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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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.
The authors declare no competing financial interests.
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.
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.
Workflow representing the “identification pipeline” used to identify originally submitted spectra of incorrectly identified and unidentified clusters.
Flow chart summarizing all analyses steps performed during the analyses of the spectrum clustering results, as described in the main manuscript.
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.
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.
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).
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 Figures 1–9 and Supplementary Notes 1–8 (PDF 3504 kb)
List of processed human PRIDE Archive submissions as part of the test dataset (XLS 94 kb)
List of analysed phosphorylation studies submitted to PRIDE Archive. (XLS 123 kb)
List of identified phosphorylated peptides identified in the three examples presented in the manuscript. (XLS 79 kb)
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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