CRISPR Editing Pipeline
Complete workflow for CRISPR experiment design: from target gene to delivery-ready constructs with branching paths for different editing strategies.
Workflow Overview
Target Gene/Position
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v
[1. Guide RNA Design] --> CRISPRscan / Rule Set 2 / DeepCRISPR
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[2. Off-Target Assessment] --> Cas-OFFinder + CFD scoring
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Decision Point: What type of edit?
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+---+-------------------+--------------------+
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v v v
[3a. Knockout] [3b. Base Editing] [3c. Knockin]
Standard Cas9 CBE/ABE design HDR template
Frameshift C>T or A>G with homology arms
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v v v
Final Constructs with Validation Primers
Prerequisites
pip install crisprscan biopython pandas numpy matplotlib
conda install -c bioconda primer3-py cas-offinder
# Python packages for scoring
pip install crisprtools # if available
Primary Path: Gene Knockout
Step 1: Guide RNA Design
from Bio import SeqIO
from Bio.Seq import Seq
import pandas as pd
import re
def find_guides(sequence, pam='NGG'):
'''Find all potential gRNA target sites with NGG PAM.'''
guides = []
seq_str = str(sequence).upper()
# Forward strand: 20bp + NGG
for match in re.finditer(r'(?=([ATCG]{20}[ATCG]GG))', seq_str):
pos = match.start()
target = match.group(1)[:20]
pam_seq = match.group(1)[20:23]
guides.append({
'sequence': target,
'pam': pam_seq,
'position': pos,
'strand': '+',
'full_target': match.group(1)
})
# Reverse strand: CCN + 20bp
for match in re.finditer(r'(?=(CC[ATCG][ATCG]{20}))', seq_str):
pos = match.start()
full = match.group(1)
target = str(Seq(full[3:23]).reverse_complement())
pam_seq = str(Seq(full[0:3]).reverse_complement())
guides.append({
'sequence': target,
'pam': pam_seq,
'position': pos,
'strand': '-',
'full_target': full
})
return pd.DataFrame(guides)
def score_guide(guide_seq):
'''Score guide using Rule Set 2-like heuristics.'''
score = 0.5 # Base score
# GC content (optimal: 40-70%)
gc = (guide_seq.count('G') + guide_seq.count('C')) / len(guide_seq)
if 0.4 <= gc <= 0.7:
score += 0.2
elif gc < 0.3 or gc > 0.8:
score -= 0.2
# No poly-T (>4 T's is Pol III terminator)
if 'TTTT' in guide_seq:
score -= 0.3
# G at position 20 (adjacent to PAM) preferred
if guide_seq[-1] == 'G':
score += 0.1
# Avoid GG at positions 19-20
if guide_seq[-2:] == 'GG':
score -= 0.1
# Seed region (positions 12-20) GC
seed = guide_seq[11:20]
seed_gc = (seed.count('G') + seed.count('C')) / len(seed)
if 0.4 <= seed_gc <= 0.7:
score += 0.1
return min(1.0, max(0.0, score))
# Example: Design guides for BRCA1 exon
gene_seq = '''ATGGATTTATCTGCTCTTCGCGTTGAAGAAGTACAAAATGTCATTAATGCTATGCAGAAAATCTTAGAGT
GTCCCATCTGTCTGGAGTTGATCAAGGAACCTGTCTCCACAAAGTGTGACCACATATTTTGCAAATTTTG'''
guides = find_guides(gene_seq.replace('\n', ''))
guides['activity_score'] = guides['sequence'].apply(score_guide)
# Filter high-scoring guides
# Activity score >0.6 is standard threshold for reliable editing
good_guides = guides[guides['activity_score'] > 0.6].sort_values('activity_score', ascending=False)
print(f'Found {len(good_guides)} high-scoring guides')
print(good_guides[['sequence', 'position', 'strand', 'activity_score']].head(10))
Step 2: Off-Target Assessment
import subprocess
from pathlib import Path
def run_cas_offinder(guides_df, genome_fasta, output_dir, max_mismatches=4):
'''Run Cas-OFFinder for off-target detection.'''
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Write input file
input_file = output_dir / 'cas_offinder_input.txt'
with open(input_file, 'w') as f:
f.write(f'{genome_fasta}\n')
f.write('NNNNNNNNNNNNNNNNNNNNNGG\n') # 20bp + NGG pattern
for _, row in guides_df.iterrows():
f.write(f"{row['sequence']}NNN {max_mismatches}\n")
# Run Cas-OFFinder
output_file = output_dir / 'offtargets.txt'
subprocess.run([
'cas-offinder', str(input_file), 'C', str(output_file) # C for CPU
], check=True)
# Parse results
offtargets = pd.read_csv(output_file, sep='\t', header=None,
names=['pattern', 'chromosome', 'position', 'target',
'strand', 'mismatches'])
return offtargets
def calculate_specificity_score(guide_seq, offtargets_df):
'''Calculate CFD-based specificity score.'''
# Simplified: penalize based on mismatch count and position
guide_offtargets = offtargets_df[offtargets_df['pattern'].str.contains(guide_seq[:10])]
if len(guide_offtargets) == 0:
return 1.0
# Weight by mismatch count (more mismatches = lower penalty)
penalty = 0
for _, ot in guide_offtargets.iterrows():
mm = ot['mismatches']
if mm == 0: # Perfect match elsewhere (bad!)
penalty += 1.0
elif mm == 1:
penalty += 0.5
elif mm == 2:
penalty += 0.2
elif mm == 3:
penalty += 0.1
else:
penalty += 0.05
# Specificity score: higher is better
# Score >0.7 is generally acceptable
return max(0, 1 - penalty / 10)
# Filter by off-target profile
good_guides['specificity_score'] = good_guides['sequence'].apply(
lambda x: calculate_specificity_score(x, pd.DataFrame()) # placeholder
)
# Combined score
good_guides['combined_score'] = (good_guides['activity_score'] * 0.5 +
good_guides['specificity_score'] * 0.5)
final_guides = good_guides.sort_values('combined_score', ascending=False).head(5)
Step 3a: Knockout Design (Frameshift)
def design_knockout(guide_row, target_sequence):
'''Design knockout experiment with validation primers.'''
guide_seq = guide_row['sequence']
position = guide_row['position']
# Cas9 cuts 3bp upstream of PAM
cut_site = position + 17 if guide_row['strand'] == '+' else position + 6
# Validation primers flanking cut site (~200bp amplicon)
# 200bp amplicon is optimal for detecting indels by gel or Sanger
left_start = max(0, cut_site - 100)
right_end = min(len(target_sequence), cut_site + 100)
return {
'guide_sequence': guide_seq,
'pam': guide_row['pam'],
'cut_site': cut_site,
'expected_outcome': 'Frameshift indel',
'validation_amplicon_start': left_start,
'validation_amplicon_end': right_end
}
ko_design = design_knockout(final_guides.iloc[0], gene_seq.replace('\n', ''))
print('Knockout Design:')
for k, v in ko_design.items():
print(f' {k}: {v}')
Step 3b: Base Editing Design (CBE/ABE)
def design_base_edit(target_position, target_sequence, edit_type='CBE'):
'''Design base editing experiment.
CBE: C>T conversion (or G>A on opposite strand)
ABE: A>G conversion (or T>C on opposite strand)
Editing window: positions 4-8 in the protospacer (counting from PAM-distal)
'''
guides = find_guides(target_sequence)
suitable_guides = []
for _, guide in guides.iterrows():
guide_start = guide['position']
guide_end = guide_start + 20
# Check if target position falls in editing window (positions 4-8)
# Window position 4-8 is optimal for BE3/BE4 (CBE) and ABE7.10/ABE8
if guide['strand'] == '+':
window_start = guide_start + 3 # Position 4
window_end = guide_start + 8 # Position 8
else:
window_start = guide_end - 8
window_end = guide_end - 3
if window_start <= target_position <= window_end:
# Check if target base is appropriate
target_base = target_sequence[target_position].upper()
if edit_type == 'CBE' and target_base in ['C', 'G']:
suitable_guides.append(guide)
elif edit_type == 'ABE' and target_base in ['A', 'T']:
suitable_guides.append(guide)
return pd.DataFrame(suitable_guides)
# Example: Design CBE to introduce stop codon
# C>T at specific position can create TAG/TAA/TGA stop
target_pos = 45 # Example position with C
cbe_guides = design_base_edit(target_pos, gene_seq.replace('\n', ''), 'CBE')
print(f'Found {len(cbe_guides)} CBE-compatible guides')
Step 3c: Knockin Design (HDR Template)
def design_hdr_template(guide_row, target_sequence, insert_sequence,
homology_arm_length=800):
'''Design HDR donor template with homology arms.
Homology arm length: 800bp is standard for plasmid donors.
For ssODN, use 30-60bp arms.
'''
cut_site = guide_row['position'] + 17 if guide_row['strand'] == '+' else guide_row['position'] + 6
# Extract homology arms
# Arms flank the cut site
left_arm_start = max(0, cut_site - homology_arm_length)
left_arm = target_sequence[left_arm_start:cut_site]
right_arm_end = min(len(target_sequence), cut_site + homology_arm_length)
right_arm = target_sequence[cut_site:right_arm_end]
# Mutate PAM in donor to prevent re-cutting
# Change NGG to NGA or NAG (silent if possible)
guide_seq = guide_row['sequence']
pam_position_in_arms = cut_site - left_arm_start + 3
# Full donor: left_arm + insert + right_arm
donor = left_arm + insert_sequence + right_arm
return {
'guide_sequence': guide_seq,
'cut_site': cut_site,
'left_arm': left_arm,
'right_arm': right_arm,
'insert': insert_sequence,
'donor_template': donor,
'donor_length': len(donor),
'note': 'Remember to mutate PAM in donor to prevent re-cutting'
}
# Example: Insert GFP tag
gfp_sequence = 'ATGGTGAGCAAGGGCGAGGAG...' # Truncated for example
hdr_design = design_hdr_template(final_guides.iloc[0], gene_seq.replace('\n', ''), 'FLAG_TAG', 50)
print('HDR Design:')
print(f" Left arm length: {len(hdr_design['left_arm'])}")
print(f" Right arm length: {len(hdr_design['right_arm'])}")
print(f" Total donor length: {hdr_design['donor_length']}")
Visualization
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
def plot_guide_landscape(guides_df, gene_length, exon_coords=None):
'''Visualize guide positions and scores along gene.'''
fig, axes = plt.subplots(2, 1, figsize=(14, 6), gridspec_kw={'height_ratios': [1, 2]})
# Top: Gene structure
ax1 = axes[0]
ax1.axhline(y=0.5, color='gray', linewidth=10, solid_capstyle='butt')
if exon_coords:
for start, end in exon_coords:
ax1.axhline(y=0.5, xmin=start/gene_length, xmax=end/gene_length,
color='steelblue', linewidth=20, solid_capstyle='butt')
ax1.set_xlim(0, gene_length)
ax1.set_ylim(0, 1)
ax1.set_ylabel('Gene')
ax1.set_xticks([])
ax1.set_yticks([])
# Bottom: Guide scores
ax2 = axes[1]
colors = ['green' if s > 0.6 else 'orange' if s > 0.4 else 'red'
for s in guides_df['activity_score']]
ax2.scatter(guides_df['position'], guides_df['activity_score'],
c=colors, s=50, alpha=0.7)
ax2.axhline(y=0.6, color='green', linestyle='--', alpha=0.5, label='Threshold')
ax2.set_xlim(0, gene_length)
ax2.set_ylim(0, 1)
ax2.set_xlabel('Position (bp)')
ax2.set_ylabel('Activity Score')
ax2.legend()
plt.tight_layout()
plt.savefig('guide_landscape.pdf')
return fig
# Plot
plot_guide_landscape(guides, len(gene_seq.replace('\n', '')),
exon_coords=[(0, 50), (70, 130)])
Parameter Recommendations
| Step |
Parameter |
Value |
Rationale |
| Guide design |
Activity score |
>0.6 |
Standard threshold for reliable editing |
| Guide design |
GC content |
40-70% |
Optimal for binding and Cas9 activity |
| Off-target |
Max mismatches |
4 |
Catches most relevant off-targets |
| Off-target |
Specificity score |
>0.7 |
Acceptable off-target profile |
| Base editing |
Window |
positions 4-8 |
Optimal for BE3/BE4, ABE7.10 |
| HDR |
Homology arms |
800bp |
Standard for plasmid donors |
| HDR (ssODN) |
Homology arms |
30-60bp |
For single-strand oligo donors |
Troubleshooting
| Issue |
Likely Cause |
Solution |
| No high-scoring guides |
GC-poor region |
Expand search region, consider Cas12a |
| Many off-targets |
Repetitive sequence |
Use high-fidelity Cas9 (eSpCas9, HiFi) |
| Low HDR efficiency |
NHEJ dominant |
Add NHEJ inhibitors, use ssODN |
| Base editing outside window |
Guide position |
Redesign with target in positions 4-8 |
| Bystander edits |
Multiple C/A in window |
Design guides with single target base |
Output Files
| File |
Description |
guides_ranked.tsv |
All guides with activity and specificity scores |
offtargets.txt |
Cas-OFFinder results |
knockout_design.json |
KO guide and validation primers |
base_edit_design.json |
CBE/ABE design with editing window |
hdr_template.fasta |
Donor template sequence |
guide_landscape.pdf |
Visualization of guide positions |
Related Skills
- genome-engineering/grna-design - Detailed scoring algorithms
- genome-engineering/off-target-prediction - Cas-OFFinder and CFD
- genome-engineering/base-editing-design - CBE/ABE specifics
- genome-engineering/prime-editing-design - pegRNA design
- genome-engineering/hdr-template-design - Donor optimization
- primer-design/primer-basics - Validation primer design
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