bio-workflows-crispr-editing-pipeline

Version Compatibility

Reference examples tested with: BioPython 1.83+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, primer3-py 2.0+

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

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

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

CRISPR Editing Pipeline

"Design a complete CRISPR editing experiment for my target gene" → Orchestrate guide RNA design, off-target assessment, and strategy-specific template design (knockout, base editing, HDR knock-in, or prime editing) to produce delivery-ready constructs.

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

# 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))