tSFM

tSFM analyzes, visualizes, and statistically evaluates single- and paired-site class-informative features (CIFs) of transfer RNAs (tRNAs) and related sequence families (RNA, protein, genes, or genomic elements) to characterize structure-function relationships and evolutionary divergence across taxa.

Key Features:

• Computation of CIFs: Computes class-informative features for single sites and paired sites within sequence families.
• Structure-conditioned information statistics: Uses structure-conditioned information statistics to predict and quantify CIFs.
• Visualization: Produces visual representations of predicted CIFs and their evolutionary divergences.
• Permutation significance testing: Evaluates CIF significance using permutation P-values and provides multiple estimators.
• Peaks-over-Threshold method: Implements the Peaks-over-Threshold approach (Knijnenburg et al. 2009) to improve speed and accuracy of permutation P-value calculations.
• Confidence intervals: Reports confidence intervals for permutation P-value estimators.
• Multi-threaded implementation: Supports multi-threaded computation for parallel processing.
• Python and C implementation: Implemented in Python 3 with compiled C extensions for performance optimization.

Scientific Applications:

• Evolutionary analysis of tRNAs: Characterizes how tRNA CIFs have evolved across different taxa.
• Structure-function mapping: Links tRNA structural features to functional class assignments via CIFs.
• Comparative analyses across sequence families: Applies CIF analysis to RNA, protein, gene, or other genomic element families to compare structural signals.
• Statistical assessment of divergence: Assesses evolutionary significance of CIF divergences between taxa using improved permutation methods.

Methodology:

Computes and visualizes CIFs using structure-conditioned information statistics, evaluates significance with permutation P-values including multiple estimators and confidence intervals, and applies the Peaks-over-Threshold method (Knijnenburg et al. 2009) to speed and improve permutation P-value calculations; implemented in Python 3 with compiled C extensions and multi-threaded processing.