Computational and Statistical Framework for Quantitative Proteomics Analysis.

Authors

Ismail Kirrout, Nuria Montes

Published in

Methods in molecular biology (Clifton, N.J.). Volume 3070. Pages 349-395.

Abstract

Mass spectrometry-based quantitative proteomics usually produces large datasets that require exhaustive analysis to extract underlying biological information. This chapter presents a step-by-step pipeline for the statistical and computational analysis of such data, oriented and generalizable to any mass spectrometry-derived proteomic dataset. These steps include: (i) data preprocessing and quality control, (ii) identification of differentially abundant proteins through statistical modeling, (iii) functional enrichment analyses, including Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) to integrate proteomic changes in the context of biological processes and pathways, and, finally, (iv) interactome (protein-protein interaction) construction and visualization to situate proteomic alterations within signaling networks. Throughout, reproducible off-the-shelf R code and practical guidance for each step are provided, facilitating an end-to-end analysis from raw proteomic data to the biological interpretation, illustrated with visualization examples and best-practice recommendations. The complete script and necessary files are freely available at https://github.com/UMBB-IIS-Princesa/Quantitative-Proteomics-Pipeline.

PMID:
42681023
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.