Open AccessJUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Author(s) -
David Vanderwall,
Suresh Poudel,
Yingxue Fu,
Ji-Hoon Cho,
Timothy I. Shaw,
Ashutosh Mishra,
Anthony A. High,
Junmin Peng,
Yuxin Li
Publication year - 2021
Publication title -
journal of visualized experiments
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.596
H-Index - 91
ISSN - 1940-087X
DOI - 10.3791/62796
Subject(s) - proteome , proteomics , computational biology , computer science , software , visualization , protein expression , quantitative proteomics , profiling (computer programming) , bioinformatics , systems biology , cluster analysis , biology , data mining , artificial intelligence , genetics , gene , programming language , operating system
With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.