TFAM

TFAM classifies and predicts the functional identity of transfer RNAs (tRNAs) across genomes using automated statistical models that leverage comprehensive sequence features beyond anticodons.

Key Features:

• Automated statistical classification: Performs automated statistical assignment of tRNA identity and function with optimization for bacterial genomes.
• Sequence-feature analysis: Utilizes comprehensive sequence information beyond anticodons to determine tRNA identity determinants.
• Initiator tRNA prediction: Predicts initiator tRNAs across diverse genomic datasets.
• Nonstandard tRNA charging prediction: Predicts amino acid charging identities of nonstandard tRNAs such as suppressors.
• Pseudo-tRNA historical inference: Infers historical identity of pseudo-tRNAs from sequence signals.
• Detection of identity determinant variation: Detects variation in tRNA identity determinants among bacterial genomes.
• Evolutionary and lateral transfer detection: Enables detection of evolutionary adaptations and potential lateral gene transfers, exemplified by identification of a eukaryotic-like A73 identity element in an alpha-proteobacterial clade (including Rhizobiales, Caulobacter crescentus, Silicibacter pomeroyi) and associated eukaryotic-like HisRS integration.
• Robustness to sequencing errors: Provides resilience to sequencing errors for statistical analyses of tRNA sequences and functions.
• Model coverage: Includes updated models covering proteobacterial lysylated isoleucine tRNAs and preliminary eukaryotic and archaeal models.
• Genome-scale classification: Supports classification of all tRNA genes for certain bacterial taxa and genome-scale initiator tRNA predictions.

Scientific Applications:

• tRNA annotation: Functional annotation of tRNA genes, including charging identity and initiator status.
• Comparative genomics: Comparative analyses of tRNA identity rules and determinants across taxa.
• Evolutionary studies: Investigation of evolutionary adaptations and lateral gene transfer involving tRNA identity elements and aminoacyl-tRNA synthetases (e.g., HisRS).
• Statistical sequence analysis: Statistical analysis of tRNA sequence variation, function, and evolutionary patterns with robustness to sequencing errors.

Methodology:

Uses an automated statistical classification approach that leverages comprehensive tRNA sequence features (beyond anticodons) and organism-specific models, including proteobacterial, preliminary eukaryotic, and archaeal models.