Mining the hidden proteome using hundreds of public proteomics datasets
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Keynote talk during the Belgian Proteomics Society meeting in Ghent (November 2016)
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Mining the hidden proteome using hundreds of public proteomics datasets
- 1. Mining the hidden proteome using hundreds of public proteomics datasets Dr. Juan Antonio Vizcaíno EMBL-European Bioinformatics Institute Hinxton, Cambridge, UK
- 2. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Overview • PRIDE Archive and ProteomeXchange • Reuse of public proteomics data • PRIDE Cluster iteration 1 • PRIDE Cluster iteration 2 -> Mining the hidden proteome
- 3. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 What is a proteomics publication in 2016? • Proteomics studies generate potentially large amounts of data and results. • Ideally, a proteomics publication needs to: • Summarize the results of the study • Provide supporting information for reliability of any results reported • Information in a publication: • Manuscript • Supplementary material • Associated data submitted to a public repository
- 4. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 • PRIDE stores mass spectrometry (MS)-based proteomics data: • Peptide and protein expression data (identification and quantification) • Post-translational modifications • Mass spectra (raw data and peak lists) • Technical and biological metadata • Any other related information • Full support for tandem MS approaches • Any type of data can be stored. PRIDE (PRoteomics IDEntifications) Archive http://www.ebi.ac.uk/pride/archive Martens et al., Proteomics, 2005 Vizcaíno et al., NAR, 2016
- 5. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 ProteomeXchange: A Global, distributed proteomics database PASSEL (SRM data) PRIDE (MS/MS data) MassIVE (MS/MS data) Raw ID/Q Meta jPOST (MS/MS data) Mandatory raw data deposition since July 2015 • Goal: Development of a framework to allow standard data submission and dissemination pipelines between the main existing proteomics repositories. http://www.proteomexchange.org New in 2016 Vizcaíno et al., Nat Biotechnol, 2014 Deutsch et al., NAR, 2017, in press
- 6. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 ProteomeCentral Metadata / Manuscript Raw Data Results Journals Peptide Atlas Receiving repositories PRIDE Researcher’s results Raw data Metadata PASSEL Research groups Reanalysis of datasets MassIVE jPOST MS/MS data (as complete submissions) Any other workflow (mainly partial submissions) DATASETS SRM data Reprocessed results MassIVE ProteomeXchange data workflow Vizcaíno et al., Nat Biotechnol, 2014 Deutsch et al., NAR, 2017, in press
- 7. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 ProteomeCentral: Centralised portal for all PX datasets http://proteomecentral.proteomexchange.org/cgi/GetDataset
- 8. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 ProteomeCentral Metadata / Manuscript Raw Data Results Journals Peptide Atlas Receiving repositories PRIDE Researcher’s results Raw data Metadata PASSEL Research groups Reanalysis of datasets MassIVE jPOST MS/MS data (as complete submissions) Any other workflow (mainly partial submissions) DATASETS SRM data Reprocessed results MassIVE ProteomeXchange data workflow
- 9. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 ProteomeCentral Metadata / Manuscript Raw Data Results Journals UniProt/ neXtProtPeptide Atlas Other DBs Receiving repositories PRIDE GPMDBResearcher’s results Raw data Metadata PASSEL proteomicsDB Research groups Reanalysis of datasets MassIVE jPOST MS/MS data (as complete submissions) Any other workflow (mainly partial submissions) DATASETS OmicsDI Integration with other omics datasets SRM data Reprocessed results MassIVE ProteomeXchange data workflow
- 10. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Archive – over 5,000 datasets from over 51 countries and 2,000 groups • USA – 814 datasets • Germany – 528 • UK – 338 • China – 328 • France – 222 • Netherlands – 175 • Canada - 137 Data volume: • Total: ~275 TB • Number of all files: ~560,000 • PXD000320-324: ~ 4 TB • PXD002319-26 ~2.4 TB • PXD001471 ~1.6 TB • 1,973 datasets i.e. 52% of all are publicly accessible • ~90% of all ProteomeXchange datasets YearSubmissions All submissions Complete PRIDE Archive growth In the last 12 months: ~165 submitted datasets per month Top Species studied by at least 100 datasets: 2,010 Homo sapiens 604 Mus musculus 191 Saccharomyces cerevisiae 140 Arabidopsis thaliana 127 Rattus norvegicus >900 reported taxa in total
- 11. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Overview • PRIDE Archive and ProteomeXchange • Reuse of public proteomics data • PRIDE Cluster iteration 1 • PRIDE Cluster iteration 2
- 12. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Datasets are being reused more and more…. Vaudel et al., Proteomics, 2016 Data download volume for PRIDE Archive in 2015: 198 TB 0 50 100 150 200 250 2013 2014 2015 2016 Downloads in TBs
- 13. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Data sharing in Proteomics Vaudel et al., Proteomics, 2016
- 14. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Data sharing in Proteomics Vaudel et al., Proteomics, 2016
- 15. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Draft Human proteome papers published in 2014 Wilhelm et al., Nature, 2014 •Around 60% of the data used for the analysis comes from previous experiments, most of them stored in proteomics repositories such as PRIDE/ProteomeXchange, PASSEL or MassIVE. •They complement that data with “exotic” tissues.
- 16. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Data sharing in Proteomics Vaudel et al., Proteomics, 2016
- 17. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Examples of repurposing datasets: proteogenomics Data in public resources can be used for genome annotation purposes
- 18. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Public datasets from different omics: OmicsDI http://www.ebi.ac.uk/Tools/omicsdi/ • Aims to integrate of ‘omics’ datasets (proteomics, transcriptomics, metabolomics and genomics at present). PRIDE MassIVE jPOST PASSEL GPMDB ArrayExpress Expression Atlas MetaboLights Metabolomics Workbench GNPS EGA Perez-Riverol et al., Nat Biotechnol, in press
- 19. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 OmicsDI: Portal for omics datasets
- 20. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 OmicsDI: Portal for omics datasets
- 21. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Overview • PRIDE Archive and ProteomeXchange • Reuse of public proteomics data • PRIDE Cluster iteration 1 • PRIDE Cluster iteration 2 -> Mining the hidden proteome
- 22. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Data sharing in Proteomics Vaudel et al., Proteomics, 2016
- 23. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster - Concept NMMAACDPR NMMAACDPR PPECPDFDPPR NMMAACDPR NMMAACDPR NMMAACDPR Consensus spectrum PPECPDFDPPR Threshold: At least 3 spectra in a cluster and ratio >70%. Originally submitted identified spectra Spectrum clustering Provide a QC-filtered peptide-centric view of PRIDE Archive
- 24. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 One perfect cluster in PRIDE Cluster web - 880 PSMs give the same peptide ID - 4 species - 28 datasets - Same instruments http://www.ebi.ac.uk/pride/cluster/
- 25. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster: Implementation • Clustered all public, identified spectra in PRIDE • EBI compute farm, LSF • 20.7 M identified spectra • 610 CPU days, two calendar weeks • Validation, calibration • Feedback into PRIDE datasets • EBI farm, LSF • Griss et al., Nat. Methods, 2013
- 26. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Overview • PRIDE Archive and ProteomeXchange • Reuse of public proteomics data • PRIDE Cluster iteration 1 • PRIDE Cluster iteration 2 -> Mining the hidden proteome
- 27. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster Iteration 2: Why? • PRIDE Archive has experienced a huge increase in data since 2013. • We wanted to develop an algorithm that could also work with unidentified spectra. Year Submissions All submissions Complete PRIDE Archive growth
- 28. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster: Second Implementation • Clustered all public, identified spectra in PRIDE • EBI compute farm, LSF • 20.7 M identified spectra • 610 CPU days, two calendar weeks • Validation, calibration • Feedback into PRIDE datasets • EBI farm, LSF • Griss et al., Nat. Methods, 2013 • Clustered all public spectra in PRIDE by April 2015. • Apache Hadoop. • Starting with 256 M spectra. • 190 M unidentified spectra (they were filtered to 111 M for spectra that are likely to represent a peptide). • 66 M identified spectra • Result: 28 M clusters • 5 calendar days on 30 node Hadoop cluster, 340 CPU cores • Griss et al., Nat. Methods, 2016
- 29. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster - Concept
- 30. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Examples: one perfect cluster (2)
- 31. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Highlights of the output of the analysis • 1. Inconsistent spectrum clusters • 2. Clusters including identified and unidentified spectra. • 3. Clusters just containing unidentified spectra.
- 32. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 1. Re-analysis of inconsistent clusters NMMAACDPR NMMAACDPR IGGIGTVPVGR NMMAACDPR PPECPDFDPPR VFDEFKPLVEEPQNLIK NMMAACDPR IGGIGTVPVGR Consensus spectrum PPECPDFDPPR VFDEFKPLVEEP QNLIK Originally submitted identified spectra Spectrum clustering No sequence has a proportion in the cluster >50%
- 33. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 1. Re-analysis of inconsistent clusters • Re-analysed 3,997 large (>100 spectra), inconsistent clusters with PepNovo, SpectraST, X!Tandem. • 453 clusters (11%) were identified as peptides originated from keratins, trypsin, albumin, and hemoglobin. • In this case, it is likely that a contaminants DB was not used in the search
- 34. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Validation
- 35. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016
- 36. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016
- 37. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016
- 38. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 2. Inferring identifications for originally unidentified spectra Not identified PPECPDFDPPR PPECPDFDPPR PPECPDFDPPR Not identified Not identified Consensus spectrum PPECPDFDPPR Not identified Originally submitted spectra Spectrum clustering Identifications are inferred
- 39. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 2. Inferring identifications for originally unidentified spectra 39 • 9.1 M unidentified spectra were contained in clusters with a reliable identification. • These are candidate new identifications (that need to be confirmed), often missed due to search engine settings • Example: 49,263 reliable clusters (containing 560,000 identified and 130,000 unidentified spectra) contained phosphorylated peptides, many of them from non-enriched studies.
- 40. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 3. Consistently unidentified clusters Not identified Not identified Not identified Not identified Consensus spectrum Not identified Not identified Originally submitted spectra Spectrum clustering Method to target commonly found unidentified spectra ??
- 41. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster Sequence-based search engines Spectrum clustering Incorrectly or unidentified spectra
- 42. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 3. Consistently unidentified clusters • 19 M clusters contain only unidentified spectra. • 41,155 of these spectra have more than 100 spectra (=12 M spectra, 5%). • Most of them are likely to be derived from peptides. • They could correspond to PTMs or variant peptides. • With various methods, we found likely identifications for about 20%. • Vast amount of data mining remains to be done.
- 43. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 3. Consistently unidentified clusters
- 44. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 3. Consistently unidentified clusters
- 45. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 PRIDE Cluster as a Public Data Mining Resource 45 • http://www.ebi.ac.uk/pride/cluster • Spectral libraries for 16 species. • All clustering results, as well as specific subsets of interest available. • Source code (open source) and Java API
- 46. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Consistently unidentified clusters • We provide the results split per species in MGF and mzML format. • Very interested in getting people trying to work in those. • Available for several species (Largest clusters at present).
- 47. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Summary • PRIDE is now receiving ~8 datasets per working day. • A lot of possibilities open for reuse of this data. • New OmicsDI resource. • It is possible to detect spectra that are consistently unidentified across hundreds of datasets (maybe peptide variants, or peptides with PTMs not initially considered).
- 48. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Aknowledgements: People Attila Csordas Tobias Ternent Gerhard Mayer (de.NBI) Johannes Griss Yasset Perez-Riverol Manuel Bernal-Llinares Andrew Jarnuczak Enrique Perez Former team members, especially Rui Wang, Florian Reisinger, Noemi del Toro, Jose A. Dianes & Henning Hermjakob Acknowledgements: The PRIDE Team All data submitters !!! @pride_ebi @proteomexchange
- 49. Juan A. Vizcaíno juan@ebi.ac.uk BePA Conference Ghent, 17 November 2016 Questions? http://www.slideshare.net/JuanAntonioVizcaino