{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Oom AL"],"funding":["NCATS NIH HHS","NIAID NIH HHS","NIAMS NIH HHS","NIGMS NIH HHS"],"pagination":["100194"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8956815"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(3)"],"pubmed_abstract":["As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming supp"],"journal":["Molecular & cellular proteomics : MCP"],"pubmed_title":["Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression."],"pmcid":["PMC8956815"],"funding_grant_id":["F31 AI141111","P50 AI150476","UL1 TR001442","K08 AI112394","P30 AI036214","T32 GM007752","T32 AR064194","U19 AI135990","R01 AI129706","U19 AI135972"],"pubmed_authors":["Richards A","Krogan NJ","Oom AL","Lewinski MK","Guatelli J","Gonzalez DJ","Shams-Ud-Doha K","Stoneham CA","Wozniak JM"],"additional_accession":[]},"is_claimable":false,"name":"Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression.","description":"As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming supp","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2026-05-30T20:56:42.451Z","creation":"2025-04-04T10:01:51.235Z"},"accession":"S-EPMC8956815","cross_references":{"pubmed":["35017099"],"doi":["10.1016/j.mcpro.2022.100194"]}}