bio-primer-design-qpcr-primers
$
npx mdskill add GPTomics/bioSkills/bio-primer-design-qpcr-primersDesign qPCR primers and TaqMan/molecular beacon probes using primer3-py
- Solve the task of designing primers and probes for real-time PCR assays
- Depends on primer3-py, BioPython, and pandas for sequence analysis
- Uses configurable parameters like Tm, spacing, and hydrolysis probe constraints
- Returns designed primers and probes as output for downstream use
SKILL.md
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---
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
---
## Version Compatibility
Reference examples tested with: BioPython 1.83+, pandas 2.2+, 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.
# qPCR Primer and Probe Design
Design primers and internal probes for quantitative PCR using primer3-py.
**"Design qPCR primers with probe"** → Generate primer pairs plus internal TaqMan/molecular beacon probes with constrained Tm and spacing.
- Python: `primer3.design_primers(seq_args, global_args)` with `PRIMER_PICK_INTERNAL_OLIGO=1` (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
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