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[Preprint]. 2024 Mar 10:2024.03.05.583643.
doi: 10.1101/2024.03.05.583643.

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

Yi Hsiao  1 , Haijian Zhang  2 , Ginny Xiaohe Li  3 , Yamei Deng  3 , Fengchao Yu  3 , Hossein Valipour Kahrood  2   4 , Joel R Steele  2 , Ralf B Schittenhelm  2 , Alexey I Nesvizhskii  1   3
Affiliations

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

Yi Hsiao et al. bioRxiv. .

Update in

Abstract

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Keywords: AP-MS; FragPipe; LiP-MS; bioinformatics; downstream analysis; open source; protein phosphorylation; software.

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Conflict of interest statement

CONFLICT OF INTEREST A.I.N. and F.Y. receive royalties from the University of Michigan for the sale of MSFragger and IonQuant software licenses to commercial entities. All license transactions are managed by the University of Michigan Innovation Partnerships office and all proceeds are subject to university technology transfer policy. The other authors have no competing interests to declare.

Figures

Figure 1.
Figure 1.
Overview of FragPipe-Analyst. LFQ: data-dependent label-free quantification, TMT: tandem mass tag, DIA: data-independent acquisition LFQ.
Figure 2.
Figure 2.
Example analysis results of global proteome ccRCC dataset obtained from FragPipe-Analyst. (a) PCA plot (b) Correlation matrix plot. Asterisks denote samples of dissimilar patterns. (c) Boxplot shows selected markers of renal cell carcinomas. Asterisks denote the expression of the papillary renal cell carcinoma sample (C3N-00832-T) (d) Heatmap based on differentially expressed proteins. Asterisk denotes the sample with dissimilar pattern. (e) Volcano plot (f) Over-representation test result for upregulated proteins identified in (e) against Hallmark gene set. Only proteins with log2 fold change more than 0.7 and adjusted p value lower than 0.05 were considered.
Figure 3.
Figure 3.
(a) Volcano plot comparing effect of CCND1 bait versus control. (b) Upset plot shows number of identified proteins across different conditions. (c) Screenshot of the filtering panel of the presence/absence tab and the Venn diagram (d) after filtering.
Figure 4.
Figure 4.
Volcano plot showing comparison of peptide intensities between rapamycin treated (RPM) and DMSO control. One peptide of FKBP1A (GWEEGVAQMSVGQR) is highlighted in red because it’s selected in the result table. Other peptides of FKBP1A are colored in blue.
Figure 5.
Figure 5.
Results for ccRCC site-specific protein phosphorylation analysis using FragPipeAnalystR (a) The flow diagram of data processing (b) PCA plot of protein phosphorylation sites after normalization. (c) Boxplot shows abundance of selected phosphorylation sites PKM (P14618_Y148), MAPK1(P28482_Y187), and EIF4EBP1 (Q13541_S65) before and after protein abundance normalization. Results of site-specific enrichment analysis of PTM-SEA (d) and the Kinase library (e). Both results showed the activation of AKT in ccRCC.
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