SPECtre

SPECtre applies spectral coherence-based classification to ribosome profiling (Ribo-seq) sequencing data to model tri-nucleotide periodicity and identify actively translating regions.

Key Features:

• Tri-Nucleotide Periodicity Modeling: Models the tri-nucleotide periodicity inherent in ribosome-protected fragments from Ribo-seq data to assess translation dynamics.
• Spectral Coherence-Based Classifier: Employs spectral coherence on normalized ribosome profiling read coverage across rolling windows along transcripts and compares signals to an idealized reference to distinguish active translation from background noise without requiring matched mRNA-Seq data.
• Detection Accuracy and False Discovery Rate: Comparative analyses report improved accuracy in detecting active translation with a low false discovery rate relative to existing methods.
• Resource Efficiency: Optimized for reduced runtime and memory usage while performing spectral coherence-based analysis.

Scientific Applications:

• Measurement of active protein translation: Quantifies regions of active translation from Ribo-seq data.
• Gene expression and translational control studies: Supports analysis of translational control mechanisms and gene expression regulation.
• Noise discrimination in transcriptome-wide studies: Separates translation signals from background noise in transcriptome-wide Ribo-seq analyses.
• Disease-related translation research: Facilitates investigation of diseases involving translation dysregulation.

Methodology:

Models tri-nucleotide periodicity using spectral coherence-based classification applied to normalized ribosome profiling read coverage across rolling windows along transcripts, comparing observed signals to an idealized reference signal and operating without matched mRNA-Seq data.