TRESS

TRESS detects N6-methyladenosine (m6A) methylation regions in mRNA from Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) data.

Key Features:

• Empirical Bayesian Hierarchical Model: Employs an empirical Bayesian hierarchical model that accounts for biological variance across replicates and other sources of variation in MeRIP-seq data.
• Shrinkage Estimation: Incorporates shrinkage estimation that borrows information across transcriptome-wide data to stabilize parameter estimates.
• Improved Accuracy and Efficiency: Demonstrated in simulation studies and analyses of real datasets to outperform existing peak calling methods in accuracy, robustness, and efficiency.

Scientific Applications:

• m6A profiling: Profiles N6-methyladenosine (m6A) methylation on mRNA to enable transcriptome-wide study of post-transcriptional epigenetic modifications.
• Disease and gene regulation studies: Facilitates investigation of associations between m6A modifications and diseases and analysis of m6A impacts on gene expression regulation and potential therapeutic targets.

Methodology:

Uses empirical Bayesian hierarchical modeling with shrinkage estimation to account for biological variance across replicates and stabilize parameter estimates in MeRIP-seq data; evaluated via simulation studies and analyses of real datasets.