Agent skill

bio-primer-design-qpcr-primers

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-primer-design-qpcr-primers

SKILL.md

name: bio-primer-design-qpcr-primers description: Design qPCR primers and TaqMan/molecular beacon probes using primer3-py. Configure probe Tm, primer-probe spacing, and hydrolysis probe constraints for real-time PCR assays. Use when designing qPCR primers and probes. tool_type: python primary_tool: primer3-py measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

qPCR Primer and Probe Design

Design primers and internal probes for quantitative PCR using primer3-py.

Required Imports

python
import primer3
from Bio import SeqIO

Design Primers with TaqMan Probe

python
sequence = 'ATGCGTACGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCG' * 3

result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence},
    global_args={
        'PRIMER_PICK_LEFT_PRIMER': 1,
        'PRIMER_PICK_RIGHT_PRIMER': 1,
        'PRIMER_PICK_INTERNAL_OLIGO': 1,  # Design internal probe
        'PRIMER_PRODUCT_SIZE_RANGE': [[70, 150]],  # Short amplicons for qPCR
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MIN_TM': 58.0,
        'PRIMER_MAX_TM': 62.0,
        'PRIMER_INTERNAL_OPT_TM': 70.0,  # Probe Tm ~10C higher
        'PRIMER_INTERNAL_MIN_TM': 68.0,
        'PRIMER_INTERNAL_MAX_TM': 72.0,
        'PRIMER_INTERNAL_MIN_SIZE': 18,
        'PRIMER_INTERNAL_OPT_SIZE': 25,
        'PRIMER_INTERNAL_MAX_SIZE': 30,
    }
)

Extract Probe Results

python
num_returned = result['PRIMER_PAIR_NUM_RETURNED']
print(f'Found {num_returned} primer/probe sets')

for i in range(num_returned):
    left = result[f'PRIMER_LEFT_{i}_SEQUENCE']
    right = result[f'PRIMER_RIGHT_{i}_SEQUENCE']
    probe = result[f'PRIMER_INTERNAL_{i}_SEQUENCE']
    probe_tm = result[f'PRIMER_INTERNAL_{i}_TM']
    left_tm = result[f'PRIMER_LEFT_{i}_TM']
    right_tm = result[f'PRIMER_RIGHT_{i}_TM']
    product_size = result[f'PRIMER_PAIR_{i}_PRODUCT_SIZE']

    print(f'Set {i}:')
    print(f'  Forward: {left} (Tm: {left_tm:.1f}C)')
    print(f'  Reverse: {right} (Tm: {right_tm:.1f}C)')
    print(f'  Probe:   {probe} (Tm: {probe_tm:.1f}C)')
    print(f'  Product: {product_size}bp')

qPCR-Optimized Parameters

python
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_TARGET': [100, 30],  # Target region for probe
    },
    global_args={
        'PRIMER_PICK_INTERNAL_OLIGO': 1,
        'PRIMER_PRODUCT_SIZE_RANGE': [[60, 100], [100, 150]],  # Prefer short
        'PRIMER_NUM_RETURN': 5,
        # Primer parameters
        'PRIMER_OPT_SIZE': 20,
        'PRIMER_MIN_SIZE': 18,
        'PRIMER_MAX_SIZE': 25,
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MIN_TM': 58.0,
        'PRIMER_MAX_TM': 62.0,
        'PRIMER_OPT_GC_PERCENT': 50.0,
        'PRIMER_MIN_GC': 35.0,
        'PRIMER_MAX_GC': 65.0,
        # Probe parameters (TaqMan: Tm 8-10C higher than primers)
        'PRIMER_INTERNAL_OPT_SIZE': 25,
        'PRIMER_INTERNAL_MIN_SIZE': 18,
        'PRIMER_INTERNAL_MAX_SIZE': 30,
        'PRIMER_INTERNAL_OPT_TM': 70.0,
        'PRIMER_INTERNAL_MIN_TM': 68.0,
        'PRIMER_INTERNAL_MAX_TM': 72.0,
        'PRIMER_INTERNAL_MIN_GC': 30.0,
        'PRIMER_INTERNAL_MAX_GC': 70.0,
        # Avoid G at 5' end of probe (quenches FAM)
        'PRIMER_INTERNAL_MAX_SELF_ANY': 8,
    }
)

TaqMan Probe Constraints

python
# Additional considerations for TaqMan probes
global_args = {
    'PRIMER_PICK_INTERNAL_OLIGO': 1,
    'PRIMER_PRODUCT_SIZE_RANGE': [[70, 150]],
    # Probe Tm should be 8-10C higher than primers
    'PRIMER_OPT_TM': 60.0,
    'PRIMER_INTERNAL_OPT_TM': 70.0,
    # Probe should be closer to forward primer
    'PRIMER_INTERNAL_MIN_SIZE': 18,
    'PRIMER_INTERNAL_MAX_SIZE': 30,
    # Avoid long poly-X runs in probe
    'PRIMER_INTERNAL_MAX_POLY_X': 3,
}

SYBR Green Primers (No Probe)

python
# For SYBR Green, design primers without probe
result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence},
    global_args={
        'PRIMER_PICK_LEFT_PRIMER': 1,
        'PRIMER_PICK_RIGHT_PRIMER': 1,
        'PRIMER_PICK_INTERNAL_OLIGO': 0,  # No probe
        'PRIMER_PRODUCT_SIZE_RANGE': [[70, 200]],  # Short for qPCR
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MIN_TM': 58.0,
        'PRIMER_MAX_TM': 62.0,
        'PRIMER_MAX_SELF_ANY': 4,  # Strict for SYBR specificity
        'PRIMER_MAX_SELF_END': 2,
        'PRIMER_PAIR_MAX_COMPL_ANY': 4,
        'PRIMER_PAIR_MAX_COMPL_END': 2,
    }
)

Design for Exon-Spanning (Avoid Genomic DNA)

python
# For cDNA-specific amplification, target exon junction
# Mark the exon junction position
exon_junction = 150  # Position where exons meet

result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_OVERLAP_JUNCTION_LIST': [exon_junction],  # Primer must span
    },
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[70, 150]],
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MIN_3_PRIME_OVERLAP_OF_JUNCTION': 4,  # Min bases on each side
    }
)

Multiplex Primer Design

python
# Design primers for multiple targets with compatible Tms
targets = [
    {'name': 'gene1', 'seq': sequence1, 'target': [100, 30]},
    {'name': 'gene2', 'seq': sequence2, 'target': [150, 30]},
]

results = []
for target in targets:
    result = primer3.design_primers(
        seq_args={
            'SEQUENCE_TEMPLATE': target['seq'],
            'SEQUENCE_ID': target['name'],
            'SEQUENCE_TARGET': target['target'],
        },
        global_args={
            'PRIMER_PICK_INTERNAL_OLIGO': 1,
            'PRIMER_PRODUCT_SIZE_RANGE': [[70, 150]],
            'PRIMER_OPT_TM': 60.0,  # Same Tm for all
            'PRIMER_MAX_TM': 61.0,
            'PRIMER_MIN_TM': 59.0,
            'PRIMER_INTERNAL_OPT_TM': 70.0,
        }
    )
    results.append(result)

Validate Tm Calculations

python
# Verify Tm with primer3's thermodynamic calculations
primer_seq = 'ATGCGATCGATCGATCGATC'

# Standard Tm
tm = primer3.calc_tm(primer_seq)
print(f'Standard Tm: {tm:.1f}C')

# Tm with specific salt conditions (match your qPCR master mix)
tm_adjusted = primer3.calc_tm(
    primer_seq,
    mv_conc=50.0,    # Monovalent cation (K+, Na+) mM
    dv_conc=3.0,     # Divalent cation (Mg2+) mM
    dntp_conc=0.8,   # dNTP mM (reduces free Mg2+)
    dna_conc=250.0,  # Primer concentration nM
)
print(f'Adjusted Tm: {tm_adjusted:.1f}C')

Format qPCR Results

python
import pandas as pd

def qpcr_results_to_df(result):
    rows = []
    for i in range(result['PRIMER_PAIR_NUM_RETURNED']):
        row = {
            'pair': i,
            'forward': result[f'PRIMER_LEFT_{i}_SEQUENCE'],
            'reverse': result[f'PRIMER_RIGHT_{i}_SEQUENCE'],
            'fwd_tm': result[f'PRIMER_LEFT_{i}_TM'],
            'rev_tm': result[f'PRIMER_RIGHT_{i}_TM'],
            'product_size': result[f'PRIMER_PAIR_{i}_PRODUCT_SIZE'],
        }
        if f'PRIMER_INTERNAL_{i}_SEQUENCE' in result:
            row['probe'] = result[f'PRIMER_INTERNAL_{i}_SEQUENCE']
            row['probe_tm'] = result[f'PRIMER_INTERNAL_{i}_TM']
        rows.append(row)
    return pd.DataFrame(rows)

df = qpcr_results_to_df(result)
print(df)

qPCR Design Guidelines

Parameter Primers TaqMan Probe
Length 18-25 bp 18-30 bp
Tm 58-62C 68-72C
GC% 35-65% 30-70%
Amplicon 70-150 bp -
5' base Any Avoid G (quenches FAM)

Related Skills

  • primer-basics - General PCR primer design
  • primer-validation - Check primers for dimers and specificity
  • sequence-manipulation - Work with cDNA sequences

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results