Proteomics data standards

Introduction to the PSI standard data
formats
Dr. Juan Antonio Vizcaíno
PRIDE Group Coordinator
Proteomics Services Team
EMBL-EBI
Hinxton, Cambridge, UK

Juan A. Vizcaíno
juan@ebi.ac.uk
WT Proteomics Bioinformatics Course 2015
Hinxton, 10 December 2015
Overview
• A couple of slides about the need of data standards
• The Proteomics Standards Initiative
• Existing data standards
• Connection with ProteomeXchange and IMEx

Juan A. Vizcaíno
juan@ebi.ac.uk
Standards are needed in life: also in bioinformatics…
With a small number
of standards,
data converters are feasible
Data standards are needed

Juan A. Vizcaíno
juan@ebi.ac.uk
Taken from Biocomical, http://biocomicals.blogspot.com

Juan A. Vizcaíno
juan@ebi.ac.uk
Mass Spectrometry (MS)-based proteomics
• Many different workflows -> Many different data
types -> Need for several data standards.
• Discovery mode:
• Bottom-up proteomics
• Data dependent acquisition
• Data independent acquisition
• Top down proteomics
• Targeted mode:
• SRM (Selected Reaction Monitoring)

Juan A. Vizcaíno
juan@ebi.ac.uk
•Develops data format standards for proteomics.
•Both data representation and annotation standards.
•Involves data producers, database providers, software producers,
publishers, …
•Active Workgroups: MI, MS, PI, Mod.
•Inter-group activities: MIAPE and Controlled Vocabularies.
•Started in 2002, so some experience already…
•One annual meeting in March-April, regular phone calls.
•Close interaction with the metabolomics community.
http://www.psidev.info
HUPO Proteomics Standards Initiative

Juan A. Vizcaíno
juan@ebi.ac.uk
PSI Deliverables
•Minimum information (MIAPE) specifications: Format-independent
specification of minimum information guidelines.
•Formats: Usually an XML schema (but also tab-delimited files) capable of
representing the relevant Minimum Information, plus additional detailed data
for the domain.
•Controlled vocabularies: Usually an OBO-style hierarchical controlled
vocabulary precisely defining the metadata that are encoded in the formats.
•Databases and Tools: Foster software implementations to make the
standards truly useful.
•Community interaction to ensure deposition of data in public repositories.

Juan A. Vizcaíno
juan@ebi.ac.uk
PSI MS Controlled Vocabulary
Mayer et al., Database, 2013

Juan A. Vizcaíno
juan@ebi.ac.uk
The typical dilemma
•Data standards need to be stable to promote adoption
•Proteomics standards need to evolve very rapidly:
• Data is inherently very complex
• Experimental techniques are evolving all the time

Juan A. Vizcaíno
juan@ebi.ac.uk
•MS data: mzML (also used in MS metabolomics).
•Protein and peptide identifications: mzIdentML.
•Peptide and protein quantification: mzQuantML (also supports
small molecules).
•SRM transitions (for targeted proteomics): TraML.
•Molecular interactions: PSI MI XML and MITAB.
•Latest addition (just published): mzTab: identification and
quantification results for peptides, proteins and small molecules
(also used in MS metabolomics).
www.psidev.info
Existing data standards in proteomics

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

Juan A. Vizcaíno
juan@ebi.ac.uk
Binary data
mzData
mzXML
mzML
XML-based
files
.dta, .pkl, .mgf,
.ms2
Peak lists
Data formats for mass spectra data

Juan A. Vizcaíno
juan@ebi.ac.uk
An example of success story: mzML
• A data format for the storage and exchange of MS output files
• Designed by merging the best aspects of both mzData and mzXML
• Developed with full participation of academic researchers, hardware
and software vendors
• Expected to replace mzXML and mzData, but not expected to
completely replace vendor binary formats
• Captures spectra (raw data or peak lists), chromatograms and related
metadata
• Version 1.0 released in June 2008, v1.1 released in June 2009
• Many implementations already exist
• Version 1.2 with enhanced compression considered for 2014
Martens et al., MCP, 2011

Juan A. Vizcaíno
juan@ebi.ac.uk
Martens et al., MCP, 2011

Juan A. Vizcaíno
juan@ebi.ac.uk
The most popular search
engines support mzML
Many parser libraries available
Conversion from raw files
into mzMLhttp://www.psidev.info/mzml_1_0_0

Juan A. Vizcaíno
juan@ebi.ac.uk
Application of mzML to metabolomics

Juan A. Vizcaíno
juan@ebi.ac.uk
mzIdentML, mascot
.dat, sequest .out,
SpectrumMill .spo
pep.xml, prot.xml
Only qualitative data!
Data formats for output from search engines

Juan A. Vizcaíno
juan@ebi.ac.uk
mzIdentML: peptide and protein identifications
• Overview
• XML-based data standard for peptide and protein identifications e.g.
following database search and protein inference.
• Sections for all PSMs, proteins/protein groups inferred,
protocols/parameters etc.
• Timeline:
• Original 1.0 version in Aug 2009.
• Version 1.1 stable (Aug 2011).
• Manuscript published in MCP in 2012*.
• 2012-2015:
• Improving support for protein grouping multiple search engines, pre-fractionation
approaches and de novo sequencing.
• Now firmly embedded as part of ProteomeXchange submission
process, and supported by lots of external software.
* Jones, A. R., Eisenacher, M., Mayer, G., Kohlbacher, O., et al., The mzIdentML data standard for mass spectrometry-based
proteomics results. Molecular & Cellular Proteomics 2012, 11, M111.014381.

Juan A. Vizcaíno
juan@ebi.ac.uk
mzIdentML: peptide and protein identifications
* Jones, A. R., Eisenacher, M., Mayer, G., Kohlbacher, O., et al., The mzIdentML data standard for mass spectrometry-based
proteomics results. Molecular & Cellular Proteomics 2012, 11, M111.014381.
MzIdentML
CvList
AnalysisSoftwareList
AnalysisSampleCollection
SequenceCollection
AnalysisCollection
AnalysisProtocolCollection
DataCollection
URLsof controlledvocabularies
usedwithinthe file
Softwarepackagesused
Biologicalsamples analysed,
annotated with CV terms
Databaseentriesofprotein/ peptide
sequencesidentifiedandmodifications
Applicationofprotocol
inputs= externalspectra1..n
output= SpectrumIdentificationList1
SpectrumIdentificationProtocol
ProteinDetectionProtocol
SpectrumIdentificationProtocol
AdditionalSearchParams
ModificationParams
Enzymes
DatabaseFilters
Parametersfor theprotein
detection procedure
Inputs
AnalysisData
AnalysisData
SpectrumIdentificationList
Thedatabasesearched and theinput
fileconverted tomzIdentML
SpectrumIdentificationResult
SpectrumIdentificationItem
ProteinDetectionList
ProteinAmbiguityGroup
ProteinDetectionHypothesis
All identificationsmade from
searchingonespectrum
One(poly)peptide-
spectrummatch
Aset ofrelatedprotein
identificationse.g.conflicting
peptide-proteinassignments
Asingle proteinidentification
SpectrumIdentification
ProteinDetectionApplicationofprotocol
inputs= SpectrumIdentificationList1..n
output= ProteinDetectionList1

Juan A. Vizcaíno
juan@ebi.ac.uk
An example: XML snippet of mzIdentML

Juan A. Vizcaíno
juan@ebi.ac.uk
Support for mzIdentML
Search
Engine
Results +
MS files
Search
engines
mzIdentML
- Mascot
- MSGF+
- Myrimatch and related tools from D. Tabb’s lab
- OpenMS
- PEAKS
- PeptideShaker (several open source tools)
- ProCon (ProteomeDiscoverer, Sequest)
- Scaffold
- TPP via the idConvert tool (ProteoWizard)
- ProteinPilot (from version 5.0)
- X!Tandem (from PILEDRIVER version)
- Others: library for X!Tandem conversion, lab
internal pipelines, …
- Crux
An increasing number of tools support export to mzIdentML
1.1
Updated list: http://www.psidev.info/tools-implementing-

Juan A. Vizcaíno
juan@ebi.ac.uk
mzIdentML status
• Current status of mzIdentML 1.2
• Improved support for protein grouping**
• Support for PTM localisation scoring added - COMPLETE
• Support for peptide-level statistics added - COMPLETE
• Better support for multiple search approaches - COMPLETE
• Support for crosslinking approaches – INCOMPLETE
• mzIdentML 1.2 submission to document process -
INCOMPLETE
** Seymour, S. L., Farrah, T., Binz, P. A., Chalkley, R. J., et al., A standardized framing for reporting protein identifications in
mzIdentML 1.2. Proteomics 2014, 14, 2389-2399.

Juan A. Vizcaíno
juan@ebi.ac.uk
mzQuantML: Standard for quantitative data
Overview
• XML-based standard for quantification data – following use of quant software
• Can report tables of data (QuantLayers), columns are: StudyVariables, Assays or Ratios;
rows are ProteinGroups, Proteins or Peptides
• Can also capture 2D coordinates of quantified regions in LC-MS (Features)
Timeline
• Version 1.0 rc-1 submitted to the PSI process October 2011; Version 1.0 rc-2 June 2012; Re-
submitted to PSI process in October 2012
• Completed PSI process in Feb 2013 – version 1.0 release
• Supports label-free (intensity), label-free (spectral counting), MS2 tag techniques (e.g. iTRAQ) and
MS1 label techniques e.g. SILAC
• Schema is fixed with each technique defined by separate semantic rules, implemented in validator
software
• Manuscript published in MCP in summer 2013*
• Updated in 2013-2014 to support SRM as a new technique** (version 1.0.1 just submitted to the
document process).
*Walzer et al. MCP 2013 Aug;12(8):2332-40. doi: 10.1074/mcp.O113.028506
**Qi et al. PROTEOMICS, 2015

Juan A. Vizcaíno
juan@ebi.ac.uk
mzQuantML: Standard for quantitative data
*Walzer et al. MCP 2013 Aug;12(8):2332-40. doi: 10.1074/mcp.O113.028506
**Qi et al. PROTEOMICS, 2015

Juan A. Vizcaíno
juan@ebi.ac.uk
mzQuantML status
Open issues
• Updated in 2014 to support absolute quant techniques but
not yet added to specification document
• Some supporting software but not currently implemented for
import in databases (PRIDE?).
• No webpage for software implementations.

Juan A. Vizcaíno
juan@ebi.ac.uk
Wide variety of quantification techniques

Juan A. Vizcaíno
juan@ebi.ac.uk
mzQ library
• The “mzqLibrary” includes a set of applications/modules to
facilitate the use of mzQuantML within analysis pipelines.
• The “mzqLibrary” provides common routines for post-
processing quantitative proteomics data via the command
line interface or graphical user interface - called the
“mzqViewer".
• The mzqViewer integrates with R to produce a heat map or
principal component analysis (PCA) on selected data
tables. Line plots can also be produced showing peptide
and protein quant data across MS runs.
* Qi et al., Proteomics, 2015

Juan A. Vizcaíno
juan@ebi.ac.uk
The mzqLibrary can convert output of
Progenesis (*.csv files), MaxQuant (*.txt files),
and OpenMS (*.consensusXML files) into
mzQuantML (*.mzq).
The mzqLibrary can convert mzQuantML file to four file
formats (XLS, CSV, HTML, and mzTab).
mzqLibrary:
Converter &
Exporter

Juan A. Vizcaíno
juan@ebi.ac.uk
The last addition: mzTab – Aims and concept
• To provide a simple and efficient way of exchanging results from MS
approaches.
• Simpler summary report of the experimental results
• Peptides and proteins identified in a given experimental setting
• Small molecules identified
• Reported quantification values
• Technical and biological metadata
• Easier to parse and use by the research community, systems
biologists as well as providers of knowledge bases.
• It can be used by non-experts in bioinformatics.
• It does not aim to replace mzIdentMl and mzQuantML

Juan A. Vizcaíno
juan@ebi.ac.uk
mzTab - Sections
• Basic information about experiment and sample
• Key-Value pairsMetadata
• Basic information about protein identifications
• Table-basedProtein
• Information about quantified peptides
• Table-basedPeptide
• Information about identified spectra
• Table-basedPSM
• Basic information about identified small molecules
• Table-basedSmall Molecule
Griss et al., MCP, 2014

Juan A. Vizcaíno
juan@ebi.ac.uk
Metadata section - Example

Juan A. Vizcaíno
juan@ebi.ac.uk
Protein Section (label-free)

Juan A. Vizcaíno
juan@ebi.ac.uk
mzTab – Current implementations
• jmzTab (Java API): Version 3.0 is now a stable version. Manuscript
published in the journal Proteomics.
• mzTab Validator, PRIDE XML to mzTab converter (PRIDE team).
• mzIdentML and mzQuantML to mzTab converters (Andy Jones
group).
• Mascot (Matrix Science) exporter.
• MaxQuant: exporter in beta is available.
• OpenMS (version 1.10).
• R/Bioconductor package Msnbase (L. Gatto, Cambridge University).
• LipidDataAnalyzer (J. Hartler, University of Graz, see next talk).
• Metabolights (EBI).

Juan A. Vizcaíno
juan@ebi.ac.uk
mzTab – ongoing development in metabolomics
• More detailed modelling of MS metabolomics data
• Led by S. Neumann (COSMOS EU FP7 project).
• Extension from one to three sections.
Example file exists at
https://github.com/sneumann/mtbls2/faahKO.mzTab
http://www.cosmos-fp7.eu/

Juan A. Vizcaíno
juan@ebi.ac.uk
Unify exchange of transitions with TraML
• PSI’s TraML (Transitions Markup Language)
• Format for encoding SRM/MRM transitions
• Version 1.0.0 now released and published in MCP (Deutsch et al. 2012)
Journal
Articles
Transitions
Databases
Excel
sheets
SRM
Analysis
Software
Instruments
TraML

Juan A. Vizcaíno
juan@ebi.ac.uk
Unify exchange of transitions with TraML
Deutsch et al., MCP, 2012

Juan A. Vizcaíno
juan@ebi.ac.uk
Java library for working with TraML files
It aims:
- command line & simple GUI
- TraML to TSV <-> TSV to TraML
- TSV vendor formats from TSQ, QTRAP5500, AgilentQQQ
Published: Helsens et al., JPR, 2011
TraML software implementations

Juan A. Vizcaíno
juan@ebi.ac.uk
PSI document process
•Every data standard has to undergo a
thorough review process…
•In fact, in practice, two review processes
happen in parallel: the PSI and
manuscript review.

Juan A. Vizcaíno
juan@ebi.ac.uk
Data standard publications
mzML (data standard for MS data) Martens et al., MCP, 2011
mzIdentML (standard for peptide/protein IDs) Jones et al., MCP, 2012
TraML (for SRM transitions) Deutsch et al., MCP, 2012
mzQuantML (for quantitative data) Waltzer et al., MCP, 2013
mzTab (peptide/protein ID and quantification) Griss et al., MCP, 2014
Some updates already going on (e.g. mzIdentML 1.2)

Juan A. Vizcaíno
juan@ebi.ac.uk
Importance of making software available
jmzML (http://code.google.com/p/jmzml/) Cote et al., Proteomics, 2009
jmzIdentML (http://code.google.com/p/jmzidentml/) Reisinger et al., Proteomics, 2012
jmzReader (http://code.google.com/p/jmzreader/) Griss et al., Proteomics, 2012
jmzQuantML (http://code.google.com/p/jmzquantml/) Qi et al., Proteomics, 2014
jmzTab (http://code.google.com/p/mztab/) Xu et al., Proteomics, 2014
PSI promotes implementations. The reference libraries are always
open source.

Juan A. Vizcaíno
juan@ebi.ac.uk
And also… protein-protein interactions
PSI-XML: XML-based format
• Version 2.5 is the working version
• Version 3.0 under development
MITAB: tab-delimited format

Juan A. Vizcaíno
juan@ebi.ac.uk
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).
• EU FP7 CA (01/2011-> 06/2014).
• Common identifier space (PXD identifiers)
• Two supported data workflows: MS/MS and SRM.
• Main objective: Make life easier for researchers
http://www.proteomexchange.org Vizcaíno et al., Nat Biotechnol, 2014

Juan A. Vizcaíno
juan@ebi.ac.uk
MBInfo
The IMEx Consortium (www.imexconsortium.org)
Orchard et al., Nat Methods, 2012

Juan A. Vizcaíno
juan@ebi.ac.uk
Conclusions
• The PSI is a very active group.
• Big variety of data types in proteomics -> Several data
standards available.
• Adoption of standards (as usual) takes some time.
• Public databases greatly benefit from them.

Juan A. Vizcaíno
juan@ebi.ac.uk
Next meeting in Ghent on April!

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

Proteomics data standards

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