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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".
---
name: blast-search-3f1d444ddcbd
description: 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".
---
# 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)
```bash
# 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:
```python
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
---
name: blast-search-3f1d444ddcbd
description: 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".
---
# 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)
```bash
# 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:
```python
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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.