biopython

name	biopython
description	A comprehensive toolbox for computational molecular biology; use it when you need programmatic sequence/structure parsing, batch bioinformatics pipelines, or automated NCBI/BLAST workflows.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

Use this skill when you need to:

• Batch-process DNA/RNA/protein sequences (translation, reverse complement, statistics) as part of a custom pipeline.
• Parse, validate, convert, or stream large bioinformatics files (FASTA/FASTQ/GenBank/PDB/mmCIF) without loading everything into memory.
• Programmatically query and download records from NCBI (GenBank, PubMed, Gene, Protein) via Bio.Entrez, respecting rate limits.
• Automate BLAST searches (web or local) and parse results to extract top hits and metadata.
• Build or manipulate phylogenetic trees from alignments or distance matrices (e.g., NJ trees) for downstream analysis.

Note: For quick one-off queries, tools like gget may be more convenient; for multi-service API aggregation, bioservices may be a better fit.

Key Features

• Sequence objects and utilities: Bio.Seq, Bio.SeqRecord, Bio.SeqUtils (GC fraction, molecular weight, translation, etc.).
• File I/O and format conversion: Bio.SeqIO, Bio.AlignIO for FASTA/FASTQ/GenBank and alignment formats.
• NCBI access: Bio.Entrez for esearch, efetch, elink, and structured parsing via Entrez.read.
• BLAST: Bio.Blast.NCBIWWW for remote BLAST and Bio.Blast.NCBIXML for XML parsing.
• Structural bioinformatics: Bio.PDB for PDB/mmCIF parsing, hierarchy traversal, and geometry calculations.
• Phylogenetics: Bio.Phylo and Bio.Phylo.TreeConstruction for tree I/O, distances, and construction.

Reference guides (if present in this repository) can be consulted for deeper module-specific patterns:

• references/sequence_io.md
• references/alignment.md
• references/databases.md
• references/blast.md
• references/structure.md
• references/phylogenetics.md
• references/advanced.md

Dependencies

• Python >= 3.8 (Biopython 1.85 supports Python 3)
• biopython==1.85
• numpy>=1.20 (required by Biopython)

Install:

python -m pip install "biopython==1.85" "numpy>=1.20"

Example Usage

A complete, runnable example that:

1. parses a FASTA file,
2. computes GC fraction,
3. runs a remote BLAST (optional),
4. fetches the top hit from NCBI,
5. prints basic results.

Create example_biopython_pipeline.py:

from __future__ import annotations

import os
import time
from typing import Optional

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

# Optional BLAST (remote). Comment out if you do not want network calls.
from Bio.Blast import NCBIWWW, NCBIXML


def configure_entrez() -> None:
    """
    NCBI requires an email. An API key increases rate limits.
    Set these via environment variables to avoid hardcoding secrets.
    """
    email = os.environ.get("NCBI_EMAIL")
    if not email:
        raise RuntimeError("Set NCBI_EMAIL env var (required by NCBI). Example: export NCBI_EMAIL='[e-mail]'")
    Entrez.email = email

    api_key = os.environ.get("NCBI_API_KEY")
    if api_key:
        Entrez.api_key = api_key


def read_first_fasta_record(path: str):
    with open(path, "r", encoding="utf-8") as handle:
        return next(SeqIO.parse(handle, "fasta"))


def blast_top_accession(sequence: str, program: str = "blastn", database: str = "nt") -> Optional[]:
    
    result_handle = NCBIWWW.qblast(program, database, sequence)
    blast_record = NCBIXML.read(result_handle)

      blast_record.alignments:
         

    
     blast_record.alignments[].accession


 () -> :
     Entrez.efetch(db=, =accession, rettype=, retmode=)  handle:
         handle.read()


 () -> :
    configure_entrez()

    record = read_first_fasta_record()
    seq = record.seq

    ()
    ()
    ()

    
    time.sleep()

    top_acc = blast_top_accession((seq))
      top_acc:
        ()
        

    ()

    time.sleep()
    fasta_text = fetch_fasta_by_accession(top_acc)
    ()
    (fasta_text)


 __name__ == :
    main()

Run:

export NCBI_EMAIL="[e-mail]"
# export NCBI_API_KEY="your_ncbi_api_key"  # optional
python example_biopython_pipeline.py

Provide an input.fasta in the same directory, e.g.:

>demo
ATCGATCGATCGATCGATCG

Implementation Details

• Streaming I/O for large datasets: Prefer iterator-based parsing (SeqIO.parse) to avoid loading entire files into memory. Use SeqIO.read only when exactly one record is expected.
• Entrez configuration and rate limits:
  • Always set Entrez.email (NCBI requirement).
  • Optionally set Entrez.api_key to increase request limits.
  • In batch jobs, add delays (e.g., time.sleep(0.34) as a conservative baseline) and implement retries for transient HTTP failures.
• BLAST considerations:
  • NCBIWWW.qblast(...) is convenient but can be slow and is not ideal for high-throughput workloads.
  • Parse results with NCBIXML.read(...) (single record) or NCBIXML.parse(...) (multiple records).
  • Filter hits by HSP metrics (e-value, identity) by iterating alignment.hsps.
• Sequence statistics and transformations:
  • Use Bio.SeqUtils.gc_fraction(seq) for GC fraction (returns 0–1).
  • Use seq.translate(table=...) with the correct genetic code table for reproducibility.
• Structure parsing (if used):
  • Use Bio.PDB.PDBParser(QUIET=True) to suppress warnings when appropriate.
  • Navigate the SMCRA hierarchy (Structure → Model → Chain → Residue → Atom) for robust traversal and geometry calculations.
• Reproducibility:
  • Record key parameters (file formats, translation table, BLAST program/database, e-value thresholds, NCBI query terms).
  • Cache downloaded records when iterating to avoid repeated network calls.