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Related Concept Videos

Proteomics01:33

Proteomics

7.7K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Related Experiment Video

Updated: Aug 18, 2025

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis
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MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass

Devon Kohler1, Tsung-Heng Tsai2, Erik Verschueren3

  • 1Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, USA.

Molecular & Cellular Proteomics : MCP
|December 10, 2022
PubMed
Summary

This study introduces MSstatsPTM, a statistical framework for analyzing posttranslational modifications (PTMs) using liquid chromatography-mass spectrometry (LC-MS/MS). It improves the detection of differential PTM abundance by integrating all available data and accounting for confounding factors.

Keywords:
bioinformatics softwaremass spectrometryposttranslational modificationsquantificationstatistics

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Biostatistics

Background:

  • Liquid chromatography-mass spectrometry (LC-MS/MS) is widely used for posttranslational modification (PTM) analysis.
  • Statistical challenges in PTM data analysis include low abundance, limited peptide observation, and confounding protein/PTM abundance.
  • Existing methods struggle to integrate all relevant information and account for complex variations.

Purpose of the Study:

  • To develop a versatile, accurate, and reproducible statistical framework for detecting differential PTM abundance.
  • To address the statistical challenges in LC-MS/MS-based PTM quantification.
  • To provide a robust methodology that integrates PTM and protein-level abundance information.

Main Methods:

  • Proposed a statistical framework requiring experimental design that quantifies peptides with and without PTMs.
  • Utilized separate linear mixed-effects models for peptides with and without modification sites.
  • Combined model-based inferences for PTM and protein abundances to manage confounding.
  • Supported both label-free and tandem mass tag (TMT)-based LC-MS/MS acquisitions.

Main Results:

  • Demonstrated improved fold change estimation and detection of differential PTM abundance.
  • Evaluations on simulations, spike-in, and biological experiments confirmed framework's efficacy.
  • The framework showed superior performance compared to current approaches.

Conclusions:

  • The proposed statistical framework effectively addresses key challenges in differential PTM abundance analysis.
  • MSstatsPTM provides a reliable tool for PTM quantification in various experimental settings.
  • The open-source R/Bioconductor package MSstatsPTM facilitates reproducible PTM research.