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Methods Mol Biol. 2019;1959:225-246. doi: 10.1007/978-1-4939-9164-8_15.

Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR.

Author information

1
Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France.
2
Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France. thomas.burger@cea.fr.
3
CNRS, BIG-BGE, Grenoble, France. thomas.burger@cea.fr.

Abstract

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

KEYWORDS:

Data processing; Differential analysis; Label-free proteomics; Relative quantification; Statistical software

PMID:
30852826
DOI:
10.1007/978-1-4939-9164-8_15
[Indexed for MEDLINE]

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