Codetta

Codetta predicts genetic code tables from nucleotide sequences by aligning profile HMMs of conserved proteins to infer the most probable amino acid decoding for each of the 64 codons.

Key Features:

• No requirement for sequence annotation: Operates on raw nucleotide sequences without requiring annotated coding regions.
• Taxonomic independence: Requires no prior taxonomic classification of the organism for analysis.
• Profile HMM alignments to conserved proteins: Uses alignments of profile hidden Markov models (HMMs) of conserved proteins against input sequences to infer the most likely amino acid decoding for each of the 64 codons.

Scientific Applications:

• Deciphering unusual genetic codes: Predicts alternative genetic code tables to identify nonstandard codon-to-amino-acid assignments.
• Studying evolutionary adaptations: Enables analysis of genetic code variation to investigate molecular evolution and adaptive changes.
• Metagenomics and unclassified sequences: Applies to metagenomic datasets by analyzing unannotated sequences without prior organism classification.

Methodology:

Alignments of profile HMMs of conserved proteins to input nucleotide sequences are used to predict the most probable amino acid assignment for each codon and reconstruct the genetic code table.