The mzQuantML Data Standard for Mass Spectrometry–based Quantitative Studies in Proteomics

The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS) 1The abbreviations used are:CVcontrolled vocabularyiTRAQisobaric tag for relative and absolute quantitationLCliquid chromatographyMIAPEMinimum Information about a Proteomics ExperimentMSmass spectrometryPSIProteomics Standards InitiativeSILACstable isotope labeling by amino acids in cell cultureXSDXML Schema Definition. 1The abbreviations used are:CVcontrolled vocabularyiTRAQisobaric tag for relative and absolute quantitationLCliquid chromatographyMIAPEMinimum Information about a Proteomics ExperimentMSmass spectrometryPSIProteomics Standards InitiativeSILACstable isotope labeling by amino acids in cell cultureXSDXML Schema Definition. leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml. The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS) 1The abbreviations used are:CVcontrolled vocabularyiTRAQisobaric tag for relative and absolute quantitationLCliquid chromatographyMIAPEMinimum Information about a Proteomics ExperimentMSmass spectrometryPSIProteomics Standards InitiativeSILACstable isotope labeling by amino acids in cell cultureXSDXML Schema Definition. 1The abbreviations used are:CVcontrolled vocabularyiTRAQisobaric tag for relative and absolute quantitationLCliquid chromatographyMIAPEMinimum Information about a Proteomics ExperimentMSmass spectrometryPSIProteomics Standards InitiativeSILACstable isotope labeling by amino acids in cell cultureXSDXML Schema Definition. leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml. controlled vocabulary isobaric tag for relative and absolute quantitation liquid chromatography Minimum Information about a Proteomics Experiment mass spectrometry Proteomics Standards Initiative stable isotope labeling by amino acids in cell culture XML Schema Definition. controlled vocabulary isobaric tag for relative and absolute quantitation liquid chromatography Minimum Information about a Proteomics Experiment mass spectrometry Proteomics Standards Initiative stable isotope labeling by amino acids in cell culture XML Schema Definition. The Proteomics Standards Initiative (PSI) has been working for ten years to improve the reporting and standardization of proteomics data. The PSI has published minimum reporting guidelines, called MIAPE (Minimum Information about a Proteomics Experiment) documents, for MS-based proteomics (1Taylor C.F. Paton N.W. Lilley K.S. Binz P.-A. Julian R.K. Jones A.R. Zhu W. Apweiler R. Aebersold R. Deutsch E.W. Dunn M.J. Heck A.J.R. Leitner A. Macht M. Mann M. Martens L. Neubert T.A. Patterson S.D. Ping P. Seymour S.L. Souda P. Tsugita A. Vandekerckhove J. Vondriska T.M. Whitelegge J.P. Wilkins M.R. Xenarios I. Yates J.R. Hermjakob H. The minimum information about a proteomics experiment (MIAPE).Nat. Biotechnol. 2007; 25: 887-893Crossref PubMed Scopus (580) Google Scholar) and molecular interactions (2Orchard S. Salwinski L. Kerrien S. Montecchi-Palazzi L. Oesterheld M. Stumpflen V. Ceol A. Chatr-aryamontri A. Armstrong J. Woollard P. Salama J.J. Moore S. Wojcik J. Bader G.D. Vidal M. Cusick M.E. Gerstein M. Gavin A.-C. Superti-Furga G. Greenblatt J. Bader J. Uetz P. Tyers M. Legrain P. Fields S. Mulder N. Gilson M. Niepmann M. Burgoon L. Rivas J.D.L. Prieto C. Perreau V.M. Hogue C. Mewes H.-W. Apweiler R. Xenarios I. Eisenberg D. Cesareni G. Hermjakob H. The minimum information required for reporting a molecular interaction experiment (MIMIx).Nat. Biotechnol. 2007; 25: 894-898Crossref PubMed Scopus (212) Google Scholar), as well as data standards for raw/processed MS data in mzML (3Martens L. Chambers M. Sturm M. Kessner D. Levander F. Shofstahl J. Tang W.H. Römpp A. Neumann S. Pizarro A.D. Montecchi-Palazzi L. Tasman N. Coleman M. Reisinger F. Souda P. Hermjakob H. Binz P.-A. Deutsch E.W. mzML—a community standard for mass spectrometry data.Mol. Cell. Proteomics. 2011; 10 (R110.000133)Abstract Full Text Full Text PDF PubMed Scopus (452) Google Scholar), peptide and protein identifications in mzIdentML (4Jones A.R. Eisenacher M. Mayer G. Kohlbacher O. Siepen J. Hubbard S. Selley J. Searle B. Shofstahl J. Seymour S. Julian R. Binz P.-A. Deutsch E.W. Hermjakob H. Reisinger F. Griss J. Vizcaino J.A. Chambers M. Pizarro A. Creasy D. The mzIdentML data standard for mass spectrometry-based proteomics results.Mol. Cell. Proteomics. 2012; 11 (M111.014381)Abstract Full Text Full Text PDF PubMed Scopus (158) Google Scholar), transitions for selected reaction monitoring analysis in TraML (5Deutsch E.W. Chambers M. Neumann S. Levander F. Binz P.-A. Shofstahl J. Campbell D.S. Mendoza L. Ovelleiro D. Helsens K. Martens L. Aebersold R. Moritz R.L. Brusniak M.-Y. TraML—a standard format for exchange of selected reaction monitoring transition lists.Mol. Cell. Proteomics. 2012; 11 (R111.015040)Abstract Full Text Full Text PDF Scopus (61) Google Scholar), and molecular interactions in PSI-MI format (6Hermjakob H. Montecchi-Palazzi L. Bader G. Wojcik J. Salwinski L. Ceol A. Moore S. Orchard S. Sarkans U. von Mering C. Roechert B. Poux S. Jung E. Mersch H. Kersey P. Lappe M. Li Y. Zeng R. Rana D. Nikolski M. Husi H. Brun C. Shanker K. Grant S.G.N. Sander C. Bork P. Zhu W. Pandey A. Brazma A. Jacq B. Vidal M. Sherman D. Legrain P. Cesareni G. Xenarios I. Eisenberg D. Steipe B. Hogue C. Apweiler R. The HUPO PSI's molecular interaction format—a community standard for the representation of protein interaction data.Nat. Biotechnol. 2004; 22: 177-183Crossref PubMed Scopus (510) Google Scholar). Standards are particularly important for quantitative proteomics research, because the associated bioinformatics analysis is highly challenging as a result of the range of different experimental techniques for deriving abundance values for proteins using MS. The techniques can be broadly divided into those based on (i) differential labeling, in which a metabolic label or chemical tag is applied to cells, peptides, or proteins, samples are mixed, and intensity signals for peptide ions are compared within single MS runs; or (ii) label-free methods in which MS runs occur in parallel and bioinformatics methods are used to extract intensity signals, ensuring that like-for-like signals are compared between runs (7Gonzalez-Galarza F.F. Lawless C. Hubbard S.J. Hermjakob H. Jones A.R. A critical appraisal of techniques, software packages and standards for quantitative proteomic analysis.OMICS. 2012; 16: 431-442Crossref PubMed Scopus (43) Google Scholar). In most label-based and label-free approaches, peptide ratios or abundance values must be summarized in order for one to arrive at relative protein abundance values, taking into account ambiguity in peptide-to-protein inference. Absolute protein abundance values can typically be derived only using internal standards spiked into samples of known abundance (8Ross P.L. Huang Y.N. Marchese J.N. Williamson B. Parker K. Hattan S. Khainovski N. Pillai S. Dey S. Daniels S. Purkayastha S. Juhasz P. Martin S. Bartlet-Jones M. He F. Jacobson A. Pappin D.J. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.Mol. Cell. Proteomics. 2004; 3: 1154-1169Abstract Full Text Full Text PDF PubMed Scopus (3680) Google Scholar, 9Pratt J.M. Simpson D.M. Doherty M.K. Rivers J. Gaskell S.J. Beynon R.J. Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes.Nat. Protoc. 2006; 1: 1029-1043Crossref PubMed Scopus (304) Google Scholar). The PSI has recently developed a MIAPE-Quant document defining and describing the minimal information necessary in order to judge or repeat a quantitative proteomics experiment. Software packages tend to report peptide or protein abundance values in a bespoke format, often as tab or comma separated values, for import into spreadsheet software. In complementary work, the PSI has developed a standard format for capturing these final results in a standardized tab separated value format, called mzTab, suitable for post-processing and visualization in end-user tools such as Microsoft Excel or the R programming language. The final results of a quantitative analysis are sufficient for many purposes, such as performing statistical analysis to determine differential expression or cluster analysis to find co-expressed proteins. However, mzTab (or similar bespoke formats) was not designed to hold a trace of how the peptide and protein abundance values were calculated from MS data (i.e. metadata is lost that might be crucial for other tasks). For example, most quantitative software packages detect and quantify so-called “features” (representing all ions collected for a given peptide) in two-dimensional MS data, where the two dimensions are retention time from liquid chromatography (LC) and mass over charge (m/z). Without capturing the two-dimensional coordinates of the features, it is not possible to write visualization software showing exactly what the software has quantified; researchers have to trust that the software has accurately quantified all ions from isotopes of a given peptide, excluding any overlapping ions derived from other peptides. The history of proteomics research has been one in which studies of highly variable quality have been published. There is quality or performed on quantitative software M. A. C. data standards and 2011; PubMed Scopus Google Scholar), it is to quality on a of peptide and protein abundance values. The PSI has recently developed which can or MS data in a format, and the mzIdentML to results and the important metadata as software such that peptide and protein data can be two standards are now used for data and to support software that groups can on format there has been no widely used format or data standard for capturing metadata and data to the quantitation of analysis In work, we report the mzQuantML standard from the which has recently the PSI standardization J.A. Martens L. Hermjakob H. Julian R.K. Paton N.W. The PSI document and on the PSI 2007; PubMed Scopus Google Scholar), from which was that quantitative proteomics research from for what have to data at of the analysis The mzQuantML standard has been designed to quantitative values calculated for features, peptides, proteins, protein groups (where there is ambiguity in protein inference), associated software has been designed to small molecule data to improve The format can represent experimental replicates and grouping of and it has been designed via an and The mzQuantML was developed over years at PSI S. Jones A. C. Hermjakob H. a report on the of the PubMed Scopus Google Scholar, S. J.P. Deutsch E.W. Eisenacher M. J.A. Hermjakob H. report on the of the 2011; PubMed Scopus Google Scholar, S. Binz P.-A. C. Gilson M.K. Jones A.R. G. Vizcaino J.A. Deutsch E.W. Hermjakob H. years of proteomic a report on the 2012; PubMed Scopus Google Scholar), and between the The use and for the of mzQuantML are as are from the that the format for the reporting of quantitative proteomic data from reporting to MIAPE of quantitative data to public data exchange between software import of data into statistical and the to or the analysis workflow using the no have that the format final abundance values or for peptides, proteins, and protein quantitation values about abundance values at the of a single and of runs; the evidence trail for how final abundance values were such as the used for quantifying peptides and between on different regions of the MS or on different MS runs that report on the peptide or small and about sufficient to the of data All have been and to any to that the is and the possible can be The has been developed as an XML Schema by controlled vocabulary and as of the controlled vocabulary G. Montecchi-Palazzi L. Ovelleiro D. Jones A.R. Binz P.-A. Deutsch E. Orchard S. J.A. Hermjakob H. C. Eisenacher M. The HUPO Proteomics Standards spectrometry controlled PubMed Scopus Google Scholar), used in mzIdentML, and To the of different quantitative have been as of the and in software. are required to between the techniques in the (i) (ii) MS and of mzQuantML are to support selected reaction monitoring techniques that be in The are to in a single are required to that software mzQuantML data from a the and All resources have been in the public domain the of the In the we different of the mzQuantML which is summarized in The metadata about how a quantitative analysis was performed by software a of the experimental in of or and grouping of replicates into so-called study are important to as many quantitative software packages use such information for reporting data over The format structures for capturing data values at from MS runs, peptide signals derived from different samples or between MS and proteins or protein groups quantified by the software. 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P. Beynon R.J. D. K. F. J. Jones A.R. A software and for performing stable isotope and quantification using 2012; 16: PubMed Scopus Google Scholar) and J. Mann M. peptide mass and protein Biotechnol. PubMed Scopus Google Scholar), and in a of for and mzQuantML and performing to mzTab (for the PSI mzQuantML There is a Microsoft Excel to mzQuantML that in values of time data of two samples different experimental in a format and an mzQuantML output the relative abundance value ratios for the peptides and proteins. The mzQuantML data is used as the for quantitative in the which output in and mzQuantML The M. A. C. A. R. E. N. O. A. K. Kohlbacher O. software for mass PubMed Scopus Google Scholar) support for and mzQuantML in on O. K. C. E. N. O. Sturm M. proteomics 2007; PubMed Scopus Google Scholar) can mzQuantML from the quantitation tools and can import mzQuantML for internal The can for XML and the is of mzQuantML to the and the use of the tools for the of mzQuantML data to In the context of MS proteomics the of quantitative data has been limited J.A. J.M. Martens L. Proteomics data a for data and as a for Proteomics. PubMed Scopus Google Scholar) because of the of data standardization and the of experimental is that mzQuantML be used in the data The to standard and data the MS-based proteomics data J.A. R. Reisinger F. H. J.M. J. Hermjakob H. Martens L. The Proteomics PubMed Scopus Google Scholar) and F. Deutsch E.W. P. J. S. J. Aebersold R. The 2006; PubMed Scopus Google Scholar). the of the workflow for data has been and a of data have been (for an of public the quantitative information can be in any format as that are and available for by The of the data workflow for quantification information is in The and of mzQuantML and mzTab are for the of these and is the of mzQuantML is a in the by the are available from the project that the of experimental techniques by the from which selected are The a single MS from a analysis versus using and performed in the in from which quantitative values are for and peptide a single MS from an analysis using and the intensity is for from within by for MS runs in a label-free performed using data are for proteins and peptides and has a of the quantified by the software, using the a single is encoded the as not the coordinates of the isotopes has to all by the in a encoded in mzIdentML The mzQuantML and mzIdentML have been to a analysis trace for a experimental data and thus these are other is that mzQuantML be be and by software as is in the The mzQuantML is and such are given that the data for analysis or The a in which two samples were quantified in a time analysis at time a ten in and two to the replicate of the The mzQuantML and mzTab have been developed in a by the PSI to different mzQuantML has been designed as a format for for import into visualization or post-processing software and to that a trace of analysis is in a standardized format, which might be particularly for proteomics in In mzTab has been developed as a for the of final end-user visualization in or statistical software. There is considerable between the two for reporting final abundance values for proteins or peptides and to how the For the has been to mzTab a the and use by mzTab, and mzIdentML the mzTab, mzIdentML, and mzQuantML The PSI has recently developed and a minimum information document for quantification studies called MIAPE is available from the PSI The MIAPE document what information is to report in order to the study to be a of the labeling software and parameters grouping of and mzQuantML has the to represent a MIAPE as in the material associated the be that a mzQuantML might not be MIAPE as might not be available to the software. mzQuantML has the to represent information by MIAPE For example, mzQuantML can a trace of a software internal data and which might not be by MIAPE In parallel MIAPE have and on the and data that be a proteomics S. Aebersold R. protein the of Cell. Proteomics. 2006; Full Text Full Text PDF PubMed Scopus Google Scholar). mzQuantML has been designed to support such for quantitative data, as in the material associated the The mzQuantML standard has been designed to improve the for software in and visualization of data to that data sets to public a trace of how protein values were calculated via peptide to regions in two-dimensional The project is by software to that the stable of mzQuantML can be used to different experimental methods widely used in proteomics and to and techniques as are published. Several different are now using mzQuantML as the of and the and are by an of the the of the standard format, there is that public for proteomics data to quantitative data sets for community and to the project at a PSI via the or the Google from the Proteomics Standards Initiative for and at