Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。