ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

Minadakis, George; Sokratous, Kleitos

doi:10.1016/j.csbj.2020.06.036

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…There are many tools that facilitate these downstream analysis steps, some are provided as graphical user interfaces, e.g., Perseus, 24 ProtExA 28 and LFQ-Analyst, 29 or as R packages such as MSstats, 30 DEP, 31 NormalyzerDE 32 and protti. 33 However, many of these existing pipelines implement few algorithms for normalization and statistical analyses without the ability for the user community to apply additional algorithms (e.g., methods not supported when the pipeline was built/published, or novel methods published in years after).…”

Section: Introductionmentioning

confidence: 99%

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer’s Biomarker Data

Koopmans

Klaassen

et al. 2022

J. Proteome Res.

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In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

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Section: Introductionmentioning

confidence: 99%

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer’s Biomarker Data

Koopmans

Klaassen

et al. 2022

J. Proteome Res.

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“…Due to its modular design, most functions can be used independently of each other and can be applied to input data from many sources. Although protti does not provide a graphical user interface, unlike proteomics tools such as proteosign ( Efstathiou et al , 2017 ), Perseus ( Tyanova and Cox, 2018 ), Prostar ( Wieczorek et al , 2017 ), DAnTE ( Karpievitch et al , 2009 ), PIQMIe ( Kuzniar and Kanaar, 2014 ), StatQuant ( van Breukelen et al , 2009 ), LFQ-Analyst ( Shah et al , 2020 ), ProtExA ( Minadakis et al , 2020 ) and MSqRob ( Goeminne et al , 2018 ) this aids the seamless implementation of protti into any R data analysis workflow.…”

Section: Introductionmentioning

confidence: 99%

protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data

Quast

Schuster

Picotti

2021

Bioinformatics Advances

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Summary We present a flexible, user-friendly R package called protti for comprehensive quality control, analysis and interpretation of quantitative bottom-up proteomics data. protti supports the analysis of protein-centric data such as those associated with protein expression analyses, as well as peptide-centric data such as those resulting from limited proteolysis-coupled mass spectrometry analysis. Due to its flexible design, it supports analysis of label-free, data-dependent, data-independent and targeted proteomics datasets. protti can be run on the output of any search engine and software package commonly used for bottom-up proteomics experiments such as Spectronaut, Skyline, MaxQuant or Proteome Discoverer, adequately exported to table format. Availability protti is implemented as an open-source R package. Release versions are available via CRAN (https://CRAN.R-project.org/package=protti) and work on all major operating systems. The development version is maintained on GitHub (https://github.com/jpquast/protti). Full documentation including examples is provided in the form of vignettes on our package website (jpquast.github.io/protti/).

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“…Thus, the bottom-up approach is the most commonly used one in high-throughput proteomics ( 4 ). There are many available proteomic differential expression analysis tools, such as Perseus (probably the most popular one) ( 5 ), DanTE ( 6 ), Prostar ( 7 ), MsqRob ( 8 ), ProteoSign ( 9 ), MSstats ( 10 ), Rover ( 11 ), HiQuant ( 12 ), PIQMIe ( 13 ), Scaffold Q+S, ProtExA ( 14 ), StatQuant ( 15 ) etc. These tools display differences in terms of features, such as filtering, normalization, aggregation, statistical methods, types of analyses, data import/export formats, plots offered etc.…”

Section: Introductionmentioning

confidence: 99%

ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics

Theodorakis

Antonakis

Baltsavia

et al. 2021

Nucleic Acids Research

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Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.

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ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

Cited by 7 publications

References 37 publications

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer’s Biomarker Data

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer’s Biomarker Data

protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data

ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics

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