Dr. Juan Antonio Vizcaíno presented on the reuse of public proteomics data. The submission of proteomics datasets to repositories like PRIDE has increased dramatically in recent years. Downloads and reuse of data from PRIDE has also grown significantly, reaching 295 terabytes in 2017. Common ways researchers reuse public proteomics data include verifying published results, building spectral libraries, finding interesting datasets to reanalyze for new discoveries, and benchmarking new algorithms. Data sharing allows information to be extracted and reused in new experiments, advancing protein knowledge in areas like UniProt and neXtProt databases.

1. Reuse of public proteomics data
Dr. Juan Antonio Vizcaíno
EMBL-EBI
Hinxton, Cambridge, UK

2. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
PRIDE data submissions and data growth
May 2018 (320 datasets) was again a
record month in terms of datasets
submitted
Datasets submitted per month
> 2,400 datasets submitted in 2017
Datasets submitted per year
PRIDE contains >85% of all ProteomeXchange datasets
Dataset PXD010000 reached on June 1st

3. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Public Data Reuse in Proteomics
Vaudel et al., Proteomics, 2016

4. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data re-use in proteomics keeps increasing
Data download volume for PRIDE in 2017: 295 TB
0
50
100
150
200
250
300
350
2013 2014 2015 2016 2017
Downloads in TBs

5. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
What do researchers think about the main uses of
public proteomics data?
• Verify published results
• Build Spectral libraries
• Find interesting datasets for reanalysis:
• Find new splice isoforms
• Discover new PTMs
Source: MassIVE workshop, ASMS 2017, Nuno Bandeira et al.

6. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data sharing in Proteomics
Vaudel et al., Proteomics, 2016

7. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data sharing in Proteomics

8. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data sharing in Proteomics
• Data directly 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 2018
Hinxton, 19 July 2018
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 2018
Hinxton, 19 July 2018
Use of MS data in UniProt

11. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data sharing in Proteomics

12. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Reuse
• Information is not only extracted, but reused in new
experiments with the potential of generating new
knowledge.
• Transitions used in SRM approaches.
• Spectral libraries.

13. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
SRMAtlas
http://www.srmatlas.org/

14. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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)
• Bibliospec
• It has been claimed that the searches have more
sensitivity that with sequence database approaches

15. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Spectral searching (2)
http://peptide.nist.gov/

16. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
PRIDE Cluster as a Public Data Mining Resource
16
• 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

17. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Benchmarking
• Many datasets are reused to benchmark new algorithms
and/or tools. Comparison with previous tools.
• Usually raw data is used as the base (new analysis), but
not always.
• Very common reuse case -> A lot of examples.

18. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data sharing in Proteomics

19. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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.

20. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
PeptideAtlas and GPMDB

21. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
proteomicsDB -> Draft Human proteome paper
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.

22. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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 dataset PRD000074

23. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Info from R. Chalkley
Bromenshenk et al. (2011) PLOS One 5: e13181
Reprocessing for the validation of controversial data (2)

24. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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)

25. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
• 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)

26. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Reprocessing for the validation of controversial data
Datasets PXD000561 and PXD000865 in PRIDE Archive

27. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Various reanalysis of these datasets have been performed…
Reanalysis of Pandey dataset (Nature, 2014) made by J. Choudhary’s group at
Sanger Institute
Wright et al., Nat Commun, 2016Dataset PXD000561
http://www.ebi.ac.uk/gxa

28. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Meta-analysis approaches
• Putting data coming from a lot of experiments
together, to extract new knowledge. Examples:
• Peptide fragmentation patterns.
• Retention time prediction.
• Data integration of experiments done at different time
points.
• Two different approaches:
• Data as submitted
• Reanalysis all the data together

29. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Meta-analysis recent examples
Lund-Johanssen et al., Nat Methods, 2016 Drew et al., Mol Systems Biol, 2017

30. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Data sharing in Proteomics

31. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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.

32. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Examples of repurposing datasets: proteogenomics
Data in public resources can be used for genome annotation purposes ->
Discovery of short ORFs, translated lncRNAs, etc

33. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Examples of repurposing datasets: proteogenomics
Also some studies have been performed in model organisms: mouse, rat,
Drosophila, and other microorganisms (Mycobacterium tuberculosis,
Helicobacter pylori, rice,…)

34. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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

35. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Do you want to know a bit more…?
http://www.slideshare.net/JuanAntonioVizcaino
Martens & Vizcaíno, Trends Bioch Sci, 2017 Vaudel et al., Proteomics, 2016

36. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
PRIDE data
dissemination

37. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
EXERCISE

38. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
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

39. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018
Find the desired PRIDE project …
… and start re-analyzing the data!
… inspect the project details ….
Reshake PRIDE data!

40. Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2018
Hinxton, 19 July 2018