--- name: bio-codon-usage-ae62602d3aa5 description: Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis. tool_type: python primary_tool: Bio.SeqUtils.CodonUsage --- # Codon Usage Analyze codon usage patterns and calculate codon adaptation metrics using Biopython. ## Required Imports ```python 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 ### Count Codons in Sequence ```python 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) ```python 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) ### Using CodonUsage Module ```python from Bio.SeqUtils.CodonUsage import CodonAdaptationIndex # Create CAI calculator with reference set cai = CodonAdaptationIndex() # Generate index from highly expressed genes cai.generate_index('highly_expressed_genes.fasta') # Calculate CAI for a sequence seq = Seq('ATGCGATCGATCGATCGTAA') cai_value = cai.cai_for_gene(str(seq)) print(f'CAI: {cai_value:.3f}') # Range 0-1, higher = better adapted ``` ### CAI with Custom Codon Index ```python from Bio.SeqUtils.CodonUsage import CodonAdaptationIndex cai = CodonAdaptationIndex() # Set custom index (relative adaptiveness for each codon) custom_index = { 'TTT': 0.5, 'TTC': 1.0, # Phe 'TTA': 0.1, 'TTG': 0.5, 'CTT': 0.3, 'CTC': 1.0, 'CTA': 0.1, 'CTG': 1.0, # Leu # ... define all 64 codons } cai.set_cai_index(custom_index) ``` ## Synonymous Codon Usage ### RSCU (Relative Synonymous Codon Usage) RSCU = (observed codon frequency) / (expected frequency if all synonymous codons were used equally) ```python from Bio.Data import CodonTable def calculate_rscu(seq, table_id=1): codon_table = CodonTable.unambiguous_dna_by_id[table_id] counts = count_codons(seq) # Group codons by amino acid aa_to_codons = {} for codon in counts: if codon in codon_table.stop_codons: continue try: aa = codon_table.forward_table[codon] aa_to_codons.setdefault(aa, []).append(codon) except KeyError: continue # Calculate RSCU for each codon rscu = {} for aa, codons in aa_to_codons.items(): total = sum(counts.get(c, 0) for c in codons) n_synonymous = len(codons) expected = total / n_synonymous if n_synonymous > 0 else 0 for codon in codons: observed = counts.get(codon, 0) rscu[codon] = observed / expected if expected > 0 else 0 return rscu ``` ### Identify Rare Codons ```python def find_rare_codons(seq, threshold=0.1): freq = codon_frequencies(seq) return {codon: f for codon, f in freq.items() if f < threshold} ``` ### Codon Bias by Position (GC123) ```python from Bio.SeqUtils import GC123 seq = Seq('ATGCGATCGATCGATCGATCGATCGATCGTAA') gc_total, gc_pos1, gc_pos2, gc_pos3 = GC123(seq) print(f'Total GC: {gc_total:.1f}%') print(f'1st position GC: {gc_pos1:.1f}%') print(f'2nd position GC: {gc_pos2:.1f}%') print(f'3rd position GC: {gc_pos3:.1f}% (wobble position)') ``` ## Codon Tables ### Access Codon Tables ```python from Bio.Data import CodonTable # Get standard table std_table = CodonTable.unambiguous_dna_by_id[1] # List all available tables for id, table in CodonTable.unambiguous_dna_by_id.items(): print(f'{id}: {table.names[0]}') ``` ### Common Codon Tables | ID | Name | Organism | |----|------|----------| | 1 | Standard | Most organisms | | 2 | Vertebrate Mitochondrial | Human, mouse mito | | 4 | Mold Mitochondrial | Fungi, protozoa mito | | 5 | Invertebrate Mitochondrial | Insects, worms mito | | 11 | Bacterial/Plastid | E. coli, chloroplasts | ### Codon Table Properties ```python table = CodonTable.unambiguous_dna_by_id[1] print(f'Start codons: {table.start_codons}') print(f'Stop codons: {table.stop_codons}') # Forward table: codon -> amino acid print(table.forward_table['ATG']) # 'M' # Back table: amino acid -> list of codons back_table = {} for codon, aa in table.forward_table.items(): back_table.setdefault(aa, []).append(codon) print(f'Leucine codons: {back_table["L"]}') ``` ## Code Patterns ### Full Codon Usage Report ```python def codon_usage_report(seq, table_id=1): from Bio.Data import CodonTable table = CodonTable.unambiguous_dna_by_id[table_id] counts = count_codons(seq) total = sum(counts.values()) # Group by amino acid aa_groups = {} for codon, aa in table.forward_table.items(): aa_groups.setdefault(aa, []).append(codon) report = {} for aa, codons in sorted(aa_groups.items()): aa_total = sum(counts.get(c, 0) for c in codons) report[aa] = { 'total': aa_total, 'codons': {c: {'count': counts.get(c, 0), 'freq': counts.get(c, 0) / aa_total if aa_total > 0 else 0} for c in codons} } return report ``` ### Compare Codon Usage Between Sequences ```python def compare_codon_usage(seq1, seq2): freq1 = codon_frequencies(seq1) freq2 = codon_frequencies(seq2) all_codons = set(freq1.keys()) | set(freq2.keys()) comparison = {} for codon in sorted(all_codons): f1, f2 = freq1.get(codon, 0), freq2.get(codon, 0) comparison[codon] = {'seq1': f1, 'seq2': f2, 'diff': f1 - f2} return comparison ``` ### Optimize Codons for Expression ```python def optimize_codons(protein_seq, preferred_codons): '''Replace codons with preferred synonymous codons''' optimized = [] for aa in str(protein_seq): if aa in preferred_codons: optimized.append(preferred_codons[aa]) else: optimized.append('NNN') # Unknown return Seq(''.join(optimized)) # E. coli preferred codons ecoli_preferred = { 'A': 'GCG', 'R': 'CGT', 'N': 'AAC', 'D': 'GAT', 'C': 'TGC', 'Q': 'CAG', 'E': 'GAA', 'G': 'GGT', 'H': 'CAC', 'I': 'ATT', 'L': 'CTG', 'K': 'AAA', 'M': 'ATG', 'F': 'TTC', 'P': 'CCG', 'S': 'TCT', 'T': 'ACC', 'W': 'TGG', 'Y': 'TAC', 'V': 'GTT', } ``` ### Codon Usage from FASTA File ```python from Bio import SeqIO def analyze_fasta_codon_usage(filename): all_counts = Counter() for record in SeqIO.parse(filename, 'fasta'): all_counts.update(count_codons(record.seq)) total = sum(all_counts.values()) return {codon: count / total for codon, count in all_counts.items()} ``` ### Effective Number of Codons (Nc) A measure of codon bias (lower = more biased, range 20-61): ```python import math def effective_nc(seq, table_id=1): from Bio.Data import CodonTable table = CodonTable.unambiguous_dna_by_id[table_id] counts = count_codons(seq) # Group by degeneracy class aa_groups = {} for codon, aa in table.forward_table.items(): aa_groups.setdefault(aa, []).append(codon) # Calculate F for each amino acid nc_sum = 0 for aa, codons in aa_groups.items(): n = sum(counts.get(c, 0) for c in codons) if n <= 1: continue pi_sq_sum = sum((counts.get(c, 0) / n) ** 2 for c in codons) F = (n * pi_sq_sum - 1) / (n - 1) nc_sum += 1 / F if F > 0 else len(codons) return nc_sum if nc_sum > 0 else 61 ``` ## Property Reference | Metric | Range | Interpretation | |--------|-------|----------------| | CAI | 0-1 | Higher = better adapted to host | | RSCU | 0-N | 1.0 = no bias, >1 = overused, <1 = underused | | Nc | 20-61 | Lower = more biased | | GC3 | 0-100% | GC at wobble position | ## Common Errors | Error | Cause | Solution | |-------|-------|----------| | `KeyError` | Non-standard codon | Handle N-containing codons | | Wrong counts | Sequence not in frame | Ensure length is multiple of 3 | | No index set | Called CAI without training | Call `generate_index()` first | ## Decision Tree ``` Need to analyze codon usage? ├── Count codon frequencies? │ └── Use Counter on 3-mers ├── Calculate adaptation to host? │ └── Use CodonAdaptationIndex (CAI) ├── Identify synonymous bias? │ └── Calculate RSCU ├── Check wobble position bias? │ └── Use GC123() ├── Measure overall bias? │ └── Calculate Nc (effective number of codons) └── Optimize for expression? └── Replace with preferred synonymous codons ``` ## Related Skills - **transcription-translation** - Translate sequences and understand codon tables - **sequence-properties** - GC123 for wobble position GC content - **sequence-io/read-sequences** - Parse CDS sequences from GenBank files - **database-access** - Fetch reference gene sets from NCBI for CAI training --- Source attribution: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/bio-codon-usage/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/bio-codon-usage/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Education & Research Roles: Data Scientist, ML / AI Engineer, Researcher Specialties: API, Backend, Cross Platform Tools: Biopython Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 3fc1da9c19daa38a1643a05d2d5088d8229dede108e05280f926d5fd2ec7c130