{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Arias-Gaguancela O"],"funding":["NIDDK NIH HHS"],"pubmed_abstract":["Mass 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 challenging 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 ope"],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2025.03.24.645047"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11974799"],"repository":["biostudies-literature"],"pubmed_title":["QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets."],"pmcid":["PMC11974799"],"funding_grant_id":["R01 DK098449"],"pubmed_authors":["Palii C","Ranish J","Nissa MU","Brand M","Arias-Gaguancela O"],"additional_accession":[]},"is_claimable":false,"name":"QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets.","description":"Mass 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 challenging 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 ope","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-04-18T03:22:05.55Z","creation":"2025-07-03T03:05:00.325Z"},"accession":"S-EPMC11974799","cross_references":{"pubmed":["40196523"],"doi":["10.1101/2025.03.24.645047"]}}