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Pietz T, Gupta S, Schlaffner CN, Ahmed S, Steen H, Renard BY, Baum K. PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks. Bioinformatics 2024; 40:ii70-ii78. [PMID: 39230699 PMCID: PMC11373339 DOI: 10.1093/bioinformatics/btae389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024] Open
Abstract
MOTIVATION Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. RESULTS To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user's needs based on the reliability of imputed values. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/DILiS-lab/pepermint.
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Affiliation(s)
- Tobias Pietz
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
| | - Sukrit Gupta
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Department of Computer Science and Engineering, Indian Institute of Technology, Ropar, Rupnagar, 140001, India
| | - Christoph N Schlaffner
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States
| | - Saima Ahmed
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States
| | - Hanno Steen
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States
| | - Bernhard Y Renard
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Windreich Department for Artificial Intelligence and Human Health and Hasso Plattner Institute at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, United States
| | - Katharina Baum
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Windreich Department for Artificial Intelligence and Human Health and Hasso Plattner Institute at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, United States
- Department of Mathematics and Computer Science, Free University Berlin, Berlin, 14195, Germany
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2
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Madern M, Reiter W, Stanek F, Hartl N, Mechtler K, Hartl M. A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics. Mol Cell Proteomics 2024; 23:100694. [PMID: 38097181 PMCID: PMC10828822 DOI: 10.1016/j.mcpro.2023.100694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/01/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024] Open
Abstract
Multiplex proteomics using isobaric labeling tags has emerged as a powerful tool for the simultaneous relative quantification of peptides and proteins across multiple experimental conditions. However, the quantitative accuracy of the approach is largely compromised by ion interference, a phenomenon that causes fold changes to appear compressed. The degree of compression is generally unknown, and the contributing factors are poorly understood. In this study, we thoroughly characterized ion interference at the MS2 level using a defined two-proteome experimental system with known ground-truth. We discovered remarkably poor agreement between the apparent precursor purity in the isolation window and the actual level of observed reporter ion interference in MS2 scans-a discrepancy that we found resolved by considering cofragmentation of peptide ions hidden within the spectral "noise" of the MS1 isolation window. To address this issue, we developed a regression modeling strategy to accurately predict reporter ion interference in any dataset. Finally, we demonstrate the utility of our procedure for improved fold change estimation and unbiased PTM site-to-protein normalization. All computational tools and code required to apply this method to any MS2 TMT dataset are documented and freely available.
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Affiliation(s)
- Moritz Madern
- Max Perutz Labs, Mass Spectrometry Facility, Vienna Biocenter Campus (VBC), Vienna, Austria; Department for Biochemistry and Cell Biology, Center for Molecular Biology, University of Vienna, Vienna Biocenter Campus (VBC), Vienna, Austria
| | - Wolfgang Reiter
- Max Perutz Labs, Mass Spectrometry Facility, Vienna Biocenter Campus (VBC), Vienna, Austria; Department for Biochemistry and Cell Biology, Center for Molecular Biology, University of Vienna, Vienna Biocenter Campus (VBC), Vienna, Austria
| | - Florian Stanek
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter Campus (VBC), Vienna, Austria
| | - Natascha Hartl
- Max Perutz Labs, Mass Spectrometry Facility, Vienna Biocenter Campus (VBC), Vienna, Austria
| | - Karl Mechtler
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter Campus (VBC), Vienna, Austria
| | - Markus Hartl
- Max Perutz Labs, Mass Spectrometry Facility, Vienna Biocenter Campus (VBC), Vienna, Austria; Department for Biochemistry and Cell Biology, Center for Molecular Biology, University of Vienna, Vienna Biocenter Campus (VBC), Vienna, Austria.
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3
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Kuo TY, Wang JH, Huang YW, Sung TY, Chen CT. Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter. Sci Rep 2023; 13:14119. [PMID: 37644119 PMCID: PMC10465558 DOI: 10.1038/s41598-023-41124-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.
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Affiliation(s)
- Tzu-Yun Kuo
- Department of Biochemical Science and Technology, National Taiwan University, Taipei, 10617, Taiwan
| | - Jen-Hung Wang
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Yung-Wen Huang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan.
| | - Ching-Tai Chen
- Department of Bioinformatics and Biomedical Engineering, Asia University, Taichung, 41354, Taiwan.
- Center for Precision Health Research, Asia University, Taichung, 41354, Taiwan.
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4
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Martin EA, Fulcher JM, Zhou M, Monroe ME, Petyuk VA. TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis. J Proteome Res 2023; 22:399-409. [PMID: 36631391 DOI: 10.1021/acs.jproteome.2c00570] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.
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Affiliation(s)
- Evan A Martin
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - James M Fulcher
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - Mowei Zhou
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington99352, United States
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Tsiamis V, Schwämmle V. VIQoR: a web service for visually supervised protein inference and protein quantification. Bioinformatics 2022; 38:2757-2764. [PMID: 35561162 DOI: 10.1093/bioinformatics/btac182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 03/07/2022] [Accepted: 03/22/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization. RESULTS In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design. AVAILABILITY AND IMPLEMENTATION VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vasileios Tsiamis
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
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6
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Chen CT, Wang JH, Cheng CW, Hsu WC, Ko CL, Choong WK, Sung TY. Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage. Sci Rep 2021; 11:2233. [PMID: 33500498 PMCID: PMC7838301 DOI: 10.1038/s41598-021-81740-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html.
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Affiliation(s)
- Ching-Tai Chen
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
| | - Jen-Hung Wang
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 115, Taiwan.,Institute of Biomedical Informatics, National Yang-Ming University, Taipei, 112, Taiwan
| | - Cheng-Wei Cheng
- Genomics Research Center, Academia Sinica, Taipei, 115, Taiwan
| | - Wei-Che Hsu
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
| | - Chu-Ling Ko
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
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7
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Huang T, Choi M, Tzouros M, Golling S, Pandya NJ, Banfai B, Dunkley T, Vitek O. MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures. Mol Cell Proteomics 2020; 19:1706-1723. [PMID: 32680918 PMCID: PMC8015007 DOI: 10.1074/mcp.ra120.002105] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/09/2020] [Indexed: 11/06/2022] Open
Abstract
Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.
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Affiliation(s)
- Ting Huang
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Meena Choi
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Manuel Tzouros
- Roche Pharma Research and Early Development, Pharmaceutical Sciences-BiOmics and Pathology, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Sabrina Golling
- Roche Pharma Research and Early Development, Pharmaceutical Sciences-BiOmics and Pathology, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Nikhil Janak Pandya
- Roche Pharma Research and Early Development, Pharmaceutical Sciences-BiOmics and Pathology, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Balazs Banfai
- Roche Pharma Research and Early Development, Pharmaceutical Sciences-BiOmics and Pathology, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Tom Dunkley
- Roche Pharma Research and Early Development, Pharmaceutical Sciences-BiOmics and Pathology, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
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Matthiesen R, Carvalho AS. Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry. Methods Mol Biol 2020; 2051:161-197. [PMID: 31552629 DOI: 10.1007/978-1-4939-9744-2_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.
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Affiliation(s)
- Rune Matthiesen
- Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisboa, Portugal
| | - Ana Sofia Carvalho
- Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisboa, Portugal.
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Griss J, Vinterhalter G, Schwämmle V. IsoProt: A Complete and Reproducible Workflow To Analyze iTRAQ/TMT Experiments. J Proteome Res 2019; 18:1751-1759. [PMID: 30855969 PMCID: PMC6456869 DOI: 10.1021/acs.jproteome.8b00968] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Indexed: 12/15/2022]
Abstract
Reproducibility has become a major concern in biomedical research. In proteomics, bioinformatic workflows can quickly consist of multiple software tools each with its own set of parameters. Their usage involves the definition of often hundreds of parameters as well as data operations to ensure tool interoperability. Hence, a manuscript's methods section is often insufficient to completely describe and reproduce a data analysis workflow. Here we present IsoProt: A complete and reproducible bioinformatic workflow deployed on a portable container environment to analyze data from isobarically labeled, quantitative proteomics experiments. The workflow uses only open source tools and provides a user-friendly and interactive browser interface to configure and execute the different operations. Once the workflow is executed, the results including the R code to perform statistical analyses can be downloaded as an HTML document providing a complete record of the performed analyses. IsoProt therefore represents a reproducible bioinformatics workflow that will yield identical results on any computer platform.
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Affiliation(s)
- Johannes Griss
- EMBL-European
Bioinformatics Institute, Wellcome Trust Genome Campus, CB10
1SD Hinxton, Cambridge, United
Kingdom
- Department
of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Goran Vinterhalter
- Faculty
of Mathematics, University of Belgrade, Studentski trg 16, 11001 Belgrade, Serbia
| | - Veit Schwämmle
- Department
for Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
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