biopython-advanced

When to Use

• You need motif discovery/statistics (e.g., PWM/consensus, motif counts across multiple sequences).
• You want restriction enzyme site analysis (e.g., find cut sites for specific enzymes in a DNA sequence).
• You need codon usage / sequence utility calculations (e.g., codon frequency from CDS, GC content, basic sequence stats).
• You are working with population genetics (PopGen) utilities for advanced analyses.
• You need advanced visualization such as GenomeDiagram-style plots for genomic features.

Key Features

• Motif analysis using Biopython’s Bio.motifs (counts, consensus, simple statistics).
• Restriction analysis using Bio.Restriction (enzyme lookup, cut site detection).
• Sequence utilities via Bio.SeqUtils (codon usage and related helpers).
• Access to additional advanced tools such as CodonTable, SeqFeature, and IUPACData when needed.
• Standardized workflow conventions:
  • Write configuration to config/task_config.json as an intermediate artifact.
  • Run tasks uniformly via python scripts/<task_name>.py.
  • Avoid stacking many CLI flags; keep parameters in config files.
  • Always use encoding="utf-8" for file I/O; JSON output uses ensure_ascii=False.

Dependencies

Required:

• biopython (>=1.80)
• numpy (>=1.21)

Optional (for reporting/plotting):

• reportlab (>=3.6)
• matplotlib (>=3.5)

Example Usage

The following examples are complete runnable scripts that follow the conventions:

• configuration stored in config/task_config.json
• invoked as python scripts/<task_name>.py
• explicit UTF-8 encoding and ensure_ascii=False for JSON output

1) Motif Statistics

config/task_config.json

{
  "task": "motif_stats",
  "sequences": ["ATGCATGCATGC", "ATGCGTGCATGC", "ATGCATGTATGC"]
}

scripts/motif_stats.py

import json
from Bio import motifs
from Bio.Seq import Seq

def main():
    with open("config/task_config.json", "r", encoding="utf-8") as f:
        cfg = json.load(f)

    seqs = [Seq(s) for s in cfg["sequences"]]
    m = motifs.create(seqs)

    result = {
        "alphabet": str(m.alphabet),
        "length": m.length,
        "counts": {k: dict(v) for k, v in m.counts.items()},
        "consensus": str(m.consensus),
        "degenerate_consensus": str(m.degenerate_consensus),
    }

    with open("outputs/motif_stats.json", "w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

if __name__ == "__main__":
    main()

Run:

python scripts/motif_stats.py

2) Restriction Enzyme Cleavage Sites

config/task_config.json

{
  "task": "restriction_sites",
  "sequence": "GAATTCGCGGAATTC",
  "enzymes": ["EcoRI", "BamHI"]
}

scripts/restriction_sites.py

import json
from Bio.Seq import Seq
from Bio.Restriction import RestrictionBatch

def main():
    with open("config/task_config.json", "r", encoding="utf-8") as f:
        cfg = json.load(f)

    seq = Seq(cfg["sequence"])
    batch = RestrictionBatch(cfg["enzymes"])
    analysis = batch.search(seq)

    # Convert enzyme keys to strings for JSON serialization
    result = {str(enzyme): positions for enzyme, positions in analysis.items()}

    with open("outputs/restriction_sites.json", "w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

if __name__ == "__main__":
    main()

Run:

python scripts/restriction_sites.py

3) Codon Usage Frequency (CDS)

config/task_config.json

{
  "task": "codon_usage",
  "cds": "ATGGCTGCTGCTGCTTAA"
}

scripts/codon_usage.py

import json
from collections import Counter

def main():
    with open("config/task_config.json", "r", encoding="utf-8") as f:
        cfg = json.load(f)

    cds = cfg["cds"].upper().replace(" ", "").replace("\n", "")
    codons = [cds[i:i+3] for i in range(0, len(cds) - (len(cds) % 3), 3)]
    counts = Counter(codons)
    total = sum(counts.values()) or 1

    result = {
        "total_codons": total,
        "codon_counts": dict(sorted(counts.items())),
        "codon_frequencies": {k: v / total for k, v in sorted(counts.items())},
        "note": "This example computes raw codon frequencies from the provided CDS. Validate CDS frame and stop codons for your use case."
    }

    with open("outputs/codon_usage.json", "w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

if __name__ == "__main__":
    main()

Run:

python scripts/codon_usage.py

Implementation Details

• Configuration-first execution

  • All task parameters are stored in config/task_config.json to keep CLI invocation stable and reproducible.
  • Scripts read the config as the single source of truth and write results to outputs/*.json.

• Motif statistics (Bio.motifs)

  • A motif is created from aligned sequences of equal length.
  • Outputs typically include:
    • counts: per-position nucleotide counts
    • consensus and degenerate_consensus: derived consensus sequences
  • If sequences differ in length, you must align/trim/pad them before motif creation.

• Restriction analysis (Bio.Restriction)

  • RestrictionBatch(enzymes).search(seq) returns cut positions per enzyme.
  • Enzyme objects are converted to strings for JSON serialization.

• Codon usage

  • The example computes codon frequencies by splitting the CDS into triplets in-frame.
  • Practical considerations:
    • Ensure the CDS length is a multiple of 3 (or decide how to handle remainder bases).
    • Confirm the correct reading frame and whether to include terminal stop codons.
    • For organism-specific codon usage tables, integrate Bio.Data.CodonTable as needed.

• I/O requirements

  • Always open files with encoding="utf-8".
  • Use json.dump(..., ensure_ascii=False) to preserve non-ASCII characters in outputs.

• Further reference

  • See references/advanced.md for additional notes and module coverage (motifs/PopGen/SeqUtils/Restriction/Cluster, GenomeDiagram, CodonTable/SeqFeature/IUPACData).