/bio-genome-engineering-base-editing-design

name: bio-genome-engineering-base-editing-design
description: Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.
tool_type: python
primary_tool: BE-Hive

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.

Base Editing Design

**"Design a base editor guide for my C-to-T conversion"** → Identify guide sequences that position the target nucleotide within the editing window of cytosine (CBE) or adenine (ABE) base editors, predicting editing outcomes and bystander effects.

• Python: editing window analysis with `Bio.Seq`, BE-Hive outcome prediction

Base Editor Types

Cytosine Base Editors (CBE):
- Convert C to T (or G to A on opposite strand)
- Examples: BE3, BE4, BE4max, AncBE4max
- Editing window: Positions 4-8 (PAM-distal numbering)

Adenine Base Editors (ABE):
- Convert A to G (or T to C on opposite strand)
- Examples: ABE7.10, ABE8e, ABE8.20
- Editing window: Positions 4-7 (narrower than CBE)

Position numbering:
Position 1 = PAM-proximal (next to NGG)
Position 20 = PAM-distal (5' end of spacer)
Editing window is typically positions 4-8 from PAM-distal end

Find Editable Positions

**Goal:** Identify guide sequences that place a target nucleotide within the base editor's editing window while minimizing bystander edits.

**Approach:** Scan for PAM sites in both orientations, calculate where the target base falls within the spacer, filter guides where the target lands in the CBE (positions 4-8) or ABE (positions 4-7) editing window, and rank by fewest bystander bases in the window.

from Bio.Seq import Seq
import re

# Editing window positions (1-indexed from PAM-distal end)
# Position 1 is first nt of spacer, position 20 is adjacent to PAM
CBE_WINDOW = (4, 8)   # BE4max optimal window
ABE_WINDOW = (4, 7)   # ABE8e optimal window

def find_cbe_targets(sequence, target_c_position):
    '''Find guides that place a C in the CBE editing window

    Args:
        sequence: DNA sequence containing the target C
        target_c_position: 0-indexed position of C to edit

    Returns:
        List of guide options with editing predictions
    '''
    sequence = sequence.upper()
    guides = []

    # Search for PAMs that would place target C in window
    for pam_match in re.finditer(r'(?=(.GG))', sequence):
        pam_pos = pam_match.start()

        # Calculate where target C falls in the spacer
        spacer_start = pam_pos - 20
        if spacer_start < 0:
            continue

        c_position_in_spacer = target_c_position - spacer_start + 1  # 1-indexed

        # Check if C is in editing window
        if CBE_WINDOW[0] <= c_position_in_spacer <= CBE_WINDOW[1]:
            spacer = sequence[spacer_start:pam_pos]

            # Find bystander Cs in window (may also be edited)
            bystanders = []
            for i in range(CBE_WINDOW[0] - 1, CBE_WINDOW[1]):
                if i < len(spacer) and spacer[i] == 'C' and (spacer_start + i) != target_c_position:
                    bystanders.append(i + 1)

            guides.append({
                'spacer': spacer,
                'pam_position': pam_pos,
                'target_position_in_spacer': c_position_in_spacer,
                'bystander_cs': bystanders,
                'bystander_count': len(bystanders),
                'strand': '+'
            })

    # Sort by fewest bystanders
    return sorted(guides, key=lambda x: x['bystander_count'])


def find_abe_targets(sequence, target_a_position):
    '''Find guides that place an A in the ABE editing window'''
    sequence = sequence.upper()
    guides = []

    for pam_match in re.finditer(r'(?=(.GG))', sequence):
        pam_pos = pam_match.start()
        spacer_start = pam_pos - 20
        if spacer_start < 0:
            continue

        a_position_in_spacer = target_a_position - spacer_start + 1

        if ABE_WINDOW[0] <= a_position_in_spacer <= ABE_WINDOW[1]:
            spacer = sequence[spacer_start:pam_pos]

            bystanders = []
            for i in range(ABE_WINDOW[0] - 1, ABE_WINDOW[1]):
                if i < len(spacer) and spacer[i] == 'A' and (spacer_start + i) != target_a_position:
                    bystanders.append(i + 1)

            guides.append({
                'spacer': spacer,
                'pam_position': pam_pos,
                'target_position_in_spacer': a_position_in_spacer,
                'bystander_as': bystanders,
                'bystander_count': len(bystanders),
                'strand': '+'
            })

    return sorted(guides, key=lambda x: x['bystander_count'])

Editing Efficiency by Position

# Position-dependent editing efficiency
# Based on BE-Hive and published data
# Values represent relative editing efficiency (1.0 = maximum)

CBE_POSITION_EFFICIENCY = {
    # Position: efficiency (BE4max)
    1: 0.05, 2: 0.10, 3: 0.20,
    4: 0.70, 5: 0.90, 6: 1.00,  # Peak efficiency
    7: 0.85, 8: 0.50,
    9: 0.20, 10: 0.10
}

ABE_POSITION_EFFICIENCY = {
    # Position: efficiency (ABE8e)
    1: 0.02, 2: 0.05, 3: 0.15,
    4: 0.60, 5: 0.95, 6: 1.00,  # Peak at 5-6
    7: 0.70,
    8: 0.20, 9: 0.05
}

def predict_editing_efficiency(guide, editor='CBE'):
    '''Predict editing efficiency based on position

    Interpretation:
    - >0.7: High efficiency expected (good candidate)
    - 0.4-0.7: Moderate efficiency
    - <0.4: Low efficiency (consider alternatives)
    '''
    pos = guide['target_position_in_space