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Avoiding abundance bias in the functional annotation of posttranslationally modified proteins

Nature Methods volume 12, pages 1003–1004 (2015)Cite this article

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Identification of post-translational modifications (PTMs) by mass spectrometry is biased toward abundant proteins, skewing our understanding of PTMs in favor of readily detected proteins. We developed a method and web tool (https://agotool.sund.ku.dk) to account for this protein-abundance bias in Gene Ontology (GO)-term enrichment analyses of PTM data sets.

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Figure 1: Improved functional annotation of protein PTMs.

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Acknowledgements

We thank the members of the Department of Proteomics and the Department of Disease Systems Biology at the Center for Protein Research for helpful discussions. C.C. is supported by the Hallas Møller Investigator Fellowship from the Novo Nordisk Foundation. The Center for Protein Research is funded by a generous grant from the Novo Nordisk Foundation (NNF14CC0001).

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Author notes

  1. Christian Schölz and David Lyon: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Christian Schölz, Chunaram Choudhary & Brian T Weinert

  2. Department of Disease Systems Biology, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    David Lyon, Jan C Refsgaard & Lars Juhl Jensen

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  1. Christian Schölz
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  2. David Lyon
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  3. Jan C Refsgaard
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  4. Lars Juhl Jensen
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Corresponding authors

Correspondence to Chunaram Choudhary or Brian T Weinert.

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Integrated supplementary information

Supplementary Figure 1 Abundance distributions of post-translationally modified proteins.

The histograms show the distributions of protein abundances as determined using an intensity-based absolute quantification (iBAQ) method1. Experimentally detected proteins (proteins) and posttranslationally modified proteins (modified proteins) are shown. The distributions of the indicated posttranslationally modified proteins were compared to the experimentally detected proteins by Wilcoxon test (P-value). The number of analyzed proteins is indicated in parenthesis.

1 Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

Supplementary Figure 2 Overlap between different GO-term enrichment analyses of post-translationally modified proteins.

The Venn diagrams show the overlap between significantly (P < 0.01) enriched GO terms and UniProt keywords identified by comparing the indicated groups of modified proteins to the genome, observed proteome, and abundance-corrected (corrected) proteome. Note that some terms were found using the corrected proteome that were not found using the genome and/or observed proteome.

Supplementary Figure 3 The increased specificity of the abundance-corrected analysis is partly due to the smaller sample size of the abundance-corrected proteome compared with that of the observed proteome.

(a) The model illustrates the method used to account for differences in sample size. The number of term-associating and total proteins in the abundance-corrected (corrected) proteome were increased proportionally to match those in the observed proteome (observed), which we refer to as the adjusted-corrected proteome (the frequency of term association is identical in the corrected and adjusted-corrected proteomes, but the sample size is artificially increased in the adjusted-corrected proteome to mimic the statistical power of the observed proteome. This method was only applied here and is not a feature of the web tool because terms identified using the adjusted-corrected proteome are not necessarily significant since the sample size was artificially increased). This allowed us to determine whether terms identified using the observed proteome were absent when using the corrected proteome because the number of observations decreased, thereby reducing the statistical significance. We defined the total increase in specificity as the number of terms that were found using the observed proteome, but not the corrected proteome. The number of these terms that became significant again by using the adjusted-corrected proteome was attributed to a loss of significance due to the sample size. Thus, the fraction of increased specificity due to reduced sample size is equal to the number of terms regained by using the adjusted-corrected proteome divided by the total increase in specificity. (b) The Venn diagrams show the overlap between significantly enriched terms (P<0.01) using the observed, corrected, or adjusted-corrected proteomes to compare GO term associations with the indicated groups of modified proteins. The fraction of increased specificity attributed to sample size is show together with the numbers used to calculate this value. Note that increasing the sample size did not enable the detection of significantly enriched GO terms or KWs for succinylation in yeast.

Supplementary Figure 4 KWs describing central metabolism were removed because of abundance bias and not sample size.

Based on the analysis shown in Supplementary Fig. 3 we determined that decreased keyword association in the abundance-corrected (corrected) analysis could either be attributed to sample size (keyword is significantly enriched after artificially increasing the sample size in the adjusted-corrected proteome) or to abundance-bias (increasing the sample size has no effect on the significance of keyword enrichment). Here we show significantly (P < 0.01) enriched UniProt keywords for acetylated proteins which were removed in the corrected analysis due to abundance-bias or sample size. The Venn diagrams show significantly enriched keywords for acetylation in yeast and HeLa cells based on comparison to the observed proteome, corrected proteome, or adjusted-corrected proteome. The fold enrichment (Enrichment) and Fisher P value (P value) based on the observed proteome analysis are shown. The keyword “Glycolysis” was one of the most highly enriched keywords in the observed proteome which was attributed to abundance-bias.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Methods (PDF 2645 kb)

Supplementary Table 1

PTM mapping in yeast cells (XLSX 5975 kb)

Supplementary Table 2

PTM mapping in HeLa cells (XLSX 7602 kb)

Supplementary Data

GO term and UniProt Keyword enrichment analysis (ZIP 9697 kb)

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Schölz, C., Lyon, D., Refsgaard, J. et al. Avoiding abundance bias in the functional annotation of posttranslationally modified proteins. Nat Methods 12, 1003–1004 (2015). https://doi.org/10.1038/nmeth.3621

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