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Fragment Analysis

ASecurity

Analyze restriction digest fragments using Biopython Bio.Restriction. Predict fragment sizes, get fragment sequences, simulate gel electrophoresis patterns, and perform double digests. Use when analyzing restriction digest fragment patterns.

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Added 9/22/2026
developmentpythonapi

Works with

terminalapi

Security Analysis

A100/100

Scanned 9/22/2026

Install to Claude Code

$npx -y skills add peacezha/HPClaw --skill fragment-analysis --agent claude-code

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SKILL.md
---
name: bio-restriction-fragment-analysis
description: Analyze restriction digest fragments using Biopython Bio.Restriction. Predict fragment sizes, get fragment sequences, simulate gel electrophoresis patterns, and perform double digests. Use when analyzing restriction digest fragment patterns.
tool_type: python
primary_tool: Bio.Restriction
---

## Version Compatibility

Reference examples tested with: BioPython 1.83+

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.

# Fragment Analysis

**"Predict fragment sizes from a restriction digest"** -> Simulate enzyme digestion to get fragment lengths, sequences, and gel electrophoresis patterns including double digests.
- Python: `Bio.Restriction` analysis with `catalyze()` for fragment details

## Get Fragment Sizes

```python
from Bio import SeqIO
from Bio.Restriction import EcoRI

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

# catalyze() returns tuple: (fragments_5prime, fragments_3prime)
# For standard use, take the first element
fragments = EcoRI.catalyze(seq)[0]

# fragments is tuple of Seq objects
sizes = [len(f) for f in fragments]
print(f'Fragment sizes: {sorted(sizes, reverse=True)}')
```

## Linear vs Circular Digestion

```python
from Bio.Restriction import EcoRI

# Linear DNA
fragments_linear = EcoRI.catalyze(seq, linear=True)[0]

# Circular DNA (plasmid)
fragments_circular = EcoRI.catalyze(seq, linear=False)[0]

# Circular produces one fewer fragment (ends join)
print(f'Linear: {len(fragments_linear)} fragments')
print(f'Circular: {len(fragments_circular)} fragments')
```

## Get Fragment Sequences

```python
from Bio.Restriction import EcoRI

fragments = EcoRI.catalyze(seq)[0]

for i, frag in enumerate(fragments, 1):
    print(f'Fragment {i}: {len(frag)} bp')
    print(f'  5\' end: {frag[:20]}...')
    print(f'  3\' end: ...{frag[-20:]}')
```

## Double Digest

```python
from Bio.Restriction import EcoRI, BamHI, RestrictionBatch

# Method 1: Sequential digestion
frags_ecori = EcoRI.catalyze(seq)[0]
final_fragments = []
for frag in frags_ecori:
    sub_frags = BamHI.catalyze(frag)[0]
    final_fragments.extend(sub_frags)

# Method 2: Using RestrictionBatch
batch = RestrictionBatch([EcoRI, BamHI])
# Note: RestrictionBatch doesn't have catalyze, use Analysis

# Method 3: Manual calculation from positions
ecori_sites = EcoRI.search(seq)
bamhi_sites = BamHI.search(seq)
all_sites = sorted(set(ecori_sites + bamhi_sites))

fragment_sizes = []
for i in range(len(all_sites) - 1):
    fragment_sizes.append(all_sites[i + 1] - all_sites[i])
# Add terminal fragments
fragment_sizes.insert(0, all_sites[0])
fragment_sizes.append(len(seq) - all_sites[-1])
```

## Calculate Fragment Sizes from Positions

```python
def fragments_from_positions(seq_len, cut_positions, linear=True):
    '''Calculate fragment sizes from cut positions'''
    if not cut_positions:
        return [seq_len]

    positions = sorted(cut_positions)
    fragments = []

    if linear:
        # First fragment: start to first cut
        fragments.append(positions[0])
        # Middle fragments
        for i in range(len(positions) - 1):
            fragments.append(positions[i + 1] - positions[i])
        # Last fragment: last cut to end
        fragments.append(seq_len - positions[-1])
    else:
        # Circular: all fragments between cuts
        for i in range(len(positions) - 1):
            fragments.append(positions[i + 1] - positions[i])
        # Wrap-around fragment
        fragments.append((seq_len - positions[-1]) + positions[0])

    return fragments

# Usage
sites = EcoRI.search(seq)
sizes = fragments_from_positions(len(seq), sites, linear=True)
print(f'Fragment sizes: {sorted(sizes, reverse=True)}')
```

## Simulate Gel Pattern

```python
def simulate_gel(fragment_sizes, ladder=None):
    '''Print a text-based gel simulation'''
    if ladder is None:
        ladder = [10000, 8000, 6000, 5000, 4000, 3000, 2000, 1500, 1000, 750, 500, 250]

    max_size = max(max(fragment_sizes), max(ladder))

    print('Ladder  |  Digest')
    print('-' * 30)

    for size in sorted(ladder + fragment_sizes, reverse=True):
        ladder_mark = f'{size:>6}' if size in ladder else '      '
        digest_mark = '====' if size in fragment_sizes else ''
        print(f'{ladder_mark}  |  {digest_mark}')

# Usage
sizes = [len(f) for f in EcoRI.catalyze(seq)[0]]
simulate_gel(sizes)
```

## Detailed Fragment Report

```python
from Bio.Restriction import EcoRI, BamHI

def fragment_report(seq, enzyme, linear=True):
    '''Generate detailed fragment analysis'''
    sites = enzyme.search(seq, linear=linear)
    fragments = enzyme.catalyze(seq, linear=linear)[0]

    print(f'Enzyme: {enzyme}')
    print(f'Recognition site: {enzyme.site}')
    print(f'Number of sites: {len(sites)}')
    print(f'Cut positions: {sites}')
    print(f'\nFragments ({len(fragments)}):')

    sizes = sorted([len(f) for f in fragments], reverse=True)
    total = sum(sizes)

    for i, size in enumerate(sizes, 1):
        pct = (size / total) * 100
        print(f'  {i}. {size:6d} bp ({pct:5.1f}%)')

    print(f'\nTotal: {total} bp')
    return sizes

# Usage
sizes = fragment_report(seq, EcoRI)
```

## Compare Expected vs Observed Fragments

```python
def compare_fragments(expected, observed, tolerance=50):
    '''Compare expected fragment sizes with observed (from gel)'''
    matched = []
    unmatched_exp = list(expected)
    unmatched_obs = list(observed)

    for exp in expected:
        for obs in observed:
            if abs(exp - obs) <= tolerance:
                matched.append((exp, obs))
                if exp in unmatched_exp:
                    unmatched_exp.remove(exp)
                if obs in unmatched_obs:
                    unmatched_obs.remove(obs)
                break

    print('Matched fragments:')
    for exp, obs in matched:
        print(f'  Expected: {exp}, Observed: {obs}')

    if unmatched_exp:
        print(f'\nMissing (expected but not observed): {unmatched_exp}')
    if unmatched_obs:
        print(f'\nExtra (observed but not expected): {unmatched_obs}')

# Usage
expected = [3000, 2000, 1500, 500]
observed = [3050, 2000, 1480, 510, 200]  # From gel
compare_fragments(expected, observed)
```

## Fragment with Sequence Context

```python
from Bio.Restriction import EcoRI

def annotated_fragments(seq, enzyme, context=50):
    '''Get fragments with surrounding sequence context'''
    sites = enzyme.search(seq)
    fragments = enzyme.catalyze(seq)[0]

    print(f'{enzyme} digest ({len(fragments)} fragments):')

    for i, (frag, site) in enumerate(zip(fragments, [0] + sites), 1):
        print(f'\nFragment {i}: {len(frag)} bp (starts at {site})')
        print(f"  5' sequence: {str(frag[:context])}...")
        print(f"  3' sequence: ...{str(frag[-context:])}")

# Usage
annotated_fragments(seq, EcoRI)
```

## Notes

- **catalyze() returns tuple** - use `[0]` to get 5' fragments
- **Fragment order** - fragments returned in 5' to 3' order
- **Circular DNA** - produces n fragments from n cuts (not n+1)
- **Double digest** - combine cut positions, then calculate fragments

## Related Skills

- restriction-sites - Find cut positions for fragment calculation
- restriction-mapping - Visualize fragment positions
- enzyme-selection - Choose enzymes for desired fragments

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