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MetaMass, a tool for meta-analysis of subcellular proteomics data

Nature Methods volume 13, pages 837–840 (2016)Cite this article

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Abstract

We report a tool for the analysis of subcellular proteomics data, called MetaMass, based on the use of standardized lists of subcellular markers. We analyzed data from 11 studies using MetaMass, mapping the subcellular location of 5,970 proteins. Our analysis revealed large variations in the performance of subcellular fractionation protocols as well as systematic biases in protein annotation databases. The Excel and R versions of MetaMass should enhance transparency and reproducibility in subcellular proteomics.

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Figure 1: Classification of a high-resolution data set with five sets of subcellular markers.
Figure 2: Meta-analysis of subcellular proteomics data from 11 studies.

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References

  1. Christoforou, A. et al. Nat. Commun. 7, 9992 (2016).

    Article  CAS  Google Scholar 

  2. Drissi, R., Dubois, M.L. & Boisvert, F.M. FEBS J. 280, 5626–5634 (2013).

    Article  CAS  Google Scholar 

  3. Larance, M. & Lamond, A.I. Nat. Rev. Mol. Cell Biol. 16, 269–280 (2015).

    Article  CAS  Google Scholar 

  4. Gatto, L. et al. Mol. Cell. Proteomics 13, 1937–1952 (2014).

    Article  CAS  Google Scholar 

  5. Rodríguez-Piñeiro, A.M. et al. J. Proteome Res. 11, 1879–1890 (2012).

    Article  Google Scholar 

  6. Andreyev, A.Y. et al. Mol. Cell. Proteomics 9, 388–402 (2010).

    Article  CAS  Google Scholar 

  7. Carvalho, A.S. et al. Mol. Cell. Proteomics 13, 3294–3307 (2014).

    Article  CAS  Google Scholar 

  8. Boisvert, F.M. et al. Mol. Cell. Proteomics 11, M111.011429 (2012).

    Article  Google Scholar 

  9. Pinto, G. et al. J. Proteome Res. 13, 1112–1127 (2014).

    Article  CAS  Google Scholar 

  10. Choi, S. et al. J. Proteome Res. 11, 4983–4991 (2012).

    Article  CAS  Google Scholar 

  11. Mulvey, C.M. et al. J. Proteome Res. 12, 1436–1453 (2013).

    Article  CAS  Google Scholar 

  12. Bileck, A., Kreutz, D., Muqaku, B., Slany, A. & Gerner, C. J. Proteome Res. 13, 5989–6000 (2014).

    Article  CAS  Google Scholar 

  13. Thakar, K., Karaca, S., Port, S.A., Urlaub, H. & Kehlenbach, R.H. Mol. Cell. Proteomics 12, 664–678 (2013).

    Article  CAS  Google Scholar 

  14. Larance, M. et al. Mol. Cell. Proteomics 11, M111.014407 (2012).

    Article  Google Scholar 

  15. Adam, S.A., Marr, R.S. & Gerace, L. J. Cell Biol. 111, 807–816 (1990).

    Article  CAS  Google Scholar 

  16. Ramsby, M. & Makowski, G. Cold Spring Harb. Protoc. 2011, prot5592 (2011).

    Article  Google Scholar 

  17. de Hoon, M.J., Imoto, S., Nolan, J. & Miyano, S. Bioinformatics 20, 1453–1454 (2004).

    Article  CAS  Google Scholar 

  18. Davis, J. & Goadrich, M. in Proc. 23rd International Conference on Machine Learning 233–240 (ACM, 2006).

  19. Fawcett, T. Pattern Recognit. Lett. 27, 861–874 (2006).

    Article  Google Scholar 

  20. de Souto, M.C., Jaskowiak, P.A. & Costa, I.G. BMC Bioinformatics 16, 64 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank J. Olweus, K.J. Malmberg and J. Trimmer for helpful discussions and critical reading. Grant support was provided by the K.G. Jebsen Foundation (F.L.-J.), Helse Sør-Øst (2013128, F.L.-J.), Norwegian Research Council (F.L.-J.), and Ministry of Education, Youth and Sports, Czech Republic (NPU I project no. LO1604, T.K. and J.S.).

Author information

Author notes

  1. Quentin Lecrevisse and Jan Stuchly: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Immunology, Oslo University Hospital, Oslo, Norway

    Fridtjof Lund-Johansen, Daniel de la Rosa Carrillo, Adi Mehta, Krzysztof Sikorski, Marit Inngjerdingen & Gustavo Antonio de Souza

  2. K.G. Jebsen Center for Cancer Immunotherapy, University of Oslo, Oslo, Norway

    Fridtjof Lund-Johansen & Krzysztof Sikorski

  3. Department of Dermatology, Oslo University Hospital, Oslo, Norway

    Daniel de la Rosa Carrillo

  4. K.G. Jebsen Inflammation Research Center, University of Oslo, Oslo, Norway

    Adi Mehta

  5. Department of Pediatric Hematology and Oncology, Childhood Leukemia Investigation Prague (CLIP), 2nd Faculty of Medicine, Charles University Prague and University Hospital Motol, Prague, Czech Republic

    Tomas Kalina & Jan Stuchly

  6. Department of Biostatistics, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway

    Kjetil Røysland

  7. B Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

    Andrew R M Bradbury

  8. Cancer Research Centre (IBMCC, CSIC-USAL), University of Salamanca, Salamanca, Spain

    Quentin Lecrevisse

  9. Institute of Biomedical Research of Salamanca (IBSAL), University of Salamanca, Salamanca, Spain

    Quentin Lecrevisse

  10. NUCLEUS, University of Salamanca, Salamanca, Spain

    Quentin Lecrevisse

  11. Department of Medicine, University of Salamanca, Salamanca, Spain

    Quentin Lecrevisse

Authors
  1. Fridtjof Lund-Johansen
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  2. Daniel de la Rosa Carrillo
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  5. Marit Inngjerdingen
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  6. Tomas Kalina
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Contributions

F.L.-J., data analysis, design of software and manuscript writing; D.d.l.R.C., data analysis, statistics and software testing; A.M., data analysis and literature review; K.S., data analysis, literature review and figure preparation; M.I., data analysis, Excel algorithms and literature review; T.K., supervision of R programming; G.A.d.S., retrieval and analysis of raw MS data; A.R.M.B., manuscript writing; K.R., statistical analysis; Q.L., Excel VBA programming; J.S., R programming, user manual for MetaMass R, data analysis and statistical analysis.

Corresponding author

Correspondence to Fridtjof Lund-Johansen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Overlap of subcellular markers in annotation databases.

The Venn diagram shows overlap between databases in the number of proteins are listed with a single subcellular location. Lists of proteins were extracted from the annotation lookup table in Supplementary Table 2 and processed with a web-based tool to generate Venn-diagrams (http://bioinfogp.cnb.csic.es/tools/venny/,).

Supplementary Figure 2 Validation of MetaMass

The pie chart shows the similarity in the classification of dataset 1 to that reported in study 11. The dataset was processed with MetaMass using the same SVM markers used in study 1. The bar and whisker plot shows F1 scores (reported in study 1) for proteins that were mapped by both algorithms (green) and those that were mapped in study 1 but not with MetaMass (orange). The average F1 score for proteins that were mapped in both studies was 0.89 as compared to 0.75 for proteins that were mapped in study 1 only (p=3e-97, T-test two-tailed, unequal variance).

Supplementary Figure 3 HPA annotations have high numbers of nuclear markers

The pie charts show the frequency of markers for indicated subcellular locations in indicated marker set. The data were extracted from Supplementary Table 2. See also legend to Fig. 1.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Protocol (PDF 2867 kb)

Supplementary Tables 1–14

Supplementary Tables 1: Overview of methodology described in 92 articles on subcellular proteomics. Supplementary Tables 2: All Uniprot entries for the reviewed human proteome with full-text and processed annotations on their subcellular localization from Uniprot, GO and the Human Protein Atlas. The table also contains all marker panels used to generate Fig. 1 and 2. Supplementary Tables 3: All datasets included in the study. Protein identifiers and normalized MS signal values measured in 11 independent studies on subcellular proteomics. Supplementary Tables 4: Statistics output from MetaMass Excel for Fig. 1a. Supplementary Tables 5: Mapping result from MetaMass Excel for Fig. 1a. Supplementary Tables 6: Statistics output from MetaMass Excel for Fig. 2a. Supplementary Tables 7: Mapping result from MetaMass Excel for Fig. 2a. Supplementary Tables 8: Clustering results (cluster 3.0) used as input in MetaMass Excel to perform the analysis shown in Fig. 1 and 2. Supplementary Tables 9: Import/protein identifier conversion table for formatting of user-generated MS data for analysis with MetaMass (R and Excel versions, see user MetaMass user manual p7). Supplementary Tables 10: List of links and commands for installation and use of MetaMass R. The links open webpages for installation or R and R-studio. The commands can be copied directly into R-studio for installation and use of MetaMass R. Supplementary Tables 11: Template for user-generated annotation table for use in MetaMass R. (see MetaMass user manual p25). Supplementary Tables 12: Source data used to generate Fig. 1. Protein identifiers were converted from mouse to human orthologues, and MS signal values were normalized. The data can be copied directly into MetaMass Excel together with the corresponding clustering result in supplementary Table 8 to reconstruct the analysis shown in Fig. 1. Supplementary Tables 13: Source data used to generate Fig. 2a,b. The data correspond to those in supplementary Table 3, but are limited to proteins that occur two or more of datasets 2-11. The data can be copied directly into MetaMass Excel together with the corresponding clustering result in supplementary Table 8 to reconstruct the analysis shown in Fig. 2a,b. Supplementary Tables 14: Data table output from MetaMass R when the tool was used with the same markers and data used to generate the results in Fig. 2a. see MetaMass user manual p22 (supplementary Protocol). (XLSX 15724 kb)

Supplementary Software

MetaMass Excel Macro-enabled Excel workbook for automated analysis of subcellular proteomics data. (XLSM 4189 kb)

Source data

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Lund-Johansen, F., de la Rosa Carrillo, D., Mehta, A. et al. MetaMass, a tool for meta-analysis of subcellular proteomics data. Nat Methods 13, 837–840 (2016). https://doi.org/10.1038/nmeth.3967

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