Skip to content
Back to skills

Primer Basics

ASecurity

Design PCR primers for a target sequence using primer3-py. Specify target regions, product size, melting temperature, and other constraints. Returns ranked primer pairs with quality metrics. Use when designing standard PCR primers.

  • 32 stars
  • 0 votes
  • 0 copies
  • 3 views
  • Added February 7, 2026
toolspythongodatabase

Security analysis

A100/100

Pro scans all 3 files and shows the line behind each finding

Scanned February 12, 2026

npx -y skills add mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill primer-basics --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Primer Basics?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Primer Basics
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/mdbabumiamssm-primer-basics/badge)](https://www.skillsdirectory.com/skills/mdbabumiamssm-primer-basics)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: bio-primer-design-primer-basics
description: Design PCR primers for a target sequence using primer3-py. Specify target regions, product size, melting temperature, and other constraints. Returns ranked primer pairs with quality metrics. Use when designing standard PCR primers.
tool_type: python
primary_tool: primer3-py
---

# PCR Primer Design

Design PCR primers using primer3-py, the Python binding for Primer3.

## Required Imports

```python
import primer3
from primer3 import p3helpers
from Bio import SeqIO
from Bio.Seq import Seq
```

## Sequence Preparation (p3helpers)

```python
# Sanitize sequence (uppercase, remove whitespace)
raw_seq = '  atgc gatc GATC  '
clean_seq = p3helpers.sanitize_sequence(raw_seq)
print(f'Cleaned: {clean_seq}')  # 'ATGCGATCGATC'

# Reverse complement for designing reverse primers
seq = 'ATGCGATCGATC'
rc_seq = p3helpers.reverse_complement(seq)
print(f'Reverse complement: {rc_seq}')  # 'GATCGATCGCAT'

# Ensure valid DNA sequence (ACGT only, uppercase)
valid_seq = p3helpers.ensure_acgt_uppercase('atgcNNgatc')  # Raises error if invalid
```

## Basic Primer Design

```python
sequence = 'ATGCGTACGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCG'

result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence},
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[100, 300]],
        'PRIMER_MIN_TM': 57.0,
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MAX_TM': 63.0,
        'PRIMER_MIN_GC': 40.0,
        'PRIMER_MAX_GC': 60.0,
    }
)
```

## Extract Primer Results

```python
num_returned = result['PRIMER_PAIR_NUM_RETURNED']
print(f'Found {num_returned} primer pairs')

for i in range(num_returned):
    left = result[f'PRIMER_LEFT_{i}_SEQUENCE']
    right = result[f'PRIMER_RIGHT_{i}_SEQUENCE']
    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'Pair {i}: {left} / {right}')
    print(f'  Tm: {left_tm:.1f}C / {right_tm:.1f}C, Product: {product_size}bp')
```

## Target a Specific Region

```python
# Target a specific region: [start, length]
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_TARGET': [100, 50],  # Target region at position 100, length 50
    },
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[150, 300]],
        'PRIMER_OPT_TM': 60.0,
    }
)
```

## Primers Must Span a Region

```python
# Primers must span this region (e.g., exon junction)
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_INCLUDED_REGION': [50, 200],  # Primers within this region
    },
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[100, 250]]}
)
```

## Exclude Regions

```python
# Exclude regions (e.g., SNP positions, repeats)
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_EXCLUDED_REGION': [[150, 20], [300, 15]],  # Regions to avoid
    },
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[100, 300]]}
)
```

## Constrain Primer Positions

```python
# Force primer to overlap a specific position
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_FORCE_LEFT_START': 50,   # Left primer must start here
        'SEQUENCE_FORCE_RIGHT_START': 250,  # Right primer must start here
    },
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[150, 250]]}
)
```

## Design for Sequencing

```python
# Single primer for sequencing
result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence},
    global_args={
        'PRIMER_PICK_LEFT_PRIMER': 1,
        'PRIMER_PICK_RIGHT_PRIMER': 0,  # Only design left primer
        'PRIMER_PICK_INTERNAL_OLIGO': 0,
        'PRIMER_OPT_SIZE': 20,
        'PRIMER_MIN_SIZE': 18,
        'PRIMER_MAX_SIZE': 25,
    }
)
```

## Full Parameter Control

```python
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_TARGET': [200, 50],
    },
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[150, 300], [300, 500]],  # Multiple ranges
        'PRIMER_NUM_RETURN': 5,
        'PRIMER_MIN_SIZE': 18,
        'PRIMER_OPT_SIZE': 20,
        'PRIMER_MAX_SIZE': 25,
        'PRIMER_MIN_TM': 57.0,
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MAX_TM': 63.0,
        'PRIMER_MIN_GC': 40.0,
        'PRIMER_OPT_GC_PERCENT': 50.0,
        'PRIMER_MAX_GC': 60.0,
        'PRIMER_MAX_POLY_X': 4,           # Max consecutive identical bases
        'PRIMER_MAX_NS_ACCEPTED': 0,       # No ambiguous bases
        'PRIMER_MAX_SELF_ANY': 8,          # Self-complementarity
        'PRIMER_MAX_SELF_END': 3,          # 3' self-complementarity
        'PRIMER_PAIR_MAX_COMPL_ANY': 8,    # Pair complementarity
        'PRIMER_PAIR_MAX_COMPL_END': 3,    # Pair 3' complementarity
        'PRIMER_MAX_END_STABILITY': 9.0,   # Max 3' end stability (delta G)
    }
)
```

## Load Sequence from FASTA

```python
from Bio import SeqIO

record = SeqIO.read('gene.fasta', 'fasta')
sequence = str(record.seq)

result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence, 'SEQUENCE_ID': record.id},
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[100, 300]], 'PRIMER_OPT_TM': 60.0}
)
```

## Calculate Tm Directly

```python
# Calculate Tm for an existing primer
tm = primer3.calc_tm('ATGCGATCGATCGATCGATC')
print(f'Tm: {tm:.1f}C')

# With custom salt/DNA concentrations
tm = primer3.calc_tm('ATGCGATCGATCGATCGATC', mv_conc=50.0, dv_conc=1.5, dntp_conc=0.2, dna_conc=50.0)
```

### Tm Calculation Defaults

| Parameter | Default | Description |
|-----------|---------|-------------|
| mv_conc | 50.0 mM | Monovalent cations (Na+, K+) |
| dv_conc | 0.0 mM | Divalent cations (Mg2+) |
| dntp_conc | 0.0 mM | dNTP concentration |
| dna_conc | 50.0 nM | DNA oligo concentration |

## Calculate Hairpin and Dimer Tm

```python
# Hairpin Tm
hairpin = primer3.calc_hairpin('ATGCGATCGATCGATCGATC')
print(f'Hairpin Tm: {hairpin.tm:.1f}C, dG: {hairpin.dg:.1f}')

# Homodimer Tm
homodimer = primer3.calc_homodimer('ATGCGATCGATCGATCGATC')
print(f'Homodimer Tm: {homodimer.tm:.1f}C, dG: {homodimer.dg:.1f}')

# Heterodimer Tm (between two different primers)
heterodimer = primer3.calc_heterodimer('ATGCGATCGATCGATCGATC', 'GCTAGCTAGCTAGCTAGCTA')
print(f'Heterodimer Tm: {heterodimer.tm:.1f}C, dG: {heterodimer.dg:.1f}')
```

## Format Results as DataFrame

```python
import pandas as pd

def primers_to_dataframe(result):
    rows = []
    for i in range(result['PRIMER_PAIR_NUM_RETURNED']):
        rows.append({
            'pair': i,
            'left_seq': result[f'PRIMER_LEFT_{i}_SEQUENCE'],
            'right_seq': result[f'PRIMER_RIGHT_{i}_SEQUENCE'],
            'left_tm': result[f'PRIMER_LEFT_{i}_TM'],
            'right_tm': result[f'PRIMER_RIGHT_{i}_TM'],
            'left_gc': result[f'PRIMER_LEFT_{i}_GC_PERCENT'],
            'right_gc': result[f'PRIMER_RIGHT_{i}_GC_PERCENT'],
            'product_size': result[f'PRIMER_PAIR_{i}_PRODUCT_SIZE'],
            'penalty': result[f'PRIMER_PAIR_{i}_PENALTY'],
        })
    return pd.DataFrame(rows)

df = primers_to_dataframe(result)
print(df)
```

## Common Global Arguments

| Parameter | Description | Default |
|-----------|-------------|---------|
| PRIMER_PRODUCT_SIZE_RANGE | Allowed product sizes | [[100,300]] |
| PRIMER_NUM_RETURN | Number of primer pairs | 5 |
| PRIMER_MIN/OPT/MAX_SIZE | Primer length | 18/20/27 |
| PRIMER_MIN/OPT/MAX_TM | Melting temperature | 57/60/63 |
| PRIMER_MIN/MAX_GC | GC content percent | 20/80 |
| PRIMER_MAX_POLY_X | Max poly-X run | 5 |
| PRIMER_MAX_SELF_ANY | Self complementarity | 8 |
| PRIMER_MAX_SELF_END | 3' self complementarity | 3 |

## Related Skills

- qpcr-primers - Design primers with internal probes for qPCR
- primer-validation - Check primers for specificity and secondary structures
- sequence-io - Load template sequences
- database-access/local-blast - BLAST primers for specificity checking

Files in this skill

  • SKILL.md7.8 KB
  • examples/primer_design.py1.7 KB
  • usage-guide.md1.7 KB

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.