MCQR: Enhancing the Processing and Analysis of Quantitative Proteomics Data by Incorporating Chromatography and Mass Spectrometry Information.

Abstract

In the field of proteomics, generating biologically relevant results from mass spectrometry (MS) signals remains a challenging task. This is partly due to the fact that the computational strategies for converting MS signals into biologically interpretable data depend heavily on the MS acquisition method. Additionally, the processing and the analysis of these data vary depending on whether the proteomic experiment was performed with or without labeling, and with or without fractionation. Several R packages have been developed for processing and analyzing MS data, but they only incorporate identification and quantification data; none of them takes into account other invaluable information collected during MS runs. To address this limitation, we introduce MCQR, an alternative R package for the in-depth exploration, processing, and analysis of quantitative proteomics data generated from either data-dependent or data-independent acquisition methods. MCQR leverages experimental retention time measurements for quality control, data filtering, and processing. Its modular architecture offers flexibility to accommodate various types of proteomics experiments, including label-free, label-based, fractionated, or those enriched for specific post-translational modifications. Its functions, designed as simple building blocks, are user-friendly, making it easy to test parameters and methods, and to construct customized analysis scenarios. These unique features position MCQR as a comprehensive toolbox, perfectly suited to the specific needs of MS-based proteomics experiments.