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Add more scientific skills
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Timothy Kassis
272
scientific-packages/biopython/scripts/blast_search.py
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272
scientific-packages/biopython/scripts/blast_search.py
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#!/usr/bin/env python3
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"""
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BLAST searches and result parsing using BioPython.
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This script demonstrates:
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- Running BLAST searches via NCBI (qblast)
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- Parsing BLAST XML output
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- Filtering and analyzing results
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- Working with alignments and HSPs
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"""
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from Bio.Blast import NCBIWWW, NCBIXML
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from Bio import SeqIO
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def run_blast_online(sequence, program="blastn", database="nt", expect=0.001):
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"""
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Run BLAST search via NCBI's qblast.
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Parameters:
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- sequence: Sequence string or Seq object
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- program: blastn, blastp, blastx, tblastn, tblastx
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- database: nt (nucleotide), nr (protein), refseq_rna, etc.
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- expect: E-value threshold
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"""
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print(f"Running {program} search against {database} database...")
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print(f"E-value threshold: {expect}")
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print("-" * 60)
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# Run BLAST
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result_handle = NCBIWWW.qblast(
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program=program,
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database=database,
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sequence=sequence,
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expect=expect,
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hitlist_size=50, # Number of sequences to show alignments for
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)
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# Save results
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output_file = "blast_results.xml"
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with open(output_file, "w") as out:
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out.write(result_handle.read())
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result_handle.close()
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print(f"BLAST search complete. Results saved to {output_file}")
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print()
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return output_file
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def parse_blast_results(xml_file, max_hits=10, evalue_threshold=0.001):
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"""Parse BLAST XML results."""
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print(f"Parsing BLAST results from: {xml_file}")
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print(f"E-value threshold: {evalue_threshold}")
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print("=" * 60)
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with open(xml_file) as result_handle:
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blast_record = NCBIXML.read(result_handle)
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print(f"Query: {blast_record.query}")
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print(f"Query length: {blast_record.query_length} residues")
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print(f"Database: {blast_record.database}")
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print(f"Number of alignments: {len(blast_record.alignments)}")
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print()
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hit_count = 0
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for alignment in blast_record.alignments:
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for hsp in alignment.hsps:
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if hsp.expect <= evalue_threshold:
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hit_count += 1
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if hit_count <= max_hits:
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print(f"Hit {hit_count}:")
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print(f" Sequence: {alignment.title}")
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print(f" Length: {alignment.length}")
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print(f" E-value: {hsp.expect:.2e}")
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print(f" Score: {hsp.score}")
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print(f" Identities: {hsp.identities}/{hsp.align_length} ({hsp.identities / hsp.align_length * 100:.1f}%)")
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print(f" Positives: {hsp.positives}/{hsp.align_length} ({hsp.positives / hsp.align_length * 100:.1f}%)")
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print(f" Gaps: {hsp.gaps}/{hsp.align_length}")
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print(f" Query range: {hsp.query_start} - {hsp.query_end}")
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print(f" Subject range: {hsp.sbjct_start} - {hsp.sbjct_end}")
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print()
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# Show alignment (first 100 characters)
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print(" Alignment preview:")
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print(f" Query: {hsp.query[:100]}")
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print(f" Match: {hsp.match[:100]}")
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print(f" Sbjct: {hsp.sbjct[:100]}")
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print()
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print(f"Total significant hits (E-value <= {evalue_threshold}): {hit_count}")
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print()
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return blast_record
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def parse_multiple_queries(xml_file):
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"""Parse BLAST results with multiple queries."""
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print(f"Parsing multiple queries from: {xml_file}")
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print("=" * 60)
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with open(xml_file) as result_handle:
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blast_records = NCBIXML.parse(result_handle)
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for i, blast_record in enumerate(blast_records, 1):
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print(f"\nQuery {i}: {blast_record.query}")
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print(f" Number of hits: {len(blast_record.alignments)}")
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if blast_record.alignments:
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best_hit = blast_record.alignments[0]
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best_hsp = best_hit.hsps[0]
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print(f" Best hit: {best_hit.title[:80]}...")
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print(f" Best E-value: {best_hsp.expect:.2e}")
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def filter_blast_results(blast_record, min_identity=0.7, min_coverage=0.5):
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"""Filter BLAST results by identity and coverage."""
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print(f"Filtering results:")
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print(f" Minimum identity: {min_identity * 100}%")
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print(f" Minimum coverage: {min_coverage * 100}%")
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print("-" * 60)
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filtered_hits = []
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for alignment in blast_record.alignments:
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for hsp in alignment.hsps:
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identity_fraction = hsp.identities / hsp.align_length
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coverage = hsp.align_length / blast_record.query_length
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if identity_fraction >= min_identity and coverage >= min_coverage:
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filtered_hits.append(
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{
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"title": alignment.title,
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"length": alignment.length,
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"evalue": hsp.expect,
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"identity": identity_fraction,
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"coverage": coverage,
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"alignment": alignment,
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"hsp": hsp,
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}
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)
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print(f"Found {len(filtered_hits)} hits matching criteria")
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print()
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# Sort by E-value
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filtered_hits.sort(key=lambda x: x["evalue"])
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# Display top hits
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for i, hit in enumerate(filtered_hits[:5], 1):
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print(f"{i}. {hit['title'][:80]}")
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print(f" Identity: {hit['identity']*100:.1f}%, Coverage: {hit['coverage']*100:.1f}%, E-value: {hit['evalue']:.2e}")
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print()
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return filtered_hits
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def extract_hit_sequences(blast_record, output_file="blast_hits.fasta"):
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"""Extract aligned sequences from BLAST results."""
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print(f"Extracting hit sequences to {output_file}...")
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from Bio.Seq import Seq
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from Bio.SeqRecord import SeqRecord
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records = []
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for i, alignment in enumerate(blast_record.alignments[:10]): # Top 10 hits
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hsp = alignment.hsps[0] # Best HSP for this alignment
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# Extract accession from title
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accession = alignment.title.split()[0]
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# Create SeqRecord from aligned subject sequence
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record = SeqRecord(
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Seq(hsp.sbjct.replace("-", "")), # Remove gaps
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id=accession,
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description=f"E-value: {hsp.expect:.2e}, Identity: {hsp.identities}/{hsp.align_length}",
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)
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records.append(record)
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# Write to FASTA
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SeqIO.write(records, output_file, "fasta")
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print(f"Extracted {len(records)} sequences")
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print()
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def analyze_blast_statistics(blast_record):
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"""Compute statistics from BLAST results."""
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print("BLAST Result Statistics:")
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print("-" * 60)
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if not blast_record.alignments:
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print("No hits found")
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return
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evalues = []
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identities = []
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scores = []
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for alignment in blast_record.alignments:
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for hsp in alignment.hsps:
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evalues.append(hsp.expect)
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identities.append(hsp.identities / hsp.align_length)
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scores.append(hsp.score)
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import statistics
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print(f"Total HSPs: {len(evalues)}")
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print(f"\nE-values:")
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print(f" Min: {min(evalues):.2e}")
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print(f" Max: {max(evalues):.2e}")
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print(f" Median: {statistics.median(evalues):.2e}")
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print(f"\nIdentity percentages:")
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print(f" Min: {min(identities)*100:.1f}%")
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print(f" Max: {max(identities)*100:.1f}%")
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print(f" Mean: {statistics.mean(identities)*100:.1f}%")
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print(f"\nBit scores:")
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print(f" Min: {min(scores):.1f}")
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print(f" Max: {max(scores):.1f}")
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print(f" Mean: {statistics.mean(scores):.1f}")
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print()
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def example_workflow():
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"""Demonstrate BLAST workflow."""
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print("=" * 60)
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print("BioPython BLAST Example Workflow")
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print("=" * 60)
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print()
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# Example sequence (human beta-globin)
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example_sequence = """
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ATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGCAGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATGCTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGCTCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGATCCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCACCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCACTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACTGGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC
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""".replace("\n", "").replace(" ", "")
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print("Example: Human beta-globin sequence")
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print(f"Length: {len(example_sequence)} bp")
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print()
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# Note: Uncomment to run actual BLAST search (takes time)
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# xml_file = run_blast_online(example_sequence, program="blastn", database="nt", expect=0.001)
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# For demonstration, use a pre-existing results file
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print("To run a real BLAST search, uncomment the run_blast_online() line")
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print("For now, demonstrating parsing with example results file")
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print()
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# If you have results, parse them:
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# blast_record = parse_blast_results("blast_results.xml", max_hits=5)
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# filtered = filter_blast_results(blast_record, min_identity=0.9)
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# analyze_blast_statistics(blast_record)
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# extract_hit_sequences(blast_record)
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if __name__ == "__main__":
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example_workflow()
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print()
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print("Note: BLAST searches can take several minutes.")
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print("For production use, consider running local BLAST instead.")
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