blast-search

name	blast-search
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 MolClaw 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