Search bioRxiv⌕ Search

Biology subjects

Ferretti, D.

Publications and source records attributed to Ferretti, D..

2 recordsLinked to original sources
bioRxiv2024

ProteoPlotter: an executable proteomics visualization tool compatible with Perseus

Mass spectrometry-based proteomics experiments produce complex datasets requiring robust statistical testing and effective visualization tools to ensure meaningful conclusions are drawn. The publicly-available proteomics data analysis platform, Perseus, is extensively used to perform such tasks, but opportunities to enhance visualization tools and promote accessibility of the data exist. In this study, we developed ProteoPlotter, a user-friendly, executable tool to complement Perseus for visualization of proteomics datasets. ProteoPlotter is built on the Shiny framework for R programming and enables illustration of multi-dimensional proteomics data. ProteoPlotter provides mapping of one-dimensional enrichment analyses, enhanced adaptability of volcano plots through incorporation of Gene Ontology terminology, visualization of 95% confidence intervals in principal component analysis plots using data ellipses, and customizable features. ProteoPlotter is designed for intuitive use by biological and computational researchers alike, providing descriptive instructions (i.e., Help Guide) for preparing and uploading Perseus output files. Herein, we demonstrate the application of ProteoPlotter towards microbial proteome remodeling under altered nutrient conditions and highlight the diversity of visualizations enabled with the platform for improved biological characterization. Through its comprehensive data visualization capabilities, linked to the power of Perseus data handling and statistical analyses, ProteoPlotter facilitates a deeper understanding of proteomics data to drive new biological discoveries.

bioinformatics↗
bioRxiv2024

Isobaric labeling update in MaxQuant

We present an update of the MaxQuant software for isobaric labeling data and evaluate its performance on benchmark datasets. Impurity correction factors can be applied to labels mixing C- and N-type reporter ions, such as TMT Pro. Application to a single-cell multi-species mixture benchmark shows high accuracy of the impurity-corrected results. TMT data recorded with FAIMS separation can be analyzed directly in MaxQuant without splitting the raw data into separate files per FAIMS voltage. Weighted median normalization, is applied to several datasets, including large-scale human body atlas data. In the benchmark datasets the weighted median normalization either removes or strongly reduces the batch effects between different TMT plexes and results in clustering by biology. In datasets including reference channels, we find that weighted median normalization performs as well or better when the reference channels are ignored and only the sample channel intensities are used, suggesting that the measurement of reference channels is unnecessary when using weighted median normalization in MaxQuant. We demonstrate that MaxQuant including the weighted median normalization performs well on multi-notch MS3 data, as well as on phosphorylation data. MaxQuant is freely available for any purpose and can be downloaded from https://www.maxquant.org/.

bioinformatics↗