proteomics repositories
TRANSCRIPT
EMBL-EBI Now and in the Future
Proteomics repositoriesDr. Juan Antonio Vizcano
Proteomics Team LeaderEMBL-EBIHinxton, Cambridge, UK
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Why sharing MS proteomics data?
Types of information stored in MS proteomics repositories.
Main existing repositories and their main characteristicsNo data reprocessingData reprocessingOther resources
Overview
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Corresponding public repositoriesGenomicsTranscript-omicsProteomicsDNA sequence databases (GenBank, EMBL, DDJB) ArrayExpress (EBI), GEO (NCBI)MS proteomics resources (ProteomeXchange)MetabolomicsMetaboLights (MetabolomeXchange)
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Data sharing in ProteomicsProteomics 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Reproducible Sciencehttp://www.nature.com/nature/focus/reproducibility/
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016What 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 studyProvide supporting information for reliability of any results reported
Information in a publication:ManuscriptSupplementary materialAssociated data submitted to a public repository
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Journal Submission RecommendationsJournal guidelines recommend and/or mandate submission to proteomics repositories:
ProteomicsNature BiotechnologyNature MethodsMolecular and Cellular Proteomics
Funding agencies are enforcing public deposition of data to maximize the value of the funds provided.
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Why sharing MS proteomics data?
Types of information stored in MS proteomics repositories
Main existing repositories and their main characteristicsNo data reprocessingData reprocessingOther resources
Overview
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Main types of information stored1) 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Current PSI Standard File Formats for MS
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Why sharing MS proteomics data?
Types of information stored in MS proteomics repositories.
Main existing repositories and their main characteristicsNo data reprocessingData reprocessingOther resources
Overview
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Proteomics repositories 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 GPMDBproteomicsDB and HPM
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 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.orgNew in 2016Vizcano et al., Nat Biotechnol, 2014Deutsch et al., NAR, 2017, in press
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Why sharing MS proteomics data?
Types of information stored in MS proteomics repositories.
Main existing repositories and their main characteristicsNo data reprocessingData reprocessingOther resources
Overview
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Resources that dont reprocess data1) 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Proteomics repositories 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 GPMDBproteomicsDB and HPM.
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
PRIDE stores mass spectrometry (MS)-based proteomics data:Peptide and protein expression data (identification and quantification)Post-translational modificationsMass spectra (raw data and peak lists)Technical and biological metadataAny other related information
Full support for tandem MS approachesAny type of data can be stored.
PRIDE (PRoteomics IDEntifications) Archivehttp://www.ebi.ac.uk/pride/archiveMartens et al., Proteomics, 2005Vizcano et al., NAR, 2016
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
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MassIVE (UCSD)
http://proteomics.ucsd.edu/service/massive/Data repository for MS proteomics dataTools available for users to analyse their own dataJoined ProteomeXchange on June 2014.
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016http://massive.ucsd.edu http://proteomics.ucsd.edu MassIVE InteractivityMassIVE = Mass spectrometry Interactive Virtual Environment
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016MassIVE: Do it yourselfMSGF+ - Database search engineMSPLIT Spectral Library Search EngineENOSI ProteoGenomic Search EngineMODa- Multi-blind modification database search engineSpectral Networks spectral alignment-based analysis and propagation of identificationsMulti-pass - MSPLIT, MSGFDB, MODa cascade Search WorkflowMSGFDB - Database search engineMSPLIT-DIA Spectral Library Search for SWATHUpload your own! (mzIdentML, mzTab, TSV)
http://massive.ucsd.edu http://proteomics.ucsd.edu
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
jPOST Repository site(www.jpost.org)
Joined ProteomeXchange on July 2016
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Suitable 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, 2012PASSEL: repository for SRM data
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Why sharing MS proteomics data?
Types of information stored in MS proteomics repositories.
Main existing repositories and their main characteristicsNo data reprocessingData reprocessingOther resources
Overview
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Proteomics repositories 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 GPMDBproteomicsDB and HPM.
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Reprocessing repositoriesThese 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 authors view on the data.
Main resources: GPMDB and PeptideAtlas (ISB, Seattle).
Novel resources: proteomicsDB and the Human Proteome Map.
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016All 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, 2005Desiere et al., NAR, 2006.Deutsch et al., EMBO Rep, 2008PeptideAtlas
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Originally 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, 2004GPMDB (Global Proteome Machine DB)
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Data are reprocessed using the popular X!Tandem or X!Hunter spectral searching algorithm
Also provides proteotypic peptides
GPMDB
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Nice visualization features
Provides very limited annotation with GO, BTO
Some support to targeted approaches is available
Part of the HPP consortium
GPMDB
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
http://thehpp.org/The Human Proteome Project (HPP)
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016HPP guidelines version 2.1
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
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Proteomics repositories 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 GPMDBproteomicsDB and HPM
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Draft Human proteome papers published in 2014
Wilhelm et al., Nature, 2014Kim 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016ProteomicsDBhttps://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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016ProteomicsDB (2)
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Human 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Why sharing MS proteomics data?
Types of information stored in MS proteomics repositories.
Main existing repositories and their main characteristicsNo data reprocessingData reprocessingOther resources
Overview
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Chorushttps://chorusproject.org/pages/index.htmlDeveloped 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
MaxQB
Human ProteinpediaOther repositories
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016COPaKBCardiac Organellar Protein Atlas Knowledgebase
International collaboration (EMBL-EBI involved)Windows Client and iPad AppSubmit data for analysis in dta and mzML formatsData submitted to a ProLuCID pipelineNo MS data download
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016CPTAC data portal
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Pep2pro (Arabidopsis)http://fgcz-pep2pro.uzh.ch/Centered on Arabidopsis dataDownload spectra by spectraQuantitative informationLinked to gelmap.de (2DE)
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016FINAL THOUGHTS
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Why are repositories not more popular?Dont want to share data
Researchers dont 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 dateDont want other authors to get a publication from their data.However, this philosophy is changing gradually
Slide from R. Chalkley
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Why are repositories not more popular? (2)2.Submission burdenGetting data into correct format may require some workAuthor 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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
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. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Reproducible Science
http://www.nature.com/nature/focus/reproducibility/
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016
Perez-Riverol et al., Proteomics, 2015. PMID: 25158685
Recommended reading
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 2016Questions?
Juan A. [email protected] Proteomics Bioinformatics Course 2016Hinxton, 8 December 201651