Reuse of public proteomics data
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The document discusses the PRIDE archive which stores mass spectrometry data and the goals of the ProteomeXchange consortium to enhance data submission and sharing among proteomics repositories. It highlights the importance of data reuse, reprocessing, and repurposing for generating new insights in research, as well as the increasing volume of datasets available in the PRIDE archive. Additionally, it mentions recent advancements in human proteome studies and the challenges faced in validating controversial findings.
![Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2015
Hinxton, 10 December 2015
• Quality assessment method. Takes advantage of the wealth of data in PRIDE.
• Assumption: The same peptide will generate similar MS/MS spectra
across many experiments:
• Cluster all identified spectra (20.7 M) in PRIDE (modified version of the MS-Cluster
algorithm [1]). API available (http://pride-spectra-clustering.googlecode.com).
• Those clusters which contain only/mainly one peptide are considered reliable.
• Thresholds: At least 10 spectra in a cluster and ratio >70%.
NMMAACDPR
NMMAACDPR
PPECPDFDPPR
NMMAACDPR
Consensus
PPECPDFDPPR
PRIDE Cluster
Griss et al., Nat Methods, 20131. Frank et al. Nat Methods 8, 587-591 (2011)
PRIDE Cluster](https://image.slidesharecdn.com/05reuseofdata-151217115412/85/Reuse-of-public-proteomics-data-20-320.jpg)
Reuse of public proteomics data
- 1. Exploring the potential of public proteomics data Dr. Juan Antonio Vizcaíno PRIDE Group Coordinator Proteomics Services Team EMBL-EBI Hinxton, Cambridge, UK
- 2. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 PRIDE (PRoteomics IDEntifications) Archive http://www.ebi.ac.uk/pride • PRIDE Archive 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 Martens et al., Proteomics, 2005 Vizcaíno et al., NAR, 2016, in press
- 3. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 ProteomeXchange Consortium • Goal: Development of a framework to allow standard data submission and dissemination pipelines between the main existing proteomics repositories. • Includes PeptideAtlas (ISB, Seattle), PRIDE (Cambridge, UK) and (very recently) MassIVE (UCSD, San Diego). • Common identifier space (PXD identifiers) • Two supported data workflows: MS/MS and SRM. • Main objective: Make life easier for researchers http://www.proteomexchange.org
- 4. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 PRIDE Archive submitted datasets up until 1st November, 2015 • 1,259 submitted datasets to PRIDE Archive by November 1st • 923 were submitted datasets in 2014 • In the last 6 months, 155 submitted datasets per month • Size: ~ 160 TB
- 5. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Review paper in press http://www.ncbi.nlm.nih.gov/pubmed/26449181 http://onlinelibrary.wiley.com/doi/10.1002/pmic.201500295/epdf Vaudel et al., 2016, Proteomics, in press
- 6. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics
- 7. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics
- 8. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics • Data as they are. • Protein knowledge bases: UniProt, neXtProt. • Contributing to the Protein Evidence Code.
- 9. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Protein Evidence codes in UniProt/neXtProt http://www.uniprot.org/help/protein_existence
- 10. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Use of MS data in UniProt
- 11. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Use of MS data in neXtProt
- 12. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics
- 13. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Reuse • Information is not only extracted, but reused in new experiments with the potential of generating new knowledge. • Transitions used in SRM approaches. • Meta-analysis approaches. • Spectral libraries.
- 14. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 SRMAtlas http://www.srmatlas.org/
- 15. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 MRMaid Workflow TransitionsTransitions Transition Database API Web Interface Expor t as TraML Expor t as CSV Other Tools Spectra & IDs Spectra & IDs Peptides, Spectra & IDs Transition s TransitionsTransitionsValidated Transitions Export as Vendor Specific File Transition Builder Pipeline • PRIDE database • XML files via FTP • PRIDE API Slide from C. Bessant Mead et al., MCP, 2009 http://138.250.31.29/mrmaid/
- 16. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 PeptidePicker http://mrmpeptidepicker.proteincentre.com/
- 17. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Meta-analysis approaches • Putting data coming from a lot of experiments together, to extract new knowledge. Examples: • Study the cleavage mechanism and performance of trypsin. • Fragmentation patterns. • Retention time prediction. • Which is the most suitable reference DB for long-term proteomics data storage? • Data integration of experiments done at different time points.
- 18. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Spectral searching • Concept: To compare experimental spectra to other experimental spectra. • There are many spectral libraries publicly available (for instance, from NIST, PeptideAtlas and PRIDE) • Custom ‘search engines’ have been developed: • SpectraST (TPP) • X!Hunter (GPM) • It has been claimed that the searches have more sensitivity that with sequence database approaches
- 19. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Spectral searching (2) http://peptide.nist.gov/
- 20. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 • Quality assessment method. Takes advantage of the wealth of data in PRIDE. • Assumption: The same peptide will generate similar MS/MS spectra across many experiments: • Cluster all identified spectra (20.7 M) in PRIDE (modified version of the MS-Cluster algorithm [1]). API available (http://pride-spectra-clustering.googlecode.com). • Those clusters which contain only/mainly one peptide are considered reliable. • Thresholds: At least 10 spectra in a cluster and ratio >70%. NMMAACDPR NMMAACDPR PPECPDFDPPR NMMAACDPR Consensus PPECPDFDPPR PRIDE Cluster Griss et al., Nat Methods, 20131. Frank et al. Nat Methods 8, 587-591 (2011) PRIDE Cluster
- 21. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics
- 22. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Reprocess • Data are reprocessed with the intention of obtaining new knowledge or to provide an updated view on the results. • It mainly serves the same purpose of the original experiment. • For instance, a shot-gun dataset can be reprocessed with a different algorithm or an updated sequence database.
- 23. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Reprocessing repositories • These resources collect MS raw data and reprocess it using one given analysis pipeline, and an up-to date protein sequence database. • Main resources: GPMDB and PeptideAtlas (ISB, Seattle).
- 24. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 PeptideAtlas builds Examples of builds: - Human - Human plasma - Human urine - Drosophila - Mouse - Mouse plasma - Cow - Yeast …
- 25. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data reprocessing in GPMDB
- 26. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Draft Human proteome papers published in 2014 Wilhelm et al., Nature, 2014 Kim et al., Nature, 2014 •Two independent groups claimed to have produced the first complete draft of the human proteome by MS. • Some of their findings are controversial and need further validation… but generated a lot of discussion and put proteomics in the spotlight. •They used many different tissues. Nature cover 29 May 2014
- 27. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 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.
- 28. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Reprocessing for the validation of controversial data • Analysis of Tyrannosaurus rex fossils: controversial presence of collagen (is it a contamination of the sample? Did the sample contain any T. rex proteins at all?) Asara et al. (2007) Science 316: 280-5. Asara et al. (2007) Science 316: 1324-5. Bern et al. (2009) JPR 9: 4328-32 PRIDE assay accession 8633
- 29. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Info from R. Chalkley Bromenshenk et al. (2011) PLOS One 5: e13181 Reprocessing for the validation of controversial data (2)
- 30. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Experimental Protocol 1. Collected samples from healthy, collapsing and collapsed bee colonies. 2. Homogenised bees. 3. Digested with Trypsin 4. Analyzed by LC-MSMS on LTQ 5. Searched using Sequest 6. Filtered Results using Peptide and Protein Prophet 7. Performed further analysis to determine species statistically more commonly found in collapsing/collapsed colony samples Info from R. Chalkley Bromenshenk et al. (2011) PLOS One 5: e13181 Reprocessing for the validation of controversial data (3)
- 31. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 • Big pitfall: Search database was only composed by viral proteins. Not bee proteins at all!! • After researching the data, there is no evidence for viral peptides/proteins in any of their data: honey bee, fruit fly, wasp, moth, human keratin, bacteria that like sugary environments, … • “We believe that there is currently insufficient evidence to conclude that bees are a natural host for IIV-6, let alone that the virus is linked to CCD”. Info from R. Chalkley Knudsen & Chalkley (2011) PLOS One 6: e20873 Foster (2011), MCP 10: M110.006387 Reprocessing for the validation of controversial data (4)
- 32. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Draft Human proteome papers published in 2014 Wilhelm et al., Nature, 2014 Kim et al., Nature, 2014 •Two independent groups claimed to have produced the first complete draft of the human proteome by MS. • Some of their findings are controversial and need further validation… but generated a lot of discussion and put proteomics in the spotlight. •They used many different tissues. Nature cover 29 May 2014
- 33. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Reprocessing for the validation of controversial data Datasets PXD000561 and PXD000865 in PRIDE Archive
- 34. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics
- 35. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Repurposing • Data are considered in light of a question or a context that is different from the original study. • Proteogenomics studies • Discovery of novel PTMs.
- 36. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 One example of repurpossing for genome annotation Brosch et al. (2011) Genome Res 21:756-767 • In this particular paper: • 53 genes alternatively transcribed • 10 new protein coding genes • Pipeline to integrate gene annotations in the mouse genome.
- 37. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Examples of repurposing in proteogenomics
- 38. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Repurposing: new PTMs found • Individual authors can reprocess raw data with new hypotheses in mind (not taken into account by the original authors). • Recent examples (using phosphoproteomics data sets): • O-GlcNAc-6-phosphate1 • Phosphoglyceryl2 • ADP-ribosylation3 1Hahne & Kuster, Mol Cell Proteomics (2012) 11 10 1063-9 2Moellering & Cravatt, Science (2013) 341 549-553 3Matic et al., Nat Methods (2012) 9 771-2
- 39. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Data sharing in Proteomics
- 40. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Vaudel M, Barsnes H, Berven FS, Sickmann A, Martens L: Proteomics 2011;11(5):996-9. https://github.com/compomics/searchgui https://github.com/compomics/peptide-shaker Vaudel M, Burkhart J, Zahedi RP, Berven FS, Sickmann A, Martens L, Barsnes H: Nature Biotechnology 2015; 33(1):22-4. CompOmics Open Source Analysis Pipeline
- 41. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Find the desired PRIDE project … … and start re-analyzing the data! … inspect the project details …. Reshake PRIDE data!
- 42. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Acknowledgements http://www.ncbi.nlm.nih.gov/pubmed/26449181 http://onlinelibrary.wiley.com/doi/10.1002/pmic.201500295/epdf Vaudel et al., 2016, Proteomics, in press
- 43. Juan A. Vizcaíno juan@ebi.ac.uk WT Proteomics Bioinformatics Course 2015 Hinxton, 10 December 2015 Questions?