Skip to content
cccskills
FreePublished on GitHub

bio-genome-engineering-hdr-template-design

Design donor DNA templates for precise CRISPR insertions or sequence replacement. Choose between single-stranded DNA, double-stranded DNA, and plasmid donors, then construct matching flanking sequences and primers for amplification or assembly.

OriginalShow the original description

Design homology-directed repair donor templates for CRISPR knock-ins using primer3-py. Create ssODN, dsDNA, or plasmid templates with optimized homology arms. Use when designing donor templates for precise insertions, tagging, or allele replacement.

Run score Not tested

See how to install

Good for

  • Designing donors for point mutations or small insertions
  • Planning donor sequences for protein tagging
  • Choosing donor formats for larger insertions
  • Designing primers for donor amplification and assembly

Files included (3)

  • SKILL.md8.7 KB
  • examples/hdr_template_design.py6.5 KB
  • usage-guide.md1.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Version Compatibility

Reference examples tested with: BioPython 1.83+, 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

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

HDR Template Design

"Design a donor template for my CRISPR knock-in" → Create homology-directed repair templates (ssODN, dsDNA, or plasmid) with optimized homology arm lengths and silent PAM mutations, using primer3 for flanking primer design.

  • Python: primer3.bindings.design_primers() (primer3-py) for primer/arm design, Bio.Seq for template construction

Template Types

ssODN (single-stranded oligodeoxynucleotide):
- Length: 100-200nt total
- Homology arms: 30-60nt each side
- Best for: Small insertions (<50bp), point mutations
- Delivery: Electroporation with RNP

dsDNA (double-stranded DNA):
- Length: 500bp - 5kb total
- Homology arms: 200-800bp each side
- Best for: Larger insertions (tags, reporters)
- Delivery: Plasmid or PCR product

Plasmid donor:
- Homology arms: 500-2000bp
- Best for: Large insertions (>1kb), conditional alleles
- Delivery: Transfection

ssODN Design

from Bio.Seq import Seq

def design_ssodn(target_seq, cut_site, insert_seq='', arm_length=50):
    '''Design single-stranded oligo donor for HDR

    Args:
        target_seq: Genomic sequence around cut site
        cut_site: Position of Cas9 cut (3bp upstream of PAM)
        insert_seq: Sequence to insert (empty for deletion/mutation)
        arm_length: Length of each homology arm (30-60nt optimal)

    ssODN considerations:
    - Total length should be 100-200nt (synthesis limit)
    - Asymmetric arms can improve HDR (PAM-distal shorter)
    - Strand choice: complementary to non-target strand often better
    '''
    # Extract homology arms
    left_arm = target_seq[cut_site - arm_length:cut_site]
    right_arm = target_seq[cut_site:cut_site + arm_length]

    # Assemble ssODN
    ssodn = left_arm + insert_seq + right_arm

    # Also provide reverse complement (may work better)
    ssodn_rc = str(Seq(ssodn).reverse_complement())

    return {
        'sense': ssodn,
        'antisense': ssodn_rc,
        'length': len(ssodn),
        'left_arm_length': len(left_arm),
        'right_arm_length': len(right_arm),
        'insert_length': len(insert_seq)
    }


def design_ssodn_mutation(target_seq, mutation_pos, new_base, arm_length=50):
    '''Design ssODN for a point mutation

    For point mutations, center the mutation in the ssODN.
    Also introduce silent PAM mutation to prevent re-cutting.
    '''
    # Build mutant sequence
    mutant = list(target_seq)
    mutant[mutation_pos] = new_base
    mutant_seq = ''.join(mutant)

    # Extract arms around mutation
    left_start = mutation_pos - arm_length
    right_end = mutation_pos + arm_length + 1

    ssodn = mutant_seq[left_start:right_end]

    return {
        'sequence': ssodn,
        'length': len(ssodn),
        'mutation_position_in_ssodn': arm_length,
        'original_base': target_seq[mutation_pos],
        'new_base': new_base
    }

Asymmetric Arm Design

def design_asymmetric_ssodn(target_seq, cut_site, insert_seq, pam_position):
    '''Design ssODN with asymmetric homology arms

    Asymmetric arms can improve HDR efficiency:
    - PAM-proximal arm: 30-40nt (shorter)
    - PAM-distal arm: 60-90nt (longer)

    The longer arm is on the side that gets resected first.
    '''
    # Determine which side is PAM-proximal
    if pam_position > cut_site:  # PAM is to the right
        left_arm_length = 70   # PAM-distal (longer)
        right_arm_length = 35  # PAM-proximal (shorter)
    else:  # PAM is to the left
        left_arm_length = 35
        right_arm_length = 70

    left_arm = target_seq[cut_site - left_arm_length:cut_site]
    right_arm = target_seq[cut_site:cut_site + right_arm_length]

    ssodn = left_arm + insert_seq + right_arm

    return {
        'sequence': ssodn,
        'length': len(ssodn),
        'left_arm_length': left_arm_length,
        'right_arm_length': right_arm_length,
        'asymmetry': 'PAM-distal longer'
    }

dsDNA Donor Design

Goal: Design a double-stranded DNA donor template with long homology arms for larger CRISPR knock-in insertions, along with PCR primers for amplification.

Approach: Extract left and right homology arms of specified length flanking the cut site, concatenate with the insert sequence, then design PCR primers for the arms and Gibson assembly overlap primers that span the arm-insert junctions.

def design_dsdna_donor(target_seq, cut_site, insert_seq, arm_length=500):
    '''Design double-stranded DNA donor for larger insertions

    Args:
        target_seq: Extended genomic sequence (need ~2kb around cut)
        cut_site: Position of Cas9 cut
        insert_seq: Sequence to insert (tag, reporter, etc.)
        arm_length: Homology arm length (200-800bp recommended)

    For PCR amplification, returns primer sequences for arms.
    '''
    left_arm = target_seq[cut_site - arm_length:cut_site]
    right_arm = target_seq[cut_site:cut_site + arm_length]

    donor = left_arm + insert_seq + right_arm

    return {
        'sequence': donor,
        'length': len(donor),
        'left_arm': left_arm,
        'right_arm': right_arm,
        'insert': insert_seq
    }


def design_pcr_primers_for_donor(left_arm, right_arm, insert_seq, tm_target=60):
    '''Design PCR primers to amplify HDR donor

    Creates primers for Gibson assembly or overlap PCR.
    '''
    # Forward primer: 5' end of left arm
    fwd_primer = left_arm[:20]

    # Reverse primer: 3' end of right arm (reverse complement)
    rev_primer = str(Seq(right_arm[-20:]).reverse_complement())

    # Overlap primers for Gibson assembly
    # Left arm reverse with insert overhang
    left_overlap = str(Seq(left_arm[-20:]).reverse_complement()) + insert_seq[:15]

    # Right arm forward with insert overhang
    right_overlap = insert_seq[-15:] + right_arm[:20]

    return {
        'left_arm_fwd': fwd_primer,
        'left_arm_rev': left_overlap,
        'right_arm_fwd': right_overlap,
        'right_arm_rev': rev_primer
    }

Common Insertions

# Common tag sequences for knock-ins
TAGS = {
    'FLAG': 'GATTACAAGGATGACGATGACAAG',
    '3xFLAG': 'GATTACAAGGATGACGATGACAAGGATTACAAGGATGACGATGACAAGGATTACAAGGATGACGATGACAAG',
    'HA': 'TACCCATACGATGTTCCAGATTACGCT',
    'V5': 'GGTAAGCCTATCCCTAACCCTCTCCTCGGTCTCGATTCTACG',
    'MYC': 'GAACAAAAACTCATCTCAGAAGAGGATCTG',
    '6xHIS': 'CATCACCATCACCATCAC',
    'GFP_LINKER': 'GGCGGAGGCGGAAGC',  # Flexible linker before GFP
}

def design_tag_insertion(target_seq, cut_site, tag_name, position='C-term'):
    '''Design HDR donor for protein tagging

    Args:
        position: 'N-term' or 'C-term' relative to target gene

    For C-terminal tagging, insert before stop codon.
    For N-terminal tagging, insert after start codon.
    '''
    tag_seq = TAGS.get(tag_name, tag_name)  # Use custom if not in dict

    # Add linker if needed
    if position == 'C-term':
        insert = TAGS['GFP_LINKER'] + tag_seq  # Linker before tag
    else:
        insert = tag_seq + TAGS['GFP_LINKER']  # Tag then linker

    return design_ssodn(target_seq, cut_site, insert)

PAM Mutation to Prevent Re-cutting

def add_silent_pam_mutation(donor_seq, pam_position, codon_table='standard'):
    '''Add silent mutation to disrupt PAM in donor

    After HDR, the PAM should be disrupted to prevent Cas9
    from cutting the edited allele.

    Strategy:
    - If PAM (NGG) is in coding region, make synonymous change
    - Change GG to GA, GC, or GT (no longer recognized)
    - Ensure the mutation is synonymous (silent)
    '''
    donor = list(donor_seq)

    # NGG PAM - mutate second G to A (most common silent option)
    if pam_position + 2 < len(donor):
        if donor[pam_position + 1:pam_position + 3] == ['G', 'G']:
            # Try GG -> GA (often silent in 3rd codon position)
            donor[pam_position + 2] = 'A'

    return ''.join(donor)

Related Skills

  • genome-engineering/grna-design - Design guide to create cut site
  • primer-design/primer-basics - PCR primer design for cloning
  • sequence-io/read-sequences - Read and parse GenBank features

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

adaptyv

FreeSummary

Support protein design with cloud laboratory testing for binding, expression, thermal stability, and enzyme activity. Covers sequence optimization, submitting experiments through the Adaptyv API, checking progress, and downloading experimental results.

  • Validate designed proteins with laboratory assays
  • Screen and redesign sequences for better solubility
  • Submit protein testing experiments through an API
FreedomIntelligence/OpenClaw-Medical-Skills3.1KUpdated Jul 21, 2026

Build flexible daily plans that account for ADHD-related difficulties with time estimation and getting started. Narrow priorities to three tasks, add transition buffers, and choose small next actions that match the energy available that day.

  • Choosing three priorities for today
  • Planning a schedule with transition buffers
  • Finding a small first step when starting feels difficult
FreedomIntelligence/OpenClaw-Medical-Skills3.1KUpdated Jul 21, 2026

aeon

FreeSummary

Build Python workflows for data recorded over time, including classification, forecasting, anomaly detection, and pattern search. Using aeon, the skill guides preprocessing, algorithm selection, feature extraction, and evaluation through interfaces compatible with scikit-learn.

  • Classify time series by their patterns
  • Forecast future values from historical data
  • Detect anomalies and change points
FreedomIntelligence/OpenClaw-Medical-Skills3.1KUpdated Jul 21, 2026

agent-browser

FreeSummary

Use a browser to research topics, extract page information, fill forms, and test websites. Guides agents through identifying page controls, interacting with web apps, saving screenshots or PDFs, and reusing saved login sessions.

  • Researching topics and reading web articles
  • Filling and submitting online forms
  • Extracting page text and links
FreedomIntelligence/OpenClaw-Medical-Skills3.1KUpdated Jul 21, 2026

ai-analyzer

FreeSummary

Combine health measurements, exercise, sleep, nutrition, and mental health records to examine trends, unusual changes, and estimated risks. The skill produces personalized suggestions and text or interactive HTML reports using local data without external API calls.

  • Review health records together in a comprehensive report
  • Explore links between sleep, mood, exercise, and weight
  • Summarize estimated disease and nutrition risks
FreedomIntelligence/OpenClaw-Medical-Skills3.1KUpdated Jul 21, 2026

alphafold

FreeSummary

Predict structures for designed protein sequences and protein complexes with AlphaFold2 or ColabFold. Inspect structure files and confidence scores to assess folding and chain interfaces, with guidance for local runs, Modal execution, and common prediction errors.

  • Check predicted folding of a designed protein sequence
  • Predict a binder–target protein complex
  • Review structure and interface confidence scores
FreedomIntelligence/OpenClaw-Medical-Skills3.1KUpdated Jul 21, 2026

See all skills by FreedomIntelligence

Report a problem with this skill