Post-translational modifications (PTMs) play a crucial role in dynamically altering proteomes and are key regulators for a multitude of complex processes in eukaryotic cells. Affinity enrichment followed by quantitative Mass Spectrometry (MS) is currently the most successful approach to systematically identify PTMs and quantify their relative abundance with great depth and throughput. Relative changes in PTM site abundance, quantified by MS, are traditionally modeled with a two-sample t-test, comparing either the mean intensity or the modified to unmodified ratio of representative peptides. However, the interpretation of changes at a single modification site in a typical bottom-up proteomics workflow is complicated by sparse coverage and confounded by both changes in overall protein abundance and variability in enrichment efficiency. We're proposing an alternative statistical approach to model relative abundance changes for modification sites, which explicitly incorporates major sources of variability and confounding factors present in PTM experiments. Moreover, the general statistical framework underlying the proposed approach allows for natural extensions to complex experimental designs including multiple conditions and multiple batches. We plan to evaluate our proposed approach by comparing it to the results of a naive t-test using computer simulations and a custom-designed benchmark experiment.
[doi:10.25345/C55H7BV9K]
[dataset license: CC0 1.0 Universal (CC0 1.0)]
Keywords: quantitative mass spectrometry ; benchmark ; Spike-In ; Ubiquitination ; MSstatsPTM
Principal Investigators: (in alphabetical order) |
Meena Choi, Genentech, United States Olga Vitek, Khoury College of Computer Sciences, Northeastern University, United States |
Submitting User: | mnchoi |
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