exons
        n_guides: Number of top guides to return

    Returns:
        List of guide dicts sorted by activity score
    '''
    # Find all PAM sites
    all_guides = find_pam_sites(gene_sequence)

    # Filter to coding regions if exon coordinates provided
    if exon_coords:
        coding_guides = []
        for guide in all_guides:
            for start, end in exon_coords:
                if start <= guide['position'] <= end:
                    coding_guides.append(guide)
                    break
        all_guides = coding_guides

    # Score each guide
    for guide in all_guides:
        guide['activity_score'] = score_guide_activity(guide['sequence'])

    # Sort by activity and return top N
    all_guides.sort(key=lambda x: x['activity_score'], reverse=True)
    return all_guides[:n_guides]

def find_cas12a_guides(sequence, guide_length=23):
    '''Find Cas12a (Cpf1) guide sequences

    Cas12a differences from Cas9:
    - 5' PAM (TTTV where V = A/C/G)
    - Longer guide (23nt vs 20nt)
    - Staggered cut (5nt 5' overhang)
    - Lower off-target activity
    '''
    sequence = sequence.upper()
    guides = []

    # TTTV PAM pattern (5' of guide)
    for match in re.finditer(r'TTT[ACG]', sequence):
        pos = match.end()
        if pos + guide_length <= len(sequence):
            guide = sequence[pos:pos + guide_length]
            guides.append({
                'sequence': guide,
                'pam': match.group(),
                'position': pos,
                'strand': '+',
                'nuclease': 'Cas12a'
            })

    return guides

bio-genome-engineering-grna-design

Summary

SKILL.md

Version Compatibility

Guide RNA Design

Find PAM Sites

Score On-Target Activity

CRISPRscan Scoring

Design Workflow

Cas12a Guide Design

Related Skills

Related skills