Target Audience

Software & Data

Many software tools exist for differential proteomics data analysis, e.g. 

Here, we will illustrate all concepts using the msqrob2 software. The software can be used with R/Rmarkdown scripting or with a Graphical User Interface (GUI).

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.


Detailed Program

  1. Sources of variability in label-free proteomics experiments: lecture, [PDF]
  2. Experimental design concepts for proteomics data analysis: lecture, [PDF]
  3. Tutorial on msqrob2 with the GUI: lecture,[PDF]


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.


Creative Commons License

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.