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Hsiao Y, Zhang H, Li GX, Deng Y, Yu F, Valipour Kahrood H, Steele JR, Schittenhelm RB, Nesvizhskii AI. Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst. J Proteome Res 2024. [PMID: 39254081 DOI: 10.1021/acs.jproteome.4c00294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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.
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Affiliation(s)
- Yi Hsiao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Haijian Zhang
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ginny Xiaohe Li
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Yamei Deng
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hossein Valipour Kahrood
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Joel R Steele
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ralf B Schittenhelm
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
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Warshanna A, Orsburn BC. SCP Viz - A universal graphical user interface for single protein analysis in single cell proteomics datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555397. [PMID: 37693496 PMCID: PMC10491148 DOI: 10.1101/2023.08.29.555397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Single cell proteomics (SCP) requires the analysis of dozens to thousands of single human cells to draw biological conclusions. However, assessing of the abundance of single proteins in output data presents a considerable challenge, and no simple universal solutions currently exist. To address this, we developed SCP Viz, a statistical package with a graphical user interface that can handle small and large scale SCP output from any instrument or data processing software. In this software, the abundance of individual proteins can be plotted in a variety of ways, using either unadjusted or normalized outputs. These outputs can also be transformed or imputed within the software. SCP Viz offers a variety of plotting options which can help identify significantly altered proteins between groups, both before and after quantitative transformations. Upon the discovery of subpopulations of single cells, users can easily regroup the cells of interest using straightforward text-based filters. When used in this way, SCP Viz allows users to visualize proteomic heterogeneity at the level of individual proteins, cells, or identified subcellular populations. SCP Viz is compatible with output files from MaxQuant, FragPipe, SpectroNaut, and Proteome Discoverer, and should work equally well with other formats. SCP Viz is publicly available at https://github.com/orsburn/SCPViz. For demonstrations, users can download our test data from GitHub and use an online version that accepts user input for analysis at https://orsburnlab.shinyapps.io/SCPViz/.
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Affiliation(s)
- Ahmed Warshanna
- The Department of Pharmacology and Molecular Sciences; The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205
| | - Benjamin C. Orsburn
- The Department of Pharmacology and Molecular Sciences; The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205
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Bai M, Deng J, Dai C, Pfeuffer J, Sachsenberg T, Perez-Riverol Y. LFQ-Based Peptide and Protein Intensity Differential Expression Analysis. J Proteome Res 2023. [PMID: 37220883 DOI: 10.1021/acs.jproteome.2c00812] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.
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Affiliation(s)
- Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
| | - Jingwen Deng
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Chengxin Dai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
| | - Julianus Pfeuffer
- Algorithmic Bioinformatics, Freie Universität Berlin, Berlin 14195, Germany
- Visualization and Data Analysis, Zuse Institute Berlin, Berlin 14195, Germany
| | - Timo Sachsenberg
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72076, Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hixton, Cambridge CB10 1SD, United Kingdom
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Didusch S, Madern M, Hartl M, Baccarini M. amica: an interactive and user-friendly web-platform for the analysis of proteomics data. BMC Genomics 2022; 23:817. [PMID: 36494623 PMCID: PMC9733095 DOI: 10.1186/s12864-022-09058-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Quantitative proteomics has become an increasingly prominent tool in the study of life sciences. A substantial hurdle for many biologists are, however, the intricacies involved in the associated high throughput data analysis. RESULTS In order to facilitate this task for users with limited background knowledge, we have developed amica, a freely available open-source web-based software that accepts proteomic input files from different sources. amica provides quality control, differential expression, biological network and over-representation analysis on the basis of minimal user input. Scientists can use amica's query interface interactively to compare multiple conditions and rapidly identify enriched or depleted proteins. They can visualize their results using customized output graphics, and ultimately export the results in a tab-separated format that can be shared with collaborators. The code for the application, input data and documentation can be accessed online at https://github.com/tbaccata/amica and is also incorporated in the web application. CONCLUSIONS The strong emphasis on dynamic user interactions, the integration of various databases and the option to download processed data, facilitate the analysis of complex proteomic data for both first-time users and experienced bioinformaticians. A freely available version of amica is available at https://bioapps.maxperutzlabs.ac.at/app/amica .
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Affiliation(s)
- Sebastian Didusch
- grid.473822.80000 0005 0375 3232Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria ,grid.22937.3d0000 0000 9259 8492Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria ,grid.10420.370000 0001 2286 1424University of Vienna, Center for Molecular Biology, Department of Microbiology, Immunobiology and Genetics, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
| | - Moritz Madern
- grid.473822.80000 0005 0375 3232Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria ,grid.10420.370000 0001 2286 1424University of Vienna, Center for Molecular Biology, Department of Biochemistry and Cell Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
| | - Markus Hartl
- grid.473822.80000 0005 0375 3232Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria ,grid.10420.370000 0001 2286 1424University of Vienna, Center for Molecular Biology, Department of Biochemistry and Cell Biology, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
| | - Manuela Baccarini
- grid.473822.80000 0005 0375 3232Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria ,grid.10420.370000 0001 2286 1424University of Vienna, Center for Molecular Biology, Department of Microbiology, Immunobiology and Genetics, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
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Bloom J, Triantafyllidis A, Quaglieri A, Burton Ngov P, Infusini G, Webb A. Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free Data. J Proteome Res 2021; 20:5180-5188. [PMID: 34647461 DOI: 10.1021/acs.jproteome.1c00683] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.
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Affiliation(s)
- Joseph Bloom
- Mass Dynamics, C/O Hub Southern Cross, Level 2, 696 Bourke Street, Melbourne, Victoria 3000, Australia
| | - Aaron Triantafyllidis
- Mass Dynamics, C/O Hub Southern Cross, Level 2, 696 Bourke Street, Melbourne, Victoria 3000, Australia
| | - Anna Quaglieri
- Mass Dynamics, C/O Hub Southern Cross, Level 2, 696 Bourke Street, Melbourne, Victoria 3000, Australia
| | - Paula Burton Ngov
- Mass Dynamics, C/O Hub Southern Cross, Level 2, 696 Bourke Street, Melbourne, Victoria 3000, Australia
| | - Giuseppe Infusini
- Mass Dynamics, C/O Hub Southern Cross, Level 2, 696 Bourke Street, Melbourne, Victoria 3000, Australia.,The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Andrew Webb
- Mass Dynamics, C/O Hub Southern Cross, Level 2, 696 Bourke Street, Melbourne, Victoria 3000, Australia.,The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Victoria 3010, Australia
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