bio-codon-usage

Version Compatibility

Reference examples tested with: BioPython 1.83+

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

• Python: pip show <package> then help(module.function) to check signatures

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

Codon Usage

Analyze codon usage patterns and calculate codon adaptation metrics using Biopython.

"Analyze codon usage" → Count codons in a coding sequence, compute frequencies and bias metrics.

• Python: Counter on 3-mers + CodonAdaptationIndex (BioPython)

"Optimize codons for expression" → Replace codons with host-preferred synonymous codons using a preference table.

• Python: custom mapping dict + Seq() (BioPython)

Required Imports

from Bio.Seq import Seq
from Bio.SeqUtils import GC123
from Bio.SeqUtils.CodonUsage import CodonAdaptationIndex
from Bio.Data import CodonTable
from collections import Counter

Basic Codon Counting

Goal: Tabulate codon frequencies in a coding sequence.

Approach: Split the sequence into triplets from the reading frame start, then count with Counter.

Count Codons in Sequence

from collections import Counter

def count_codons(seq):
    seq_str = str(seq).upper()
    codons = [seq_str[i:i+3] for i in range(0, len(seq_str) - 2, 3)]
    return Counter(codons)

seq = Seq('ATGCGATCGATCGATCGTAA')
codon_counts = count_codons(seq)

Codon Frequencies (Relative)

def codon_frequencies(seq):
    counts = count_codons(seq)
    total = sum(counts.values())
    return {codon: count / total for codon, count in counts.items()}

Codon Adaptation Index (CAI)

Goal: Measure how well a gene's codon usage matches highly expressed genes in a target organism.

Approach: Train a CAI index from a reference set of highly expressed genes, then score query sequences (0-1 scale, higher = better adapted).