Proteomics repositories

Proteomics repositories
Dr. Juan Antonio Vizcaíno
Proteomics Team Leader
EMBL-EBI
Hinxton, Cambridge, UK

Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2016
Hinxton, 8 December 2016
• Why sharing MS proteomics data?
• Types of information stored in MS proteomics
repositories.
• Main existing repositories and their main
characteristics
• No data reprocessing
• Data reprocessing
• Other resources
Overview

Juan A. Vizcaíno
juan@ebi.ac.uk
Corresponding public repositories
Genomics
Transcript-
omics
Proteomics
DNA sequence databases
(GenBank, EMBL, DDJB)
ArrayExpress (EBI), GEO (NCBI)
MS proteomics resources (ProteomeXchange)
Metabolomics MetaboLights (MetabolomeXchange)

Juan A. Vizcaíno
juan@ebi.ac.uk
Data sharing in Proteomics
• Proteomics data can be very complex and its interpretation is
often troublesome and/or controversial.
• In other ‘omics’ fields, data sharing ‘culture’ is well established.
Generally, it is considered to be a good scientific practise.
• In proteomics, the ‘culture’ is definitely evolving in that direction.
A big shift is happening in the last few years.
• Scientific journals and funding agencies are two of the main
drivers.

Juan A. Vizcaíno
juan@ebi.ac.uk
Reproducible Science
http://www.nature.com/nature/focus/reproducibility/

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
Journal Submission Recommendations
• Journal guidelines recommend and/or mandate
submission to proteomics repositories:
 Proteomics
 Nature Biotechnology
 Nature Methods
 Molecular and Cellular Proteomics
• Funding agencies are enforcing public deposition of data
to maximize the value of the funds provided.

Juan A. Vizcaíno
juan@ebi.ac.uk
• Why sharing MS proteomics data?
• Types of information stored in MS
proteomics repositories
• Main existing repositories and their main
characteristics
• No data reprocessing
• Data reprocessing
• Other resources
Overview

Juan A. Vizcaíno
juan@ebi.ac.uk
Main types of information stored
• 1) Original experimental data recorded by the mass
spectrometer (primary data) -. Raw data and peak lists.
• 2) Identification results inferred from the original primary
data
• 3) Quantification information
• 4) Experimental and technical metadata
• 5) Any other type of information (e.g. scripts)

Juan A. Vizcaíno
juan@ebi.ac.uk
Current PSI Standard File Formats for MS
• mzMLMS data
• mzIdentMLIdentification
• mzQuantMLQuantitation
• mzTabFinal Results
• TraMLSRM

Juan A. Vizcaíno
juan@ebi.ac.uk
• Many different workflows need to be supported. They provide
complementary ‘views’.
• No data reprocessing. Data is stored as ‘published’ or
originally analysed:
• PRIDE Archive (focused on MS/MS data, all supported)
• MassIVE (focused on MS/MS data)
• jPOST (focused on MS/MS data)
• PASSEL (only SRM data)
• Data reprocessing (MS/MS data):
• PeptideAtlas and GPMDB
• proteomicsDB and HPM

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
Resources that don’t reprocess data
1) Resources that try to represent the authors’ analysis
view on the data.
• Various workflows are allowed and they can provide
complementary results.
• Data are not ‘updated’ in time. However, meta-analysis
on top is possible.
• Accumulation of FDRs when datasets are combined.
• Main representatives: PRIDE Archive and MassIVE
(MS/MS data) and PeptideAtlas/PASSEL (SRM data).
• Data standards are essential.

Juan A. Vizcaíno
juan@ebi.ac.uk
• Many different workflows need to be supported. They provide
complementary ‘views’.
• No data reprocessing. Data is stored as ‘published’ or
originally analysed:
• PRIDE Archive (focused on MS/MS data, all supported)
• MassIVE (focused on MS/MS data)
• jPOST (focused on MS/MS data)
• PASSEL (only SRM data)
• Data reprocessing (MS/MS data):
• PeptideAtlas and GPMDB
• proteomicsDB and HPM.

Juan A. Vizcaíno
juan@ebi.ac.uk
• 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

Juan A. Vizcaíno
juan@ebi.ac.uk
MassIVE (UCSD)
http://proteomics.ucsd.edu/service/massive/
• Data repository for MS proteomics data
• Tools available for users to analyse their own data
• Joined ProteomeXchange on June 2014.

Juan A. Vizcaíno
juan@ebi.ac.uk
http://massive.ucsd.eduhttp://proteomics.ucsd.edu
MassIVE Interactivity
• MassIVE = Mass spectrometry Interactive Virtual Environment

Juan A. Vizcaíno
juan@ebi.ac.uk
MassIVE: Do it yourself
1. MSGF+ - Database search engine
2. MSPLIT – Spectral Library Search Engine
3. ENOSI – ProteoGenomic Search Engine
4. MODa - Multi-blind modification database search engine
5. Spectral Networks – spectral alignment-based
analysis and propagation of identifications
6. Multi-pass - MSPLIT, MSGFDB, MODa cascade Search
Workflow
7. MSGFDB - Database search engine
8. MSPLIT-DIA – Spectral Library Search for SWATH
9. Upload your own! (mzIdentML, mzTab, TSV)
http://massive.ucsd.eduhttp://proteomics.ucsd.edu

Juan A. Vizcaíno
juan@ebi.ac.uk
jPOST Repository site
(www.jpost.org)
• Joined ProteomeXchange
on July 2016

Juan A. Vizcaíno
juan@ebi.ac.uk
• Suitable for SRM assays
• Use the PSI standard TraML
plus the output of the most
popular vendor pipelines
• Started in 2012
• Part of the ProteomeXchange
consortium
http://www.peptideatlas.org/passel/
Farrah et al., Proteomics, 2012
PASSEL: repository for SRM data

Juan A. Vizcaíno
juan@ebi.ac.uk
Reprocessing repositories
• These resources collect MS raw data and reprocess it using
one given analysis pipeline, and an up to date protein
sequence database.
• Advantage: They provide a ‘standardized’ and updated view
on the experimental data available.
• Only one common analysis method is used and there can be
information loss.
• Different from the author’s view on the data.
• Main resources: GPMDB and PeptideAtlas (ISB, Seattle).
• Novel resources: proteomicsDB and the Human Proteome Map.

Juan A. Vizcaíno
juan@ebi.ac.uk
http://www.peptideatlas.org
- Developed at the Institute for Systems
Biology (ISB, Seattle, USA)
- Peptide identifications from MS/MS
approaches
- Data are reprocessed using the popular
Trans Proteomic Pipeline (TPP)
- Uses PeptideProphet to derive a
probability for the correct identification for
all contained peptides
PeptideAtlas

Juan A. Vizcaíno
juan@ebi.ac.uk
• All peptides IDs are mapped to
Ensembl using ProteinProphet
(to handle protein inference)
• Provides proteotypic peptide
predictions
• Limited metadata available
• Part of the HPP project
Deutsch et al., Proteomics, 2005
Desiere et al., NAR, 2006.
Deutsch et al., EMBO Rep, 2008
PeptideAtlas

Juan A. Vizcaíno
juan@ebi.ac.uk
Builds are updated in a regular basis (usually once a
year)
Examples of builds:
- Human (HPP context)
- Human plasma
- Human urine
- Drosophila
- Mouse
- Mouse plasma
- Cow
- Yeast
…
PeptideAtlas builds

Juan A. Vizcaíno
juan@ebi.ac.uk
• Originally developed by R.
Beavis & R. Craig
• End point of the GPM
proteomics pipeline, to aid in
the process of validating
peptide MS/MS spectra and
protein coverage patterns.
http://gpmdb.thegpm.org/ Craig et al., J Proteome Res, 2004
GPMDB (Global Proteome Machine DB)

Juan A. Vizcaíno
juan@ebi.ac.uk
• Data are reprocessed using
the popular X!Tandem or
X!Hunter spectral searching
algorithm
• Also provides proteotypic
peptides
GPMDB

Juan A. Vizcaíno
juan@ebi.ac.uk
• Nice visualization features
• Provides very limited
annotation with GO, BTO
• Some support to targeted
approaches is available
• Part of the HPP consortium
GPMDB

Juan A. Vizcaíno
juan@ebi.ac.uk
http://thehpp.org/
The Human Proteome Project (HPP)

Juan A. Vizcaíno
juan@ebi.ac.uk
HPP guidelines version 2.1

Juan A. Vizcaíno
juan@ebi.ac.uk
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.
•Two proteomics resources have been developed:
proteomicsDB and the Human Proteome Map (HPM).Nature cover 29 May 2014

Juan A. Vizcaíno
juan@ebi.ac.uk
ProteomicsDB https://www.proteomicsdb.org/
• Data analysis using Mascot and MaxQuant
• The way the Protein FDR is calculated is controversial
•Quantification information using label free techniques
•New datasets are added in a regular basis

Juan A. Vizcaíno
juan@ebi.ac.uk
ProteomicsDB (2)

Juan A. Vizcaíno
juan@ebi.ac.uk
Human Proteome Map (HPM)
• Developed by the Pandey group.
• Data reanalysis using Mascot.
• Protein FDR is not mentioned at all in the
corresponding Nature paper.
• Static resource: it will not be updated
any longer.
http://www.humanproteomemap.org/

Juan A. Vizcaíno
juan@ebi.ac.uk
Chorus
https://chorusproject.org/pages/ind
ex.html
• Developed by M. MacCoss’ group in
Seattle (UW).
• Built on top of Amazon Cloud
technologies
• Provides data analysis capabilities for
the users
• Free for public datasets.
• The objective is to connect the data to
analysis tools in a cloud environment

Juan A. Vizcaíno
juan@ebi.ac.uk
MaxQB
Human Proteinpedia
Other repositories

Juan A. Vizcaíno
juan@ebi.ac.uk
COPaKB
Cardiac Organellar Protein Atlas Knowledgebase
International collaboration (EMBL-EBI involved)
Windows Client and iPad App
Submit data for analysis in dta and mzML formats
Data submitted to a ProLuCID pipeline
No MS data download

Juan A. Vizcaíno
juan@ebi.ac.uk
CPTAC data portal

Juan A. Vizcaíno
juan@ebi.ac.uk
Pep2pro (Arabidopsis)
http://fgcz-pep2pro.uzh.ch/
Centered on Arabidopsis data
Download spectra by spectra
Quantitative information
Linked to gelmap.de (2DE)

Juan A. Vizcaíno
juan@ebi.ac.uk
FINAL THOUGHTS

Juan A. Vizcaíno
juan@ebi.ac.uk
Why are repositories not more popular?
1. Don’t want to share data
• Researchers don’t like to be shown that they did not analyze the
data as well as they could have.
• Their FDR may be higher than they reported/think.
• Researchers are worried that they missed something in the data
that they could discover if they go back to it at a later date
• Don’t want other authors to get a publication from their data.
• However, this philosophy is changing gradually…
Slide from R. Chalkley

Juan A. Vizcaíno
juan@ebi.ac.uk
Why are repositories not more popular? (2)
2. Submission burden
• Getting data into correct format may require some work
• Author is not necessarily computer-savvy
• Having to also supply metadata is seen as a burden, if the
information is already present in an associated manuscript
• Associated raw data may be many GB in size; file transfer to
repository could take a while
Authors are impatient: want to spend time doing science, not
administration!
Slide from R. Chalkley

Juan A. Vizcaíno
juan@ebi.ac.uk
• Importance of sharing MS proteomics data
• The main existing proteomics repositories are
complementary in focus and functionality.
• Main characteristics of:
• PeptideAtlas and GPMDB (Reprocess data)
• PASSEL, MassIVE, jPOST and PRIDE Archive
(at present they do not reprocess data).
• New resources: proteomicsDB, HPM.
• Chorus, CPTAC portal,…
Conclusions

Juan A. Vizcaíno
juan@ebi.ac.uk
• Perez-Riverol et al., Proteomics, 2015. PMID: 25158685
Recommended reading

Juan A. Vizcaíno
juan@ebi.ac.uk
Questions?

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