Bio Blast Searches

Version Compatibility

Reference examples tested with: BioPython 1.83+, NCBI BLAST+ 2.15+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show biopython then help(Bio.Blast.NCBIWWW.qblast) to check signatures
• CLI: blastn -version then blastn -help

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

BLAST Searches (Remote)

"Find similar sequences in NCBI's database" -> Submit a query to NCBI's remote BLAST servers; receive a Request ID (RID); poll for completion; parse the XML hit table. Best for one-off identification of a few sequences. For >50 sequences, switch to local-blast or DIAMOND/MMseqs2 in remote-homology.

The two most consequential decisions: which program (defines query+target molecule types and word-size defaults) and which database (defines the search space and therefore E-value baselines). The third most important: do NOT misuse max_target_seqs -- it is an early-termination heuristic, not a "give me the top N hits" filter (Shah et al. 2019).

• Python: NCBIWWW.qblast(program, db, sequence) + NCBIXML.read(handle) (BioPython)
• CLI: blastn -remote -db nt -query seq.fa -out hits.xml -outfmt 5 (BLAST+)
• Web: https://blast.ncbi.nlm.nih.gov/Blast.cgi (RID lookup)

Required Setup

from Bio.Blast import NCBIWWW, NCBIXML
from Bio import SeqIO

No API key needed for remote BLAST itself, but NCBI's general rate-limit ethic still applies -- one search at a time, polite waiting, no parallelism.

Program decision (query vs database molecule)

The misuse to avoid: using default blastn (word=11) for cross-species DNA where dc-megablast is the right tool. Or using megablast (word=28) for cross-species homology where it will miss every divergent hit. The most-misused BLAST parameter according to literature.

Database decision (search space)

For publication reproducibility, never search nt or nr without recording the snapshot date and ideally archiving a frozen copy. Default to refseq_select for any cross-species homology question; switch to nt/nr only when curated coverage is insufficient.

E-value interpretation (Karlin-Altschul)

E-value = K * m * n * exp(-lambda * S), where m = effective query length, n = effective database size, lambda and K are scoring-matrix-dependent constants (Karlin & Altschul 1990 PNAS 87:2264).

Key implication of E = K * m * n * exp(-lambda * S): the same alignment against a 100x larger database has a 100x larger E-value. Cross-database E-value comparison is meaningless. Bit-score is database-size normalized and is the right cross-database metric.

For protein remote homology where E is marginal (10^-3 to 10^-1), reach for profile methods: PSI-BLAST, jackhmmer, HHblits, or Foldseek -- see remote-homology skill.

Composition-Based Statistics (CBS)

Compositional bias inflates significance for low-complexity proteins. The CBS modes (Yu et al. 2006 Nucleic Acids Res 34:5966):

For protein queries under 30 aa, switch to CBS=3. For protein with known compositional bias (e.g. coiled-coil regions, signal peptides), CBS=2 is appropriate but consider hard-masking with SEG.

The max_target_seqs trap

The misuse: max_target_seqs=10 is interpreted as "return the 10 most significant hits". It is not. The flag is an early termination parameter that affects which hits the search ever considers, not which it ultimately reports (Shah N, Nute MG, Warnow T, Pop M. (2019) Misunderstood parameter of NCBI BLAST impacts the correctness of bioinformatics workflows. Bioinformatics 35:1613-1614).

Consequences:

• Setting max_target_seqs=10 can return entirely different hits than max_target_seqs=500 then filtering to top 10 by E-value.
• The "top 10" by E-value as reported may not be the actual top 10.

Correct pattern: set hitlist_size (Bio.Blast parameter name) large (1000+), then post-filter to the top N by E-value or bit-score in Python.

Word size, gap costs, and matrix

For very short query proteins (e.g. proteomics-identified peptides), BLOSUM45 + word=2 + PAM30 substitution matrix is more sensitive than the default. Use matrix='PAM30' for searches against swissprot.

RID lifecycle

NCBIWWW.qblast() handles polling internally with a fixed retry interval. For long-running searches (>5 min) or batches, submit and capture the RID, then poll independently to avoid blocking. The RID is visible at https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Get&RID=... for 24-36 hours.

Code patterns

Standard remote BLASTN with reproducible parameters

Goal: Run BLASTN with explicit, paper-quality parameters.

Approach: Specify program, database (refseq_select for stability), word size, expect, and a large hitlist_size to dodge the max_target_seqs trap.

Reference (BioPython 1.83+):

from Bio.Blast import NCBIWWW, NCBIXML

handle = NCBIWWW.qblast(
    program='blastn',
    database='refseq_select_rna',
    sequence=query_seq,
    expect=1e-10,
    word_size=11,
    hitlist_size=500,  # large; filter top-N downstream
    format_type='XML',
)
record = NCBIXML.read(handle); handle.close()
top10 = sorted(record.alignments, key=lambda a: a.hsps[0].expect)[:10]

Protein search with organism restriction

Goal: Find mammalian homologs of a query protein in Swiss-Prot.

Approach: entrez_query filters the BLAST search space pre-execution; faster and more meaningful E-values than post-filtering.

Reference (BioPython 1.83+):

handle = NCBIWWW.qblast(
    program='blastp',
    database='swissprot',
    sequence=protein_seq,
    entrez_query='Mammalia[Organism]',
    expect=1e-5,
    composition_based_statistics=2,
    hitlist_size=200,
)
record = NCBIXML.read(handle); handle.close()

Short peptide search

handle = NCBIWWW.qblast(
    program='blastp',
    database='swissprot',
    sequence=peptide_seq,  # < 30 aa
    matrix_name='PAM30',
    word_size=2,
    expect=1000,  # short queries need permissive cutoff
    composition_based_statistics=3,
    hitlist_size=100,
)

Save XML for re-parsing

handle = NCBIWWW.qblast('blastn', 'refseq_select_rna', query)
with open('blast.xml', 'w') as f:
    f.write(handle.read())
handle.close()

with open('blast.xml') as f:
    record = NCBIXML.read(f)

Hit extraction with identity + coverage filtering

Goal: Return structured top hits with biological metrics, not just E-values.

Approach: Walk alignments + first HSP; compute identity and query coverage as fractions; sort by bit-score (database-size invariant) not E-value.

Reference (BioPython 1.83+):

def top_hits(record, min_identity=0.5, min_coverage=0.7, top_n=10):
    qlen = record.query_length
    hits = []
    for aln in record.alignments:
        hsp = aln.hsps[0]
        ident = hsp.identities / hsp.align_length
        cov = hsp.align_length / qlen
        if ident >= min_identity and cov >= min_coverage:
            hits.append({
                'accession': aln.accession,
                'title': aln.title,
                'evalue': hsp.expect,
                'bits': hsp.bits,
                'identity': ident,
                'coverage': cov,
            })
    return sorted(hits, key=lambda h: -h['bits'])[:top_n]

Programmatic RID polling for long jobs

import time

handle = NCBIWWW.qblast('tblastn', 'nr', query, hitlist_size=500, format_type='XML')
# Bio.Blast handles polling internally; for explicit control use the REST API directly
# or save and re-parse the RID URL

Failure modes

max_target_seqs misinterpretation

• Trigger: Setting hitlist_size=10 and assuming top 10 by E-value.
• Mechanism: It's an early-termination param; can miss legitimate top hits.
• Symptom: Different "top 10" between hitlist=10 and hitlist=500 filtered.
• Fix: Always set hitlist_size=500+ and post-filter; cite Shah 2019.

Cross-database E-value comparison

• Trigger: Comparing E from a nt search against E from a swissprot search.
• Mechanism: E scales linearly with database size; comparison is meaningless.
• Symptom: Misleading rankings between two analyses.
• Fix: Compare bit-scores instead, or set the same database for both.

Megablast for cross-species

• Trigger: Default megablast (word=28) on a cross-species DNA query.
• Mechanism: Word size 28 requires 28-nt exact match to seed; cross-species mRNA has too much divergence.
• Symptom: Zero hits or only hits to the same species.
• Fix: Use dc-megablast (discontiguous) or blastn with word=11.

Reproducibility loss against nt/nr

• Trigger: Manuscript says "BLASTed against nt"; reviewer re-runs 3 weeks later.
• Mechanism: Databases change daily; new genomes deposited.
• Symptom: Different hit set, different paper conclusions.
• Fix: Use refseq_select for reproducibility, or record snapshot date + archive subset.

Server timeout on large queries

• Trigger: Multi-megabase query or batch submission.
• Mechanism: Remote BLAST has a per-query compute budget.
• Symptom: Job stuck in queue, eventually fails.
• Fix: Split into smaller queries; or switch to local-blast / DIAMOND / MMseqs2.

Compositional bias inflates E

• Trigger: Protein query with low-complexity region (coiled-coil, signal peptide).
• Mechanism: Default CBS=2 handles most cases, but extreme bias still inflates scores.
• Symptom: Many "significant" hits to unrelated low-complexity proteins.
• Fix: Confirm CBS=2 is on; consider hard-masking with filter='S' (SEG).

Empty FASTA defline submitted

• Trigger: Sending sequence as a raw string without >id\n.
• Mechanism: BLAST treats as anonymous query; some downstream parsers misbehave.
• Symptom: Hits returned but record.query is None.
• Fix: Always pass FASTA with a defline; or pass a SeqRecord.

Common errors

References

• Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. (1990) Basic local alignment search tool. J Mol Biol 215:403-410.
• Karlin S, Altschul SF. (1990) Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc Natl Acad Sci USA 87:2264-2268.
• Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389-3402.
• Yu YK, Gertz EM, Agarwala R, Schaffer AA, Altschul SF. (2006) Retrieval accuracy, statistical significance and compositional similarity in protein sequence database searches. Nucleic Acids Res 34:5966-5973.
• Shah N, Nute MG, Warnow T, Pop M. (2019) Misunderstood parameter of NCBI BLAST impacts the correctness of bioinformatics workflows. Bioinformatics 35:1613-1614.
• Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421.

Related Skills

• local-blast - Faster, unlimited local BLAST+ pipelines and database build
• remote-homology - PSI-BLAST, jackhmmer, HHblits, MMseqs2, DIAMOND, Foldseek for distant homology
• ortholog-inference - Reciprocal best hit, OrthoFinder, OMA for orthology
• sequence-io/read-sequences - Load query sequences from FASTA
• entrez-fetch - Fetch full records for BLAST hits