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Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

Nature Methods volume 13, pages 651–656 (2016)Cite this article

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Abstract

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average, 75% of spectra analyzed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large scale to shed light on these unidentified spectra. The Proteomics Identifications (PRIDE) Database Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in the PRIDE Archive, coming from hundreds of data sets, we were able to consistently characterize spectra into three distinct groups: (1) incorrectly identified, (2) correctly identified but below the set scoring threshold, and (3) truly unidentified. Using multiple complementary analysis approaches, we were able to identify ∼20% of the consistently unidentified spectra. The complete spectrum-clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

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Figure 1: Accuracy of the spectra-cluster algorithm compared with the MSCluster16 and MaRaCluster17 algorithms.
Figure 2: Overview of the results of the analysis to highlight commonly found incorrect peptide identifications in the PRIDE Archive.
Figure 3: Identified spectra from a diverse range of data sets, including spectra from experiments in other species, led to newly identified phosphorylated peptides in the Chromosome-Centric HPP data sets (PXD000529, PXD000533 and PXD000535).
Figure 4: Overview of the results of the analysis of clusters containing only unidentified spectra.
Figure 5: Delta masses observed for the 5,560 large human unidentified clusters whose consensus spectra were identified using an open modification search.

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Acknowledgements

This work was supported by the Vienna Science and Technology Fund (WWTF, grant LS11-045; grant was awarded to S.N. Wagner (Medical University of Vienna, Division of Immunology, Allergy and Infectious Diseases) and used to fund J.G.), the Wellcome Trust (grant WT101477MA to H.H. and J.A.V.), the BBSRC ('PROCESS' grant BB/K01997X/1 to H.H. and J.A.V., 'Quantitative Proteomics' grant BB/I00095X/1 to H.H.), the Deutsche Forschungsgemeinschaft (grant SFB685/B1 to O.K.), and the BMBF (grant 01ZX1301F to O.K.). We would like to acknowledge the attendees of the Midwinter Proteomics Bioinformatics Seminar 2015 at Semmering (Austria) and the Bioinformatics Hub at the HUPO conference 2015 at Vancouver (Canada), who provided valuable feedback on the data analysis. Finally, we want to acknowledge M. The and L. Käll for their support during the benchmarking of their MaRaCluster algorithm.

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Authors and Affiliations

  1. Division of Immunology, Department of Dermatology, Allergy and Infectious Diseases, Medical University of Vienna, Vienna, Austria

    Johannes Griss

  2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Johannes Griss, Yasset Perez-Riverol, Steve Lewis, José A Dianes, Noemi del-Toro, Henning Hermjakob, Rui Wang & Juan Antonio Vizcaíno

  3. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA

    David L Tabb

  4. Department of Computer Science, University of Tübingen, Tübingen, Germany

    Marc Rurik, Mathias Walzer & Oliver Kohlbacher

  5. Center for Bioinformatics, University of Tübingen, Tübingen, Germany

    Marc Rurik, Mathias Walzer & Oliver Kohlbacher

  6. Quantitative Biology Center, University of Tübingen, Tübingen, Germany

    Oliver Kohlbacher

  7. Max Planck Institute for Developmental Biology, Tübingen, Germany

    Oliver Kohlbacher

  8. National Center for Protein Sciences, Beijing, China

    Henning Hermjakob

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Contributions

J.G. developed the clustering algorithm, ran the experiments, and performed the data analysis. D.L.T. contributed to the development of the probabilistic scoring approach. Y.P.-R. contributed to the data analysis. J.G. and R.W. developed the Java APIs for the spectrum-clustering-analysis pipeline. S.L., R.W., and J.G. developed the Hadoop implementation. J.A.D., N.d.-T., Y.P.-R., and R.W. created the web interface and the API of the PRIDE Cluster resource. M.R., M.W., and O.K. performed the metabolite search. J.G., R.W., H.H., and J.A.V. supervised the project. J.G. and J.A.V. wrote the manuscript, with contributions from the rest of the authors.

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Correspondence to Johannes Griss or Juan Antonio Vizcaíno.

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Integrated supplementary information

Supplementary Figure 1 Relative Proportion of Unidentified Spectra in datasets submitted to PRIDE Archive

Box plots representing the relative proportion of unidentified spectra in the PRoteomics IDEntifications (PRIDE) Archive database. Overall, 75% of spectra in datasets submitted to PRIDE Archive are unidentified. Submitted datasets without identified spectra as well as datasets that only contained identified spectra were excluded from this calculation. Submissions to PRIDE (first box plot) represent those datasets submitted until mid-2012. Submissions to ProteomeXchange (second box plot) represent datasets submitted afterwards, once the ProteomeXchange data workflows were started. For the latter, only “complete” submissions are considered.

Supplementary Figure 2 Peptide Evidence per Proteomics Repository

Venn diagrams demonstrating that PRIDE Cluster based reliable peptide identifications provide additional MS/MS evidence for peptides not found in the other two other major MS-based data repositories (PeptideAtlas and GPMDB). Data are shown for (a) human, (b) mouse, (c) Arabidopsis thaliana, and (d) rat.

Supplementary Figure 3 PRIDE Cluster Provides MS/MS Evidence for Proteins without Experimental Evidence Annotated

PRIDE Cluster based validated peptide spectrum matches provide experimental evidence for the existence of a considerable number of proteins for which there is no experimental evidence at the protein level (PE=1), in UniProt. The present plot present the list of proteins that can be identified with at least 2 unique peptides with at least 9 amino acids, as was agreed in the latest guidelines of the Human Proteome Project. The categories represented are: only evidence on transcript level (PE=2), proteins inferred from homology (PE=3), and predicted proteins (PE=4) in the human UniProtKB/SwissProt (release 2016-03) database.

Supplementary Figure 4 Overall workflow representing the “identification pipeline”.

Workflow representing the “identification pipeline” used to identify originally submitted spectra of incorrectly identified and unidentified clusters.

Supplementary Figure 5 Overall workflow representing the PRIDE Cluster analysis process.

Flow chart summarizing all analyses steps performed during the analyses of the spectrum clustering results, as described in the main manuscript.

Supplementary Figure 6 Open modification search of unidentified mouse clusters.

Summary of results for the analysis of mouse clusters containing only unidentified spectra. The vast majority of delta masses observed in the open modification search were between -2 and +4 Da (top left panel). After adjusting the y-axis, several other delta masses were observed at a high frequency (top right panel). When limiting these delta masses to only masses that were observed at least for ten different clusters, the vast majority of delta masses could be mapped to known PTMs, as well as to one potential amino acid substitution (lower panel). For the complete list of the found delta masses see Supplementary Table 4.

Supplementary Figure 7 Open modification search of unidentified Arabidopsis thaliana clusters.

Summary of results for the analysis of Arabidopsis thaliana clusters containing only unidentified spectra. In contrast to the human and mouse data, consensus spectra of unidentified A. thaliana clusters were searched against the PRIDE Cluster spectral library for A. thaliana (version 2015-04). Again, most delta masses were found between -1 and 1 Da (top left panel). After adjusting the y-axis several other delta masses were observed (top right panel). When limiting these delta masses to only masses that were observed at least for five different clusters, three known PTMs could be identified even taking into account that the spectral library used in this search was derived from the same dataset (lower panel). For the complete list of the found delta masses see Supplementary Table 4.

Supplementary Figure 8 Identification of unidentified mouse clusters

Overview of the results of the analysis of clusters containing only unidentified spectra from mouse. (a) Venn diagram representing that 122 (15%) of the large unidentified mouse clusters were identified using SpectraST, X!Tandem and PepNovo. (b) In contrast to the results in human data, around 50% of identified proteins could not be classified as albumin, keratin, trypsin or haemoglobin. (c) Similarly to the human data, only trypsin peptides were commonly modified (e.g. dimethylated, the center line marks the median, edges the first and third quartile, whiskers extend to +/-1.58 times the inter-quartile ratio divided by the square root of the number of observations, single points denote measurements outside this range).

Supplementary Figure 9 Identification of unidentified Arabidopsis thaliana clusters

Overview of the results of the analysis of clusters containing only unidentified spectra from A. thaliana data. (a) Venn diagram representing that 50 (9%) of the large unidentified A. thaliana clusters were identified using SpectraST, X!Tandem and PepNovo. (b) In contrast to mouse and human data, no haemoglobin associated proteins were identified. Similarly to the identified proteins in the mouse dataset, most proteins could not be classified as albumin, keratin, or trypsin. All identified peptides corresponding to albumin were matches against bovine albumin and most likely experimental contaminants. (c) Similarly to the human and mouse data, trypsin peptides were commonly modified (e.g. dimethylated). Additionally, in this case the majority of albumin peptides were also modified (the center line marks the median, edges the first and third quartile, whiskers extend to +/-1.58 times the inter-quartile ratio divided by the square root of the number of observations, single points denote measurements outside this range).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Notes 1–8 (PDF 3504 kb)

Supplementary Table 1

List of processed human PRIDE Archive submissions as part of the test dataset (XLS 94 kb)

Supplementary Table 2

List of analysed phosphorylation studies submitted to PRIDE Archive. (XLS 123 kb)

Supplementary Table 3

List of identified phosphorylated peptides identified in the three examples presented in the manuscript. (XLS 79 kb)

Supplementary Table 4

List of commonly observed mass deltas when processing unidentified clusters using an open modification search. Results are given for human, mouse and Arabidopsis. (XLS 28 kb)

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Griss, J., Perez-Riverol, Y., Lewis, S. et al. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat Methods 13, 651–656 (2016). https://doi.org/10.1038/nmeth.3902

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