Proteomics public data resources: enabling "big data" analysis in proteomics

Proteomics public data resources:
enabling “big data” analysis in proteomics
Dr. Juan Antonio Vizcaíno
EMBL-European Bioinformatics Institute
Hinxton, Cambridge, UK

Juan A. Vizcaíno
juan@ebi.ac.uk
International de.NBI Symposium
Heidelberg, 9 November 2016
Overview
• Intro: Concept of “Big data” in biology and proteomics
• PRIDE Archive and ProteomeXchange
• PRIDE tools
• Reuse of public proteomics data
• Working with “Big data”: PRIDE Cluster

Juan A. Vizcaíno
juan@ebi.ac.uk
“Big data”: definition
Slide from: http://www.ibmbigdatahub.com/

Juan A. Vizcaíno
juan@ebi.ac.uk
“Big data” in biology
The term has been applied so far mainly to genomics

Juan A. Vizcaíno
juan@ebi.ac.uk
One slide intro to MS based proteomics
Hein et al., Handbook of Systems Biology, 2012

Juan A. Vizcaíno
juan@ebi.ac.uk
Data resources at EMBL-EBI
Genes, genomes & variation
ArrayExpress
Expression Atlas PRIDE
InterPro Pfam UniProt
ChEMBL ChEBI
Molecular structures
Protein Data Bank in Europe
Electron Microscopy Data Bank
European Nucleotide Archive
European Variation Archive
European Genome-phenome Archive
Gene & protein expression
Protein sequences, families & motifs
Chemical biology
Reactions, interactions &
pathways
IntAct Reactome MetaboLights
Systems
BioModels Enzyme Portal BioSamples
Ensembl
Ensembl Genomes
GWAS Catalog
Metagenomics portal
Europe PubMed Central
Gene Ontology
Experimental Factor
Ontology
Literature & ontologies

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
• 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
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
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
Deustch et al., NAR, 2017, in press

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
ProteomeCentral: Centralised portal for all PX
datasets
http://proteomecentral.proteomexchange.org/cgi/GetDataset

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE: Source of MS proteomics data
• PRIDE Archive already provides or
will soon provide MS proteomics
data to other EMBL-EBI resources
such as UniProt, Ensembl and the
EBI Expression Atlas.
http://www.ebi.ac.uk/pride/archive

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Archive – over 4,500 datasets from
over 51 countries and 1,700 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

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Components: Data Submission Process
PRIDE Converter 2
PRIDE Inspector PX Submission Tool
mzIdentML
PRIDE XML
In addition to PRIDE Archive, the PRIDE team develops
and maintains different tools and software libraries to
facilitate the handling and visualisation of MS proteomics
data and the submission process

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
PRIDE Inspector Toolsuite
Wang et al., Nat. Biotechnology, 2012
Perez-Riverol et al., Bioinformatics,
2015
Perez-Riverol et al., MCP, 2016
• PRIDE Inspector - standalone tool to enable visualisation and validation of MS
data.
• Build on top of ms-data-core-api - open source algorithms and libraries for
computational proteomics.
• Supported file formats: mzIdentML, mzML, mzTab (PSI standards), and PRIDE
XML.
• Broad functionality.
https://github.com/PRIDE-Utilities/ms-data-core-api
https://github.com/PRIDE-Toolsuite/pride-inspector
Summary and QC charts Peptide spectra annotation and
visualization

Juan A. Vizcaíno
juan@ebi.ac.uk
PX Submission Tool
 Desktop application for data
submissions to ProteomeXchange via
PRIDE
• Implemented in Java 7
• Streamlines the submission process
• Capture mappings between files
• Retain metadata
• Fast file transfer with Aspera (FASP®
transfer technology) – FTP also
available
• Command line option
Submission tool screenshot

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
Data sharing in Proteomics
Vaudel et al., Proteomics, 2016

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.
•They used many different tissues.
Nature cover 29 May 2014

Juan A. Vizcaíno
juan@ebi.ac.uk
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.

Juan A. Vizcaíno
juan@ebi.ac.uk
Examples of repurposing in proteogenomics

Juan A. Vizcaíno
juan@ebi.ac.uk
Data sharing in Proteomics

Juan A. Vizcaíno
juan@ebi.ac.uk
Challenges for data reuse in proteomics
• Insufficient technical and biological metadata.
• Large computational infrastructure maybe needed (e.g. when
analysing many datasets together).
• Shortage of expertise (people).
• Lack of standardisation in the field.

Juan A. Vizcaíno
juan@ebi.ac.uk
Summary of the talk so far
• PRIDE Archive and other ProteomeXchange resources make
possible data sharing in the MS proteomics field.
• Data sharing is becoming the norm in the field.
• Standalone tools: PRIDE Inspector and PX Submission tool.
• Datasets are increasingly reused (many opportunities):
• Example of one of the drafts of the human proteome.
• Proteogenomics approaches.
• But there are important challenges as well.

Juan A. Vizcaíno
juan@ebi.ac.uk
Overview
• Intro: Concept of “Big data” in biology and proteomics
• PRIDE Archive and ProteomeXchange
• PRIDE tools
• Reuse of public proteomics data
• Working with Big data: PRIDE Cluster

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Cluster: Initial Motivation
• Provide a QC-filtered peptide-centric view of PRIDE.
• Data is stored in PRIDE Archive as originally analysed by the
submitters (no data reprocessing is done).
• Heterogeneous quality, difficult to make the data comparable.
• Enable assessment of (published) proteomics data.

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Cluster
• Provide an aggregated peptide centric view of PRIDE Archive.
• Hypothesis: same peptide will generate similar MS/MS spectra across
experiments.
• Enables QC of peptide-spectrum matches (PSMs). Infer reliable
identifications by comparing submitted identifications of spectra within a
cluster.
 After clustering, a representative spectrum is built for all peptides
consistently identified across different datasets.
Griss et al., Nat. Methods, 2013
Griss et al., Nat. Methods,
2016

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Cluster - Concept

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Cluster: Implementation
• Griss et al., Nat. Methods, 2013
• 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

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
Parallelizing Spectrum Clustering: Hadoop
• Optimizes work distribution among machines.
• Hadoop is a (open source) Framework for parallelism
using the Map-Reduce algorithm by Google.
• Solves many general issues of large parallel jobs:
• Scheduling
• inter-job communication
• failure
https://hadoop.apache.org/

Juan A. Vizcaíno
juan@ebi.ac.uk
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
• 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

Juan A. Vizcaíno
juan@ebi.ac.uk
Examples: one perfect cluster
- 880 PSMs give the same peptide ID
- 4 species
- 28 datasets
- Same instruments

Juan A. Vizcaíno
juan@ebi.ac.uk
Examples: one perfect cluster (2)

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Cluster
Sequence-based
search engines
Spectrum clustering
Incorrectly or
unidentified spectra

Juan A. Vizcaíno
juan@ebi.ac.uk
Output of the analysis
• 1. Inconsistent spectrum clusters
• 2. Clusters including identified and unidentified spectra.
• 3. Clusters just containing unidentified spectra.

Juan A. Vizcaíno
juan@ebi.ac.uk
1. Re-analysis of inconsistent clusters
NMMAACDPR
NMMAACDPR
IGGIGTVPVGR
NMMAACDPR
PPECPDFDPPR
VFDEFKPLVEEPQNLIK
NMMAACDPR
IGGIGTVPVGR
No sequence has a
proportion in the
cluster >50%
Consensus spectrum
PPECPDFDPPR
VFDEFKPLVEEP
QNLIK
Originally submitted identified spectra
Spectrum
clustering

Juan A. Vizcaíno
juan@ebi.ac.uk
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.

Juan A. Vizcaíno
juan@ebi.ac.uk
Validation

Juan A. Vizcaíno
juan@ebi.ac.uk

Juan A. Vizcaíno
juan@ebi.ac.uk
2. Inferring identifications for originally unidentified spectra
52
• 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.

Juan A. Vizcaíno
juan@ebi.ac.uk
3. Consistently unidentified clusters
• 19 M clusters contain only unidentified spectra.
• 41,155 of these spectra have more than 100 spectra (= 12 M
spectra).
• 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.

Juan A. Vizcaíno
juan@ebi.ac.uk
3. Consistently unidentified clusters

Juan A. Vizcaíno
juan@ebi.ac.uk
PRIDE Cluster as a Public Data Mining Resource
55
• 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

Juan A. Vizcaíno
juan@ebi.ac.uk
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., 2016, BioRXxiv

Juan A. Vizcaíno
juan@ebi.ac.uk
OmicsDI: Portal for omics datasets

Juan A. Vizcaíno
juan@ebi.ac.uk
Summary part 2
• Using a “big data” approach we were able to get extra
knowledge from all the public data in PRIDE Archive.
• Spectrum clustering enables QC in proteomics resources
such as PRIDE Archive.
• It is possible to detect spectra that are consistently
unidentified across hundreds of datasets (maybe peptide
variants, or peptides with PTMs not initially considered).
• OmicsDI: new platform to identify public datasets coming
from different omics technologies (more possibilities for data
reuse!)

Juan A. Vizcaíno
juan@ebi.ac.uk
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

Juan A. Vizcaíno
juan@ebi.ac.uk
Questions?
http://www.slideshare.net/JuanAntonioVizcaino

Proteomics public data resources: enabling "big data" analysis in proteomics

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