Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Analyze DNA/RNA/protein sequences. Use when the user provides a sequence and asks for analysis, translation, GC content, ORFs, motifs, restriction sites, or primer design. Triggers on "sequence", "translate", "GC content", "ORF", "primer", "restriction", "complement", "reverse complement".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Comprehensive sequence analysis using BioPython and command-line tools.
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight
seq = Seq("ATGCGATCGATCGATCG...")
print(f"Length: {len(seq)} bp")
print(f"GC Content: {gc_fraction(seq)*100:.1f}%")
print(f"Complement: {seq.complement()}")
print(f"Reverse Complement: {seq.reverse_complement()}")
print(f"Protein: {seq.translate()}")
from Bio.Seq import Seq
def find_orfs(sequence, min_length=100):
orfs = []
seq = Seq(str(sequence))
for strand, nuc in [("+", seq), ("-", seq.reverse_complement())]:
for frame in range(3):
trans = nuc[frame:].translate()
aa_seq = str(trans)
start = 0
while start < len(aa_seq):
m_pos = aa_seq.find("M", start)
if m_pos == -1:
break
stop_pos = aa_seq.find("*", m_pos)
if stop_pos == -1:
stop_pos = len(aa_seq)
orf_len = (stop_pos - m_pos) * 3
if orf_len >= min_length:
nt_start = frame + m_pos * 3
orfs.append({
"strand": strand,
"frame": frame + 1,
"start": nt_start,
"length_aa": stop_pos - m_pos,
"length_nt": orf_len,
"protein": aa_seq[m_pos:stop_pos]
})
start = stop_pos + 1
return sorted(orfs, key=lambda x: x["length_nt"], reverse=True)
from Bio.Restriction import RestrictionBatch, Analysis
from Bio.Seq import Seq
seq = Seq("ATGCGATCGATCG...")
rb = RestrictionBatch(["EcoRI", "BamHI", "HindIII", "NotI", "XhoI"])
ana = Analysis(rb, seq)
results = ana.full()
for enzyme, sites in results.items():
if sites:
print(f"{enzyme}: cuts at positions {sites}")
from Bio.Seq import Seq
from Bio.SeqUtils import MeltingTemp as mt
def design_primers(seq_str, product_size_range=(200, 800)):
seq = Seq(seq_str)
# Forward primer (first 20bp)
fwd = seq[:20]
fwd_tm = mt.Tm_NN(fwd)
# Reverse primer (last 20bp, reverse complement)
rev = seq[-20:].reverse_complement()
rev_tm = mt.Tm_NN(rev)
print(f"Forward: 5'-{fwd}-3' (Tm={fwd_tm:.1f}°C, GC={gc_fraction(fwd)*100:.0f}%)")
print(f"Reverse: 5'-{rev}-3' (Tm={rev_tm:.1f}°C, GC={gc_fraction(rev)*100:.0f}%)")
print(f"Product size: {len(seq)} bp")
If user provides multiple sequences:
# Write sequences to FASTA file
cat > /tmp/sequences.fa << 'EOF'
>seq1
ATGCGATCG...
>seq2
ATGCAATCG...
EOF
# If clustalw/muscle available, use them
# Otherwise use BioPython's pairwise alignment
from Bio import pairwise2
from Bio.pairwise2 import format_alignment
alignments = pairwise2.align.globalxx(seq1, seq2)
print(format_alignment(*alignments[0]))
*Sequence Analysis Results*
• Length: 1,234 bp
• GC Content: 52.3%
• ORFs found: 3 (longest: 456 aa)
*Protein Translation (frame +1):*
```MRSSIDLK...STOP```
*Restriction Sites:*
• EcoRI: positions 123, 456
• BamHI: position 789
• HindIII: no sites found
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
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.
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
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.