MSstatsPTM

MSstatsPTM provides statistical methods for relative quantification and differential analysis of post-translational modifications (PTMs) in bottom-up LC-MS/MS proteomics data, supporting label-free and tandem mass tag (TMT)-based experiments.

Key Features:

• Experimental design requirement: Quantification of peptides with PTMs alongside unmodified peptides from the same proteins is required to disentangle PTM-specific changes from protein-level variation.
• Linear mixed effects modeling: Separate linear mixed effects models summarize peptide abundances for modified and unmodified sites.
• Confounder integration: Model-based inferences from PTM-level and protein-level analyses are combined to account for confounding between PTM abundance changes and overall protein abundance.
• Data type support: Supports both label-free and tandem mass tag (TMT)-based LC-MS/MS data.
• Validation and reproducibility: Performance has been evaluated on computer simulations, spike-in experiments with known ground truths, and biological experiments across organisms, modification types, and acquisition methods.
• Implementation: Implemented as an R/Bioconductor package.

Scientific Applications:

• Disease mechanism studies: Detects differential PTM abundance linked to disease-related proteomic changes.
• Cellular signaling pathway analysis: Quantifies PTM changes relevant to signaling pathway modulation.
• Protein function regulation: Assesses PTM-level regulation that affects protein activity or interactions.
• Fold-change estimation and differential PTM detection: Provides improved fold-change estimates and statistical detection of differential PTM abundance across experimental conditions.

Methodology:

Supports label-free and TMT-based LC-MS/MS data; fits separate linear mixed effects models to summarize peptide abundances for modified and unmodified sites; combines inferences from PTM-level and protein-level analyses to mitigate confounding.