Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-genome-engineering-base-editing-design

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-genome-engineering-base-editing-design🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epidemiological & causal genomicsdesign

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-genome-engineering-base-editing-design

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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

Source Doc

Excerpt From SKILL.md

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

## 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_spacer']

    if editor == 'CBE':
        efficiency = CBE_POSITION_EFFICIENCY.get(pos, 0.05)
    else:  # ABE
        efficiency = ABE_POSITION_EFFICIENCY.get(pos, 0.05)

    return efficiency