Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Run BLAST sequence similarity searches. Use when the user asks to BLAST a sequence, find similar sequences, identify a gene/protein, or do homology search. Triggers on "blast", "sequence similarity", "homology", "identify sequence".
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Run NCBI BLAST+ searches inside the BioClaw container.
| Input | Database | Program |
|---|---|---|
| Nucleotide query | Nucleotide DB | blastn |
| Protein query | Protein DB | blastp |
| Nucleotide query | Protein DB | blastx |
| Protein query | Nucleotide DB | tblastn |
# Create query file
cat > /tmp/query.fa << 'EOF'
>query_sequence
ATGCGATCGATCGATCG...
EOF
# Create subject file (if user provides reference)
cat > /tmp/subject.fa << 'EOF'
>reference
ATGCGATCGATCGATCG...
EOF
# Run BLAST
blastn -query /tmp/query.fa -subject /tmp/subject.fa -outfmt 6 -evalue 1e-5
Use BioPython's NCBIWWW module:
from Bio.Blast import NCBIWWW, NCBIXML
from Bio import SeqIO
# Read sequence
sequence = "ATGCGATCGATCGATCG..."
# Run remote BLAST
result_handle = NCBIWWW.qblast("blastn", "nt", sequence)
blast_records = NCBIXML.parse(result_handle)
for record in blast_records:
for alignment in record.alignments[:10]:
print(f"Title: {alignment.title}")
for hsp in alignment.hsps:
print(f" Score: {hsp.score}, E-value: {hsp.expect}")
print(f" Identity: {hsp.identities}/{hsp.align_length} ({hsp.identities/hsp.align_length*100:.1f}%)")
Present results in a clear table:
*BLAST Results (top 10 hits)*
• Hit 1: Homo sapiens TP53 gene (98.5% identity, E=1e-45)
• Hit 2: Mus musculus Trp53 gene (89.2% identity, E=1e-38)
...
After showing results, suggest:
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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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.
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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.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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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.
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