panoramaProteomicsOmar Arias-GaguancelaHomo Sapienshttps://panoramaweb.org/QuickProt_datasets.urlomar.arias-gaguancela@isbscience.orgInstitute for Systems BiologyPanoramaPublicMass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results, remains cumbersome due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.QuickProt: A Bioinformatics and Visualization Tool for DIA and PRM Mass Spectrometry-Based Proteomics Datasets.Arias-Gaguancela Omar O, Palii Carmen C, Nissa Mehar Un MU, Brand Marjorie M, Ranish Jeffrey JfalseQuickProt: a bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasetsMass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Although upstream processing of MS-derived data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various platforms, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results remain challenging due to the lack of integrated tools to facilitate the analyses. To facilitate downstream interrogation of data-independent acquisition (DIA) and parallel reaction monitoring (PRM) datasets, we developed a series of Python-based Google Colab notebooks called QuickProt. These pipelines were designed so that users with no coding expertise can utilize the tools. Alternatively, as open-source code, users can customize the QuickProt notebooks and incorporate them into their workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets of a time course of erythropoiesis. The analysis outputs in annotated tables and publication-ready figures revealed a dynamic rearrangement of the proteome during erythroid differentiation, with the abundances of proteins linked to gene regulatory, metabolic, and chromatin remodeling pathways increasing early in the process. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.Fri Jun 12 00:00:00 BST 2026PXD060333960640908717