본문으로 건너뛰기
cccskills
무료GitHub에 공개

bio-restriction-fragment-analysis

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(4)

  • SKILL.md7.4 KB
  • examples/gel_simulation.py1.4 KB
  • examples/predict_fragments.py1.2 KB
  • usage-guide.md3.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

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

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

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

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

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

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

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

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

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

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

alphafold

무료

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

atac-seq

무료

ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

bindcraft

무료

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

BioTender-max/awesome-bio-agent-skills2042026년 7월 2일 업데이트

BioTender-max의 스킬 모두 보기

이 스킬의 문제 신고