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Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.
Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name bio-workflows-crispr-editing-pipeline description End-to-end CRISPR experiment design from target selection to delivery-ready constructs. Covers guide RNA design, off-target assessment, and specialized editing strategies including knockouts, base editing, and HDR knockins. Use when designing complete CRISPR editing experiments for gene knockout, correction, or tagging. tool_type mixed primary_tool crisprscan workflow true depends_on ["genome-engineering/grna-design","genome-engineering/off-target-prediction","genome-engineering/base-editing-design","genome-engineering/prime-editing-design","genome-engineering/hdr-template-design"] qc_checkpoints [{"after_grna_design":"Activity score >0.6, no poly-T runs, GC 40-70%"},{"after_offtarget":"Specificity score >0.7, no coding off-targets with <3 mismatches"},{"after_template":"Homology arms verified, PAM disrupted in donor"}]
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
|
v
[1. Guide RNA Design] --> CRISPRscan / Rule Set 2 / DeepCRISPR
|
v
[2. Off-Target Assessment] --> Cas-OFFinder + CFD scoring
|
v
Decision Point: What type of edit?
|
+---+-------------------+--------------------+
| | |
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
| | |
v v v
Final Constructs with Validation Primers
Prerequisites
pip install crisprscan biopython pandas numpy matplotlib
conda install -c bioconda primer3-py cas-offinder
pip install crisprtools
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()
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'
'pam'
'position'
'strand'
'+'
'full_target'
match
1
for
match
in
r'(?=(CC[ATCG][ATCG]{20}))'
match
match
1
str
3
23
str
0
3
'sequence'
'pam'
'position'
'strand'
'-'
'full_target'
return
def
score_guide
guide_seq
'''Score guide using Rule Set 2-like heuristics.'''
0.5
'G'
'C'
len
if
0.4
0.7
0.2
elif
0.3
or
0.8
0.2
if
'TTTT'
in
0.3
if
1
'G'
0.1
if
2
'GG'
0.1
11
20
'G'
'C'
len
if
0.4
0.7
0.1
return
min
1.0
max
0.0
'''ATGGATTTATCTGCTCTTCGCGTTGAAGAAGTACAAAATGTCATTAATGCTATGCAGAAAATCTTAGAGT
GTCCCATCTGTCTGGAGTTGATCAAGGAACCTGTCTCCACAAAGTGTGACCACATATTTTGCAAATTTTG'''
'\n'
''
'activity_score'
'sequence'
'activity_score'
0.6
'activity_score'
False
print
f'Found {len (good_guides)} high-scoring guides'
print
'sequence'
'position'
'strand'
'activity_score'
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 )
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' )
for _, row in guides_df.iterrows():
f.write(f"{row['sequence' ]} NNN {max_mismatches} \n" )
output_file = output_dir / 'offtargets.txt'
subprocess.run([
'cas-offinder' , str (input_file), 'C' , str (output_file)
], check=True )
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.'''
guide_offtargets = offtargets_df[offtargets_df['pattern' ].str .contains(guide_seq[:10 ])]
if len (guide_offtargets) == 0 :
return 1.0
penalty = 0
for _, ot in guide_offtargets.iterrows():
mm = ot['mismatches' ]
if mm == 0 :
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
return max (0 , 1 - penalty / 10 )
good_guides['specificity_score' ] = good_guides['sequence' ].apply(
lambda x: calculate_specificity_score(x, pd.DataFrame())
)
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' ]
cut_site = position + 17 if guide_row['strand' ] == '+' else position + 6
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
if guide['strand' ] == '+' :
window_start = guide_start + 3
window_end = guide_start + 8
else :
window_start = guide_end - 8
window_end = guide_end - 3
if window_start <= target_position <= window_end:
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)
target_pos = 45
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
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]
guide_seq = guide_row['sequence' ]
pam_position_in_arms = cut_site - left_arm_start + 3
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'
}
gfp_sequence = 'ATGGTGAGCAAGGGCGAGGAG...'
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 ]})
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([])
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_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.tsvAll guides with activity and specificity scores offtargets.txtCas-OFFinder results knockout_design.jsonKO guide and validation primers base_edit_design.jsonCBE/ABE design with editing window hdr_template.fastaDonor template sequence guide_landscape.pdfVisualization 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
