Find candidate guide RNAs for CRISPR experiments by scanning target DNA sequences. Rank candidates using sequence features and predicted activity, optionally restrict them to coding regions, and identify guides for Cas12a.
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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.
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Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
Bio.Seq, CRISPRscan scoring modelsfrom 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
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.
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
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