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QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments

  • Joris Van Houtven
    Joris Van Houtven
    VITO NV, Applied Bio & molecular Systems, Boeretang 200, Mol 2400, Belgium
    Universiteit Hasselt, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek 3590, Belgium
    Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen 2020, Belgium
  • Annelies Agten
    Annelies Agten
    Universiteit Hasselt, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek 3590, Belgium
  • Kurt Boonen
    Kurt Boonen
    VITO NV, Applied Bio & molecular Systems, Boeretang 200, Mol 2400, Belgium
    Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen 2020, Belgium
    More by Kurt Boonen
  • Geert Baggerman
    Geert Baggerman
    VITO NV, Applied Bio & molecular Systems, Boeretang 200, Mol 2400, Belgium
    Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen 2020, Belgium
  • Jef Hooyberghs
    Jef Hooyberghs
    VITO NV, Applied Bio & molecular Systems, Boeretang 200, Mol 2400, Belgium
    Universiteit Hasselt, Theoretical Physics, Agoralaan, Diepenbeek 3590, Belgium
  • Kris Laukens
    Kris Laukens
    Universiteit Antwerpen, Biomedical Informatics Research Center Antwerp (Biomina), Middelheimlaan 1, Antwerpen 2020, Belgium
    Universiteit Antwerpen, Advanced Database Research and Modelling (ADReM), Department of Mathematics & Computer Sciences, Middelheimlaan 1, Antwerpen 2020, Belgium
    More by Kris Laukens
  • , and 
  • Dirk Valkenborg*
    Dirk Valkenborg
    VITO NV, Applied Bio & molecular Systems, Boeretang 200, Mol 2400, Belgium
    Universiteit Hasselt, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek 3590, Belgium
    Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen 2020, Belgium
    *E-mail: [email protected]
Cite this: J. Proteome Res. 2019, 18, 5, 2221–2227
Publication Date (Web):April 3, 2019
https://doi.org/10.1021/acs.jproteome.9b00072
Copyright © 2019 American Chemical Society

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    Abstract

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    In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.9b00072.

    • Organs_full_Report.pdf: Example report file of the minimal and full expression analysis of the Organs data set.(PDF)

    • Figure S1. Inner join result when combining two data frames. Figure S2. Experimental design corresponding to the Organs data set. Table S1. Experimental design corresponding to the Failed data set. Figure S3. Comparison of DEA statistics for the Organs data set when treating repeated measurements as independent, and when averaging them. Notes on user experience. Notes on aggregation. Figure S4. Elution profile of a peptide. Notes on CONSTANd normalization. Figure S5. Comparison of s from the Organs data set when treating repeated measurements as independent and when averaging them. Figure S6. MA plots of the s from the Organs data set when treating repeated measurements as independent and when averaging them. Figure S7. Volcano plot of the Failed data set for condition b versus B. Figure S8. Detailed schematic of the QCQuan workflow.(PDF)

    • R_scripts.zip. R-code for generating PSMs (generate_PSMs.R) and R-code for SASQN workflow (SASQN_workflow.R).(ZIP)

    • Organs_input.zip. Example input data of the Organs data set: Organs_input.zip(ZIP)

    • Organs_full_output.zip. Example output data of the minimal and full expression analysis of the Organs data set: Organs_full_output.zip(ZIP)

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    Cited By

    This article is cited by 8 publications.

    1. Joris Van Houtven, Jef Hooyberghs, Kris Laukens, Dirk Valkenborg. CONSTANd: An Efficient Normalization Method for Relative Quantification in Small- and Large-Scale Omics Experiments in R BioConductor and Python. Journal of Proteome Research 2021, 20 (4) , 2151-2156. https://doi.org/10.1021/acs.jproteome.0c00977
    2. Preeti Kumari Chaudhary, Sachin Upadhayaya, Sanggu Kim, Soochong Kim. The Perspectives of Platelet Proteomics in Health and Disease. Biomedicines 2024, 12 (3) , 585. https://doi.org/10.3390/biomedicines12030585
    3. Bailey L. Bowser, Renã A.S. Robinson. Enhanced Multiplexing Technology for Proteomics. Annual Review of Analytical Chemistry 2023, 16 (1) , 379-400. https://doi.org/10.1146/annurev-anchem-091622-092353
    4. Junqiang Zhao, Jianqiang Chen, Xiuhui Tian, Lisheng Jiang, Qingkui Cui, Yanqing Sun, Ningning Wu, Ge Liu, Yuzhu Ding, Jing Wang, Yongchun Liu, Dianfeng Han, Yingjiang Xu. Amantadine Toxicity in Apostichopus japonicus Revealed by Proteomics. Toxics 2023, 11 (3) , 226. https://doi.org/10.3390/toxics11030226
    5. Ahu Cephe, Necla Koçhan, Esma Gamze Aksel, Funda İpekten, Serra İlayda Yerlitaş, Gözde Ertürk Zararsız, Gökmen Zararsız. Bioinformatics and Biostatistics in Precision Medicine. 2023, 189-235. https://doi.org/10.1007/978-981-99-1529-3_8
    6. Joris Van Houtven, Bart Cuypers, Pieter Meysman, Jef Hooyberghs, Kris Laukens, Dirk Valkenborg. Constrained Standardization of Count Data from Massive Parallel Sequencing. Journal of Molecular Biology 2021, 433 (11) , 166966. https://doi.org/10.1016/j.jmb.2021.166966
    7. Olga Shevchuk, Antonija Jurak Begonja, Stepan Gambaryan, Matthias Totzeck, Tienush Rassaf, Tobias B. Huber, Andreas Greinacher, Thomas Renne, Albert Sickmann. Proteomics: A Tool to Study Platelet Function. International Journal of Molecular Sciences 2021, 22 (9) , 4776. https://doi.org/10.3390/ijms22094776
    8. Matthew B. O’Rourke, Stephanie E. L. Town, Penelope V. Dalla, Fiona Bicknell, Naomi Koh Belic, Jake P. Violi, Joel R. Steele, Matthew P. Padula. What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows. Proteomes 2019, 7 (3) , 29. https://doi.org/10.3390/proteomes7030029

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