bio-restriction-mapping — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-restriction-mapping (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Create a restriction map of my sequence" → Visualize cut site positions for multiple enzymes along a DNA sequence with inter-site distances.
Bio.Restriction.Analysis for positions, matplotlib for graphical mapsfrom Bio import SeqIO
from Bio.Restriction import EcoRI, BamHI, HindIII, RestrictionBatch, Analysis
record = SeqIO.read('sequence.fasta', 'fasta')
seq = record.seq
batch = RestrictionBatch([EcoRI, BamHI, HindIII])
analysis = Analysis(batch, seq)
# Print formatted map
analysis.print_as('map')# Map format (visual)
analysis.print_as('map')
# Linear format (list)
analysis.print_as('linear')
# Tabular format
analysis.print_as('tabulate')
# Get as string instead of printing
map_str = analysis.format_as('map')
linear_str = analysis.format_as('linear')from Bio.Restriction import EcoRI, BamHI
ecori_sites = EcoRI.search(seq)
bamhi_sites = BamHI.search(seq)
# All cut positions sorted
all_sites = sorted(ecori_sites + bamhi_sites)
# Calculate distances between consecutive sites
distances = []
for i in range(len(all_sites) - 1):
dist = all_sites[i + 1] - all_sites[i]
distances.append((all_sites[i], all_sites[i + 1], dist))
print(f'{all_sites[i]} -> {all_sites[i + 1]}: {dist} bp')from Bio import SeqIO
from Bio.Restriction import RestrictionBatch, Analysis
from Bio.Restriction import EcoRI, BamHI, HindIII, XhoI, NotI
record = SeqIO.read('plasmid.fasta', 'fasta')
seq = record.seq
seq_len = len(seq)
enzymes = RestrictionBatch([EcoRI, BamHI, HindIII, XhoI, NotI])
analysis = Analysis(enzymes, seq, linear=False)
print(f'Restriction Map: {record.id}')
print(f'Length: {seq_len} bp (circular)')
print('=' * 50)
results = analysis.full()
all_cuts = []
for enzyme, sites in results.items():
for site in sites:
all_cuts.append((site, str(enzyme)))
all_cuts.sort(key=lambda x: x[0])
print('\nCut sites (5\' -> 3\'):')
for pos, enz in all_cuts:
pct = (pos / seq_len) * 100
print(f' {pos:6d} bp ({pct:5.1f}%) - {enz}')def draw_restriction_map(seq, results, width=80):
'''Draw a simple text restriction map'''
seq_len = len(seq)
scale = width / seq_len
# Header
print(f'0{" " * (width - 6)}{seq_len}')
print('|' + '-' * (width - 2) + '|')
# Plot each enzyme
for enzyme, sites in results.items():
if not sites:
continue
line = [' '] * width
for site in sites:
pos = int(site * scale)
if pos >= width:
pos = width - 1
line[pos] = '|'
print(''.join(line) + f' {enzyme}')
print('|' + '-' * (width - 2) + '|')
# Usage
batch = RestrictionBatch([EcoRI, BamHI, HindIII])
analysis = Analysis(batch, seq)
results = analysis.full()
draw_restriction_map(seq, results)from Bio import SeqIO
from Bio.Restriction import RestrictionBatch, Analysis, EcoRI, BamHI
record = SeqIO.read('plasmid.gb', 'genbank')
seq = record.seq
enzymes = RestrictionBatch([EcoRI, BamHI])
analysis = Analysis(enzymes, seq, linear=False)
results = analysis.full()
print('Restriction Sites and Overlapping Features:')
print('=' * 60)
for enzyme, sites in results.items():
for site in sites:
print(f'\n{enzyme} at position {site}:')
for feature in record.features:
start = int(feature.location.start)
end = int(feature.location.end)
if start <= site <= end:
feat_type = feature.type
label = feature.qualifiers.get('label', feature.qualifiers.get('gene', ['unknown']))[0]
print(f' Within {feat_type}: {label} ({start}-{end})')def export_restriction_map(seq, results, output_file, seq_name='sequence'):
'''Export restriction map to text file'''
with open(output_file, 'w') as f:
f.write(f'Restriction Map: {seq_name}\n')
f.write(f'Length: {len(seq)} bp\n')
f.write('=' * 50 + '\n\n')
all_cuts = []
for enzyme, sites in results.items():
for site in sites:
all_cuts.append((site, str(enzyme)))
all_cuts.sort()
f.write('Site\tPosition\tFrom_Start\n')
for pos, enz in all_cuts:
f.write(f'{enz}\t{pos}\t{pos}\n')
f.write('\n\nFragment sizes between sites:\n')
if all_cuts:
positions = sorted([c[0] for c in all_cuts])
for i in range(len(positions) - 1):
size = positions[i + 1] - positions[i]
f.write(f'{positions[i]} -> {positions[i + 1]}: {size} bp\n')
# Usage
export_restriction_map(seq, results, 'restriction_map.txt', record.id)def circular_distances(sites, seq_len):
'''Calculate fragment sizes for circular DNA'''
if not sites:
return []
sites = sorted(sites)
fragments = []
# Between consecutive sites
for i in range(len(sites) - 1):
fragments.append(sites[i + 1] - sites[i])
# Wrap-around fragment
wrap = (seq_len - sites[-1]) + sites[0]
fragments.append(wrap)
return fragments
# Usage
ecori_sites = EcoRI.search(seq, linear=False)
fragments = circular_distances(ecori_sites, len(seq))
print(f'EcoRI fragments (circular): {fragments}')~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.