tRanslatome

tRanslatome performs simultaneous pairwise analysis of transcriptome, translatome (polysomally loaded mRNAs), and proteome datasets within the R/Bioconductor framework to identify differentially expressed genes and predict translational regulatory elements.

Key Features:

• Simultaneous Multi-omics Analysis: Enables concurrent pairwise comparison of transcriptomic (total mRNA), translatomic (polysomally loaded mRNAs), and proteomic data.
• Statistical Method Integration: Integrates a wide array of statistical methods tailored for high-throughput data to compare differential expression across biological conditions.
• Differential Expression and Theme Enrichment Analysis: Identifies differentially expressed genes (DEGs) and performs differential enrichment analysis of biological themes.
• Prediction of Translational Regulatory Elements: Predicts translational regulatory elements on mRNA sequences to explore post-transcriptional regulation.

Scientific Applications:

• Transcription–Translation Interplay: Supports studies dissecting regulatory relationships between transcription and translation across transcriptome, translatome, and proteome levels.
• Regulatory Mechanism Discovery: Facilitates identification of mechanisms governing gene expression by comparing expression changes across omics layers.
• Post-transcriptional Regulation Analysis: Enables analysis of how predicted translational regulatory elements influence mRNA stability and protein synthesis.

Methodology:

Performs simultaneous pairwise analyses of transcriptome, translatome, and proteome data, integrates raw '-omics' information from high-throughput technologies, and applies a robust statistical framework to compare biological conditions and predict translational regulatory elements.