{"doi":"10.1002/pmic.70061","title":"JUMPshiny: A User‐Friendly Platform for Comprehensive Analysis and Visualization of Quantitative Proteomics Data","abstract":"Mass spectrometry-based quantitative proteomics has revolutionized our understanding of biological processes and unveiled the molecular mechanisms underlying various diseases. The analysis and visualization of quantitative proteomics data remain complex and require user-friendly tools with robust analytical capacities. In this study, we introduce JUMPshiny, a novel, interactive, and comprehensive web-service, that is built on R-Shiny and designed for processing and presenting quantitative proteomics data. JUMPshiny includes a wide range of visualizations and offers a streamlined workflow, including experimental design, data exploration, batch normalization, differential analysis, and enrichment analysis. Through examples, we demonstrate automated quality control, interactive data visualization, and customizable statistical analyses. Built on the R-Shiny framework, JUMPshiny integrates established libraries and packages to ensure computational robustness and reproducibility. Overall, JUMPshiny represents a powerful platform for proteomics data analysis for the research community. JUMPshiny is available at https://jumpshiny.genenetwork.org. The source code is available under MIT license at: https://github.com/Wanglab-UTHSC/JUMP_shiny.","journal":"PROTEOMICS","year":2025,"id":529137,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9502,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":97159,"name":"Yingxue Fu","orcid":"0000-0003-3052-4131","position":1,"is_corresponding":false},{"id":97160,"name":"Zuo‐Fei Yuan","orcid":"0000-0003-1252-6766","position":2,"is_corresponding":false},{"id":97161,"name":"Long Wu","orcid":"0000-0002-3566-9818","position":3,"is_corresponding":false},{"id":1219527,"name":"Dehui Kong","orcid":"0000-0003-1890-6911","position":4,"is_corresponding":false},{"id":846667,"name":"Ling Li","orcid":"0000-0001-6295-2128","position":5,"is_corresponding":false},{"id":110360,"name":"Zhiping Wu","orcid":"0000-0002-5554-3681","position":6,"is_corresponding":false},{"id":24530,"name":"Pjotr Prins","orcid":"0000-0002-8021-9162","position":7,"is_corresponding":false},{"id":97162,"name":"Junmin Peng","orcid":"0000-0003-0472-7648","position":8,"is_corresponding":false},{"id":97163,"name":"Xusheng Wang","orcid":"0000-0002-1759-9588","position":9,"is_corresponding":false},{"id":1341102,"name":"Aijun Zhang","orcid":"0000-0003-2879-9961","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T02:50:52.565868Z","pmid":"41121580","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}