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。
BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新
無料GitHub で公開
blast-search
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".
含まれるファイル（1）
- SKILL.md2.2 KB
SKILL.md（原文）
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。
BLAST Search
Run NCBI BLAST+ searches inside the BioClaw container.
When to Use
- User provides a DNA/RNA/protein sequence and wants to find similar sequences
- User asks to identify an unknown sequence
- User wants to check sequence conservation across species
How to Execute
1. Determine BLAST program
| Input | Database | Program |
|---|---|---|
| Nucleotide query | Nucleotide DB | blastn |
| Protein query | Protein DB | blastp |
| Nucleotide query | Protein DB | blastx |
| Protein query | Nucleotide DB | tblastn |
2. For local BLAST (sequences provided by user)
# 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
3. For remote BLAST (against NCBI databases)
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}%)")
4. Output format
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)
...
5. Follow-up suggestions
After showing results, suggest:
- Multiple sequence alignment of top hits
- Phylogenetic analysis
- Domain/motif analysis of the query
- Structural comparison if protein
レビュー
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。
同じリポジトリのスキル
概要と使いどころ
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。
BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新
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。
BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新
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。
BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新
atac-seq
無料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。
BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新
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。
BioTender-max/awesome-bio-agent-skills2012026年7月2日 更新