Biopython Sequence Analysis

pip install biopython numpy matplotlib
conda install -c bioconda blast  # optional, for local BLAST

from Bio import SeqIO, Entrez
from Bio.SeqUtils import gc_fraction

# Fetch a GenBank record and display basic stats
Entrez.email = "your.email@example.com"
handle = Entrez.efetch(db="nucleotide", id="NM_007294", rettype="gb", retmode="text")
record = SeqIO.read(handle, "genbank")
handle.close()

print(f"ID: {record.id}")
print(f"Length: {len(record.seq)} bp")
print(f"GC content: {gc_fraction(record.seq)*100:.1f}%")
print(f"Features: {len(record.features)}")
print(f"First feature: {record.features[0].type} at {record.features[0].location}")
# ID: NM_007294.4
# Length: 7207 bp
# GC content: 47.3%
# Features: 9

from Bio import SeqIO

# Parse FASTA and FASTQ
fasta_records = list(SeqIO.parse("sequences.fasta", "fasta"))
print(f"FASTA: {len(fasta_records)} sequences")

# FASTQ: access quality scores
for rec in SeqIO.parse("reads.fastq", "fastq"):
    quals = rec.letter_annotations["phred_quality"]
    avg_q = sum(quals) / len(quals)
    print(f"  {rec.id}: {len(rec.seq)} bp, mean Q={avg_q:.1f}")
    break  # show one example

# Parse GenBank with feature access
for rec in SeqIO.parse("chromosome.gb", "genbank"):
    cdss = [f for f in rec.features if f.type == "CDS"]
    print(f"{rec.id}: {len(cdss)} CDS features")
    for cds in cdss[:3]:
        gene = cds.qualifiers.get("gene", ["unknown"])[0]
        print(f"  {gene}: {cds.location}")

from Bio import SeqIO

# SeqIO.index() for random access without loading all records
# Useful for large reference FASTA files (genomes, nr database subsets)
idx = SeqIO.index("large_genome.fasta", "fasta")
print(f"Index contains {len(idx)} sequences")

# Retrieve specific sequences by ID in O(1)
target = idx["chr1"]
print(f"chr1: {len(target.seq):,} bp")
region = target.seq[1_000_000:1_001_000]
print(f"Region [1M-1M+1kb]: {region[:60]}...")

# Format conversion: GenBank → FASTA in one call
n = SeqIO.convert("annotation.gb", "genbank", "sequences.fasta", "fasta")
print(f"Converted {n} records to FASTA")

from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.SeqUtils import gc_fraction, MeltingTemp

dna = Seq("ATGAAACCCGGGTTTTAA")
print(f"GC content: {gc_fraction(dna)*100:.1f}%")
print(f"Reverse complement: {dna.reverse_complement()}")
print(f"Transcript: {dna.transcribe()}")
print(f"Translation: {dna.translate()}")
print(f"Translation (to stop): {dna.translate(to_stop=True)}")

# Tm calculation for a short primer
primer = Seq("ATGAAACCCGGG")
tm = MeltingTemp.Tm_Wallace(primer)
print(f"Tm (Wallace): {tm:.1f}°C")

from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation

# Build a SeqRecord with annotated features
gene_seq = Seq("ATGAAACCCGGGTTTTAAATCGATCG" * 10)
record = SeqRecord(gene_seq, id="MY_GENE_001", description="synthetic example gene")

# Annotate a CDS feature
cds = SeqFeature(
    FeatureLocation(0, 18, strand=+1),
    type="CDS",
    qualifiers={"gene": ["myGene"], "product": ["hypothetical protein"]}
)
record.features.append(cds)

# Extract and translate the feature
cds_seq = cds.location.extract(record.seq)
protein = cds_seq.translate(to_stop=True)
print(f"CDS: {cds_seq}")
print(f"Protein: {protein}")

# Slice record preserving feature annotations
sub = record[0:60]
print(f"Subrecord: {len(sub.seq)} bp, {len(sub.features)} features")

from Bio import Entrez, SeqIO
import time

Entrez.email = "your.email@example.com"
# Entrez.api_key = "YOUR_API_KEY"  # for 10 req/s

# esearch: find IDs matching a query
handle = Entrez.esearch(db="nucleotide", term="BRCA1[Gene] AND Homo sapiens[Organism] AND mRNA[Filter]", retmax=10)
search_results = Entrez.read(handle)
handle.close()

print(f"Total hits: {search_results['Count']}")
ids = search_results["IdList"]
print(f"Retrieved IDs: {ids}")

# efetch: download full GenBank records
for acc_id in ids[:3]:
    handle = Entrez.efetch(db="nucleotide", id=acc_id, rettype="gb", retmode="text")
    record = SeqIO.read(handle, "genbank")
    handle.close()
    print(f"  {record.id}: {len(record.seq)} bp — {record.description[:60]}")
    time.sleep(0.4)  # stay within rate limit

from Bio import Entrez
import time

Entrez.email = "your.email@example.com"

# elink: cross-database links (e.g., PubMed article → related nucleotide sequences)
handle = Entrez.elink(dbfrom="pubmed", db="nucleotide", id="29087512")
link_results = Entrez.read(handle)
handle.close()

linked_ids = []
for linkset in link_results:
    for db_links in linkset.get("LinkSetDb", []):
        if db_links["DbTo"] == "nucleotide":
            linked_ids = [lnk["Id"] for lnk in db_links["Link"]]
            break

print(f"Nucleotide sequences linked to PubMed 29087512: {len(linked_ids)}")
print(f"First IDs: {linked_ids[:5]}")

# esummary: lightweight metadata without downloading full records
if linked_ids:
    handle = Entrez.esummary(db="nucleotide", id=",".join(linked_ids[:5]))
    summaries = Entrez.read(handle)
    handle.close()
    for doc in summaries:
        print(f"  {doc['AccessionVersion']}: {doc['Title'][:70]}")

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

# Remote BLASTP against Swiss-Prot (small, reviewed database)
query = Seq("MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSY")
result_handle = NCBIWWW.qblast("blastp", "swissprot", str(query), hitlist_size=10)

# Parse results
blast_record = NCBIXML.read(result_handle)
print(f"Query: {blast_record.query}")
print(f"Database: {blast_record.database}")
print(f"Hits: {len(blast_record.alignments)}")

E_VALUE_THRESH = 1e-5
for alignment in blast_record.alignments[:5]:
    for hsp in alignment.hsps:
        if hsp.expect < E_VALUE_THRESH:
            identity_pct = hsp.identities / hsp.align_length * 100
            print(f"\n  Hit: {alignment.title[:70]}")
            print(f"  E-value: {hsp.expect:.2e}, Identity: {identity_pct:.1f}%, Score: {hsp.score}")
            print(f"  Query:  {hsp.query[:60]}")
            print(f"  Match:  {hsp.match[:60]}")
            print(f"  Sbjct:  {hsp.sbjct[:60]}")

import subprocess
from Bio.Blast import NCBIXML
from Bio import SeqIO

# Local BLAST+ via subprocess (faster for large batches)
# Requires: makeblastdb and blastp installed (conda install -c bioconda blast)

# Step 1: Build a local database from a FASTA file
subprocess.run(
    ["makeblastdb", "-in", "ref_proteins.fasta", "-dbtype", "prot", "-out", "ref_db"],
    check=True
)

# Step 2: Run blastp against local database
result = subprocess.run(
    ["blastp", "-query", "query.fasta", "-db", "ref_db",
     "-outfmt", "5",           # XML output for NCBIXML parsing
     "-evalue", "1e-5",
     "-num_threads", "4",
     "-out", "blast_results.xml"],
    check=True
)

# Step 3: Parse XML results
with open("blast_results.xml") as fh:
    for blast_rec in NCBIXML.parse(fh):
        print(f"Query: {blast_rec.query_id}")
        for aln in blast_rec.alignments[:3]:
            hsp = aln.hsps[0]
            print(f"  Hit: {aln.hit_id}, E={hsp.expect:.2e}, Id={hsp.identities}/{hsp.align_length}")

from Bio import Phylo
import io

# Parse a Newick tree from a string
newick = "((Homo_sapiens:0.01, Pan_troglodytes:0.012):0.08, (Mus_musculus:0.15, Rattus_norvegicus:0.14):0.12, Drosophila_melanogaster:0.85);"
tree = Phylo.read(io.StringIO(newick), "newick")

print(f"Number of terminals: {tree.count_terminals()}")
print(f"Total branch length: {tree.total_branch_length():.3f}")
print(f"Terminals: {[t.name for t in tree.get_terminals()]}")

# Root with outgroup and compute distances
tree.root_with_outgroup("Drosophila_melanogaster")
human = tree.find_any("Homo_sapiens")
chimp = tree.find_any("Pan_troglodytes")
mouse = tree.find_any("Mus_musculus")
print(f"\nDistance Human–Chimp: {tree.distance(human, chimp):.4f}")
print(f"Distance Human–Mouse: {tree.distance(human, mouse):.4f}")

# Traverse all internal nodes
for clade in tree.find_clades(order="level"):
    if not clade.is_terminal():
        children = [c.name or "internal" for c in clade.clades]
        print(f"  Internal node → {children}")

from Bio import Phylo
import matplotlib.pyplot as plt
import io

newick = "((Homo_sapiens:0.01, Pan_troglodytes:0.012):0.08, (Mus_musculus:0.15, Rattus_norvegicus:0.14):0.12, Drosophila_melanogaster:0.85);"
tree = Phylo.read(io.StringIO(newick), "newick")
tree.root_with_outgroup("Drosophila_melanogaster")
tree.ladderize()   # sort clades by size for a clean layout

# Annotate bootstrap support (if present in node labels)
for clade in tree.find_clades():
    if clade.confidence is not None:
        clade.name = f"{clade.confidence:.0f}"

fig, ax = plt.subplots(figsize=(8, 5))
Phylo.draw(tree, axes=ax, do_show=False, show_confidence=True)
ax.set_title("Primate + Outgroup Phylogeny")
plt.tight_layout()
plt.savefig("phylogeny.png", dpi=150, bbox_inches="tight")
print("Saved phylogeny.png")

from Bio.Align import PairwiseAligner, substitution_matrices

# Global protein alignment with BLOSUM62
aligner = PairwiseAligner()
aligner.mode = "global"
aligner.substitution_matrix = substitution_matrices.load("BLOSUM62")
aligner.open_gap_score = -11
aligner.extend_gap_score = -1

seq1 = "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSY"
seq2 = "MTEYKLVVVGAVGVGKSALTIQLIQNHFVDEYDPTIEDSY"  # G12V variant

alignments = aligner.align(seq1, seq2)
best = alignments[0]
print(f"Score: {best.score:.1f}")
print(f"Number of alignments: {aligner.score(seq1, seq2):.0f} (score only, faster)")
print(best)   # formatted alignment string

identity = sum(a == b for a, b in zip(*best) if a != "-") / best.shape[1]
print(f"Identity: {identity*100:.1f}%")

from Bio.Align import PairwiseAligner, substitution_matrices

# Local DNA alignment
aligner = PairwiseAligner()
aligner.mode = "local"
aligner.match_score = 2
aligner.mismatch_score = -1
aligner.open_gap_score = -5
aligner.extend_gap_score = -0.5

query = "ACGTACGTACGT"
subject = "TTTTACGTACGTACGTTTTT"
alignments = list(aligner.align(query, subject))
print(f"Local alignments found: {len(alignments)}")
best = alignments[0]
print(f"Score: {best.score}")
print(f"Aligned region in subject: [{best.coordinates[1][0]}:{best.coordinates[1][-1]}]")
print(best)

# Batch pairwise identity matrix
seqs = ["ACGTACGT", "ACGTATGT", "TTGTACGT", "ACGTACGG"]
n = len(seqs)
aligner2 = PairwiseAligner(mode="global", match_score=1, mismatch_score=-1)
print("\nPairwise identity matrix:")
for i in range(n):
    row = []
    for j in range(n):
        score = aligner2.score(seqs[i], seqs[j])
        max_len = max(len(seqs[i]), len(seqs[j]))
        row.append(f"{score/max_len:.2f}")
    print("  " + "  ".join(row))

from Bio import SeqIO

# Extract all CDS protein translations from a GenBank record
for rec in SeqIO.parse("gene.gb", "genbank"):
    for feat in rec.features:
        if feat.type == "CDS":
            gene = feat.qualifiers.get("gene", ["?"])[0]
            product = feat.qualifiers.get("product", ["unknown"])[0]
            cds_nt = feat.location.extract(rec.seq)
            protein = cds_nt.translate(to_stop=True)
            print(f"{gene} ({product}): {len(protein)} aa — {protein[:10]}...")

import subprocess
import time
from Bio import Entrez, SeqIO, Phylo, AlignIO
from Bio.Align import MultipleSeqAlignment
import matplotlib.pyplot as plt

Entrez.email = "your.email@example.com"

# Step 1: Search NCBI Protein for BRCA1 orthologs
handle = Entrez.esearch(
    db="protein",
    term="BRCA1[Gene] AND Vertebrata[Organism] AND RefSeq[Filter]",
    retmax=20
)
results = Entrez.read(handle); handle.close()
ids = results["IdList"]
print(f"Found {results['Count']} sequences, using {len(ids)}")

# Step 2: Fetch sequences in FASTA format
handle = Entrez.efetch(db="protein", id=",".join(ids), rettype="fasta", retmode="text")
with open("brca1_orthologs.fasta", "w") as fh:
    fh.write(handle.read())
handle.close()
print(f"Saved {len(ids)} sequences to brca1_orthologs.fasta")
time.sleep(1)

# Step 3: Align with MUSCLE (must be installed: conda install -c bioconda muscle)
subprocess.run(
    ["muscle", "-align", "brca1_orthologs.fasta", "-output", "brca1_aligned.fasta"],
    check=True
)
alignment = AlignIO.read("brca1_aligned.fasta", "fasta")
print(f"Alignment: {len(alignment)} sequences × {alignment.get_alignment_length()} columns")

# Step 4: Build a neighbor-joining tree using Bio.Phylo + distance matrix
from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor

calculator = DistanceCalculator("identity")
dm = calculator.get_distance(alignment)
constructor = DistanceTreeConstructor(calculator, method="nj")
tree = constructor.build_tree(alignment)

# Step 5: Root and save
tree.root_at_midpoint()
tree.ladderize()
Phylo.write(tree, "brca1_nj.nwk", "newick")
print("Saved NJ tree to brca1_nj.nwk")

# Step 6: Visualize
fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax, do_show=False)
ax.set_title("BRCA1 Ortholog NJ Tree")
plt.tight_layout()
plt.savefig("brca1_tree.png", dpi=150, bbox_inches="tight")
print("Saved brca1_tree.png")

import csv
import time
from Bio import SeqIO, Entrez
from Bio.Blast import NCBIWWW, NCBIXML

Entrez.email = "your.email@example.com"

# Step 1: Load query sequence
query_record = SeqIO.read("query.fasta", "fasta")
print(f"Query: {query_record.id} ({len(query_record.seq)} aa)")

# Step 2: Remote BLAST against nr protein database
print("Submitting BLAST job (may take 1-5 minutes)...")
result_handle = NCBIWWW.qblast(
    "blastp", "nr", str(query_record.seq),
    hitlist_size=20,
    expect=1e-5,
    word_size=6,
    matrix_name="BLOSUM62"
)

# Step 3: Parse results and filter
E_THRESH = 1e-5
MIN_IDENTITY = 50.0
top_hits = []

blast_record = NCBIXML.read(result_handle)
for alignment in blast_record.alignments:
    for hsp in alignment.hsps:
        if hsp.expect > E_THRESH:
            continue
        identity_pct = hsp.identities / hsp.align_length * 100
        if identity_pct < MIN_IDENTITY:
            continue
        accession = alignment.accession
        top_hits.append({
            "accession": accession,
            "title": alignment.title[:80],
            "length": alignment.length,
            "score": hsp.score,
            "evalue": hsp.expect,
            "identity_pct": round(identity_pct, 1),
            "coverage": round(hsp.align_length / blast_record.query_length * 100, 1),
        })

print(f"Filtered to {len(top_hits)} significant hits")

# Step 4: Fetch full GenBank records for top 5 hits
accessions = [h["accession"] for h in top_hits[:5]]
time.sleep(1)
handle = Entrez.efetch(db="protein", id=",".join(accessions), rettype="gb", retmode="text")
gb_records = list(SeqIO.parse(handle, "genbank"))
handle.close()
SeqIO.write(gb_records, "top_blast_hits.gb", "genbank")
print(f"Saved {len(gb_records)} full GenBank records to top_blast_hits.gb")

# Step 5: Write CSV summary
with open("blast_summary.csv", "w", newline="") as fh:
    writer = csv.DictWriter(fh, fieldnames=top_hits[0].keys())
    writer.writeheader()
    writer.writerows(top_hits)
print(f"Saved blast_summary.csv ({len(top_hits)} hits)")

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction

input_fastq = "raw_reads.fastq"
output_fasta = "filtered_reads.fasta"
MIN_QUAL = 20     # mean Phred quality
MIN_LEN = 100     # minimum read length
MAX_LEN = 300     # maximum read length

def passes_qc(record, min_q=MIN_QUAL, min_l=MIN_LEN, max_l=MAX_LEN):
    quals = record.letter_annotations["phred_quality"]
    mean_q = sum(quals) / len(quals)
    length = len(record.seq)
    return mean_q >= min_q and min_l <= length <= max_l

kept = 0
total = 0
with open(output_fasta, "w") as out_fh:
    for record in SeqIO.parse(input_fastq, "fastq"):
        total += 1
        if passes_qc(record):
            # Convert FASTQ → FASTA (drops quality scores)
            SeqIO.write(record, out_fh, "fasta")
            kept += 1

print(f"Total reads: {total:,}")
print(f"Passed QC: {kept:,} ({kept/total*100:.1f}%)")
print(f"Saved to: {output_fasta}")