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 < (spacer)  spacer[i] ==   (spacer_start + i) != target_c_position:
                    bystanders.append(i + )

            guides.append({
                : spacer,
                : pam_pos,
                : c_position_in_spacer,
                : bystanders,
                : (bystanders),
                : 
            })

    
     (guides, key= x: x[])


 ():
    
    sequence = sequence.upper()
    guides = []

     pam_match  re.finditer(, sequence):
        pam_pos = pam_match.start()
        spacer_start = pam_pos - 
         spacer_start < :
            

        a_position_in_spacer = target_a_position - spacer_start + 

         ABE_WINDOW[] <= a_position_in_spacer <= ABE_WINDOW[]:
            spacer = sequence[spacer_start:pam_pos]

            bystanders = []
             i  (ABE_WINDOW[] - , ABE_WINDOW[]):
                 i < (spacer)  spacer[i] ==   (spacer_start + i) != target_a_position:
                    bystanders.append(i + )

            guides.append({
                : spacer,
                : pam_pos,
                : a_position_in_spacer,
                : bystanders,
                : (bystanders),
                : 
            })

     (guides, key= x: x[])

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[]

     editor == :
        efficiency = CBE_POSITION_EFFICIENCY.get(pos, )
    :  
        efficiency = ABE_POSITION_EFFICIENCY.get(pos, )

     efficiency

Bystander Edit Prediction

def predict_bystander_edits(spacer, editor='CBE'):
    '''Predict which bases in the window will be edited

    Bystanders are non-target bases in the editing window
    that may also be converted. This is a key consideration
    for base editing design.

    Returns:
        List of predicted edits with efficiency scores
    '''
    edits = []

    if editor == 'CBE':
        window = CBE_WINDOW
        target_base = 'C'
        efficiency_map = CBE_POSITION_EFFICIENCY
    else:
        window = ABE_WINDOW
        target_base = 'A'
        efficiency_map = ABE_POSITION_EFFICIENCY

    for i in range(window[0] - 1, window[1]):
        if i < len(spacer) and spacer[i] == target_base:
            pos = i + 1  # 1-indexed
            edits.append({
                'position': pos,
                'original': target_base,
                'edited': 'T' if editor == 'CBE' else 'G',
                'efficiency': efficiency_map.get(pos, 0.1)
            })

    return edits

Dual Base Editor Design

def design_dual_edit(sequence, c_position, a_position, max_distance=50):
    '''Design for simultaneous C>T and A>G edits

    Some applications require both CBE and ABE edits.
    This finds guides where both targets are accessible.
    '''
    cbe_guides = find_cbe_targets(sequence, c_position)
    abe_guides = find_abe_targets(sequence, a_position)

    # Find compatible pairs (different PAMs, both in window)
    compatible = []
    for cbe in cbe_guides:
        for abe in abe_guides:
            distance = abs(cbe['pam_position'] - abe['pam_position'])
            if distance > 0 and distance <= max_distance:
                compatible.append({
                    'cbe_guide': cbe,
                    'abe_guide': abe,
                    'distance': distance
                })

    return compatible

Sequence Context Effects

def score_sequence_context(spacer, position, editor='CBE'):
    '''Score based on sequence context preferences

    CBE context preferences (5' neighbor of target C):
    - TC: High efficiency (most preferred)
    - CC: Good efficiency
    - AC: Moderate efficiency
    - GC: Lower efficiency

    ABE has less pronounced context preferences.
    '''
    if position < 2 or position > len(spacer):
        return 0.5

    idx = position - 1  # 0-indexed

    if editor == 'CBE':
        if idx > 0:
            context = spacer[idx - 1]
            context_scores = {'T': 1.0, 'C': 0.8, 'A': 0.6, 'G': 0.4}
            return context_scores.get(context, 0.5)
    else:  # ABE
        # ABE is less context-dependent
        return 0.8

    return 0.5

Related Skills

• genome-engineering/grna-design - Standard Cas9 guide design
• genome-engineering/prime-editing-design - Alternative for non-C/A edits
• crispr-screens/base-editing-analysis - Analyze base editing outcomes