Mass spectrometry based proteomic experiments generate ever larger datasets and, as a consequence, complex data interpretation challenges. This course focuses on the statistical concepts for peptide identification, quantification, and differential analysis. Moreover, more advanced experimental designs and blocking will also be introduced. The core focus will be on shotgun proteomics data, and quantification using label-free precursor peptide (MS1) ion intensities. The course will rely exclusively on free and userfriendly opensource tools in R/Bioconductor. The course will provide a solid basis for beginners, but will also bring new perspectives to those already familiar with standard data interpretation procedures in proteomics.
Students can sharpen their background knowledge on Mass Spectrometry, Proteomics & Bioinformatics for Proteomics here: Mass Spectrometry and Bioinformatics for Proteomics
This course is oriented towards biologists and bioinformaticians with a particular interest in differential analysis for quantitative proteomics.
According to the target audience of the course we either work with a graphical user interface (GUI) in a R/shiny App msqrob2gui e.g.
or with R/markdowns scripts e.g.
Note, that users who develop R/markdown scripts can access data both from the web or from disk within their scripts. So they do not need to download the data first. The msqrob2gui Shiny App only works with data that is available on disk.
If you encounter problems related to the course material (e.g. package installation problems, bugs in the code, typos, …), please consider posting an issue on GitHub.
A special thanks to Pedro Fernandes, Coordinator of the GTPB Bioinformatics Training Programme, Instituto Gulbenkian de Ciencia, who radically changed our view on teaching and immersed us in the teaching method that we use in this course.
This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Goeminne, L. J. E., A. Sticker, L. Martens, K. Gevaert, and L. Clement. 2020. “MSqRob Takes the Missing Hurdle: Uniting Intensity- and Count-Based Proteomics.” Anal Chem 92 (9): 6278–87.
Goeminne, L. J., K. Gevaert, and L. Clement. 2016. “Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.” Mol Cell Proteomics 15 (2): 657–68.
Sticker, A., L. Goeminne, L. Martens, and L. Clement. 2020. “Robust Summarization and Inference in Proteome-wide Label-free Quantification.” Mol Cell Proteomics 19 (7): 1209–19.