M2Lite: an Open-Source, Light-Weight, Pluggable and Fast Proteome Discoverer MSF to mzidentml Tool

Transcription

1 M2Lite: an Open-Source, Light-Weight, Pluggable and Fast Proteome Discoverer MSF to mzidentml Tool Paul Aiyetan *, Bai Zhang, Lily Chen, Zhen Zhang, Hui Zhang Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA * paiyeta1@jhmi.edu Abstract- Proteome Discoverer is one of many tools used for protein database search and peptide to spectrum assignment in mass spectrometry-based proteomics. However, the inadequacy of conversion tools makes it challenging to compare and integrate its results to those of other analytical tools. Herewith, M2Lite, an open-source, light-weight, easily pluggable and fast conversion tool is presented. M2Lite converts proteome discoverer derived.msf files to the proteomics community defined standard the mzidentml file format. M2Lite s source code is available as open-source at and its compiled binaries and documentation can be freely downloaded at Keywords- Bioinformatics; Peptide Identification; Proteome Discoverer; MSF; Proteomics; mzidentml; pepxml; IDPicker Abbreviations MSF, PSM, FDR, HUPO, PSI, SQL, JDBC, itraq, LC-MS, Magellan Storage File Peptide Spectrum Matches False Discovery Rate Human Proteome Organization Proteomics Standard Initiative Structured Query Language Java Database Connectivity Isobaric Tag for Relative and Absolute Quantitation Liquid Chromatography Mass Spectrometry I. INTRODUCTION Shotgun proteomics presents a powerful tool to identify the presence of proteins within complex biological mixtures [1]. Across laboratories, approaches and tools employed significantly vary. These complicate downstream analytical processes that involve integrating data generated from different platforms. Recently, the Human Proteome Organization (HUPO) Proteomics Standard Initiative (PSI) Proteomics Informatics Working Group defined the mzidentml data standard for mass spectrometry [2, 3]. This is to unify peptide and protein identification across platforms and thus facilitate easy data integration, comparison, exchange and verification. However, the inadequacy of conversion tools may impede the achievement of this goal. Peptide identification in shotgun proteomics most commonly entails assigning observed mass spectrometry spectra to theoretical spectra derived from sequence databases. This is facilitated by protein database search tools and prominent among these is Proteome Discoverer. Proteome Discoverer integrates analytical steps into streamlined automated workflows. It incorporates search tools such as SEQUEST [4] and Mascot [5] in its analytical pipeline. Although a commercial product, it is significantly in use within the proteomics community. Proteome Discoverer is often sold along with the range of proteomics mass spectrometers by Thermo Fisher Scientific Incorporated [6]. According to mass spectrometry market analyses [7, 8], Thermo Scientific remains one of the major vendors. Proteome Discoverer is envisaged to remain prevalent and the need to convert its output to the community defined format is foreseen to persist with projected increase in demand and adoption of proteomics technology. Proteome Discoverer stores its results in a SQLite relational database file with the Magellan Storage File (.MSF) file-name extension. A typical MSF file consists of about sixty-nine relational database tables as shown in Table 1. Thermo-msf-parser [9] and ProCon [10] are known to manipulate.msf files to some extent, however, the inadequacies shown in Table 2, of these limit their utility and thus necessitate a more efficient conversion tool. M2Lite was developed as an open-source, light-weight, pluggable, and fast conversion tool to convert search results outputted from Proteome Discoverer. M2Lite ensures a simplified downstream data analysis independent of vendor supplied tools by converting Proteome Discoverer s MSF files to the mzidentml standard data format

2 TABLE 1 MSF SQLITE DATABASE TABLES AminoAcidModifications AminoAcidModificationsAminoAcids AminoAcidModificationsAminoAcidsNL AminoAcidModificationsNeutralLosses AminoAcids AnnotationDataVersion AnnotationDataset AnnotationGroups AnnotationTypes Annotations AnnotationsAnnotationGroups AnnotationsProtein Chromatograms CustomDataFields CustomDataPeptides CustomDataPeptides_decoy CustomDataProcessingNodes CustomDataProteins CustomDataProteins_decoy CustomDataSpectra Enzymes EnzymesCleavageSpecificities EventAnnotations EventAreaAnnotations Events FastaFiles FastaFilesProteinAnnotations FileInfos MassPeakRelations MassPeaks PeptideScores PeptideScores_decoy Peptides PeptidesAminoAcidModifications PeptidesAminoAcidModifications_decoy PeptidesProteins PeptidesProteins_decoy PeptidesTerminalModifications PeptidesTerminalModifications_decoy Peptides_decoy PrecursorIonAreaSearchSpectra PrecursorIonQuanResults PrecursorIonQuanResultsSearchSpectra ProcessingNodeConnectionPoints ProcessingNodeExtensions ProcessingNodeFilterParameters ProcessingNodeInterfaces ProcessingNodeParameters ProcessingNodeScores ProcessingNodes ProcessingNodesSpectra ProteinAnnotations ProteinIdentificationGroups ProteinScores ProteinScores_decoy Proteins ProteinsProteinGroups PtmAnnotationData ReporterIonQuanResults ReporterIonQuanResultsSearchSpectra ScanEvents SchemaInfo Spectra SpectrumHeaders SpectrumScores TaxonomyNames TaxonomyNodes WorkflowInfo WorkflowMessages II. MATERIALS AND METHODS To generate a sample MSF file, we performed a Proteome Discoverer version 1.3 search on a global proteome tandem mass spectrometry data (TCGA_114C_ A-01_ A-01_ A-01_W_JHUZ_ _F1.raw). The mass spectrometer was a Thermo Scientific Orbitrap Velos TM instrument. The biological sample was an offline fraction of itraq labeled peptides from three separate ovarian tissues and a pooled ovarian tissue samples (itraq4plex). The MSMS data was generated as part of the National Cancer Institute s (NCI), Clinical Proteomics Tumor Analysis Consortium (CPTAC), proteome characterization study. A full description of sample preparation and mass spectrometry protocols is publicly available and may be

3 downloaded at the CPTAC data portal ( Also available for download is the sampled.raw file. We searched using the embedded SEQUEST search engine. We searched against the NCBI RefSeq protein database (September 16, 2013 version). Search parameters we specified were a full tryptic digestion and a maximum missed cleavage of 1, a precursor mass tolerance of 10ppm and a fragment mass tolerance of 0.06 Da (Daltons), ions series weight on b and y ions, an oxidation of Methionine (M) ( Da) as a dynamic modification and static modifications of peptide N- terminus with itraq4plex ( Da) of any residue, itraq4plex modification ( Da) of Lysine (K) and carbamidomethylation ( Da) of Cysteine (C). A maximum of 6 modifications per peptide was allowed. And the search result MSF file was converted to an mzidentml format file using M2Lite as shown in Fig. 1. Fig. 1 Flow diagram showing sequential steps taken to convert Proteome Discoverer s MSF file to mzidentml and to compare its identifications with that of MyriMatch To compare Proteome Discoverer s SEQUEST search engine identification and to demonstrate the capability to integrate obtained results, sample.raw file was converted to the community defined standard mzml format file [11, 12] with the MSConvert utility in ProteoWizard [13]. We searched this mzml file with a second search engine MyriMatch [14] version as shown in Fig. 1. The parameters specified were minimum termini cleavages of 2, maximum missed cleavage of 1, a trypsin cleavage rule, maximum dynamic modification of 3, and a fragment mass tolerance of 0.06 Da. Oxidation ( Da) of Methionine (M) as a dynamic modification was specified, and for static modifications, N-terminal itraq modification ( Da) of any amino acid residue, itraq modification of Lysine (K) residues, and carbamidomethylation ( Da) of Cysteine residues were specified. For data integration, IDPicker [15] version , 64 bit was downloaded and installed. To compare the performance of M2Lite with that of known tools that manipulate MSF files, we downloaded and installed Thermo-msf-parser9 version from and Proteomics Conversion tool (ProCon) versions from We ran M2Lite and these tools using their default parameters on a Windows Server 2008 R2 standard, 64 bit operating system running on Intel Xeon CPU E5520@2.27GHz, 2.27GHz (2 processors) machine with 16 cores and a 32GB RAM. For M2Lite s performance evaluation, another smaller sized sample.msf file from a second sample.raw file TCGA_114C_ A-01_ A-01_ A-01_G_JHUZ_ _RUN2_NOFRACTION.RAW was generated. As with our first sample.raw file, the data was also generated as part of the NCI, CPTAC proteome characterization study. The full description of sample preparation and mass spectrometry protocols is publicly available at the CPTAC data portal. Proteome Discoverer search parameters were as previously described except that, in this case, deamidation ( Da) of Asparagine (N) as dynamic modification and oxidation ( Da) of Methionine as a static modification was specified. M2Lite in the Java version 6 and R version programming languages was implemented and tested. A documentation of its system requirements and installation can be found online at and also in application s README file. And M2Lite on both Proteome Discoverer version 1.3 and 1.4 MSF files was successfully tested. III. RESULTS AND DISCUSSIONS Table 2 summarizes the properties of M2Lite. It also highlights the inadequacies of Thermo-msf-parser and ProCon. Thermomsf-parser does not convert MSF files to mzidentml. ProCon converts MSF but its converted file could not be integrated into IDPicker. In addition to being able to parse MSF files to mzidentml format files, M2Lite is equally able to convert MSF files to

4 the ISB (Institute for Systems Biology) defined pepxml [16] and regular tabs-delimited file formats. M2Lite can convert multiple MSF files in a batch to respective mzidentml, pepxml or tabs-delimited file formats as opposed to Thermo-msf-parser [9] or ProCon. Tool Outputs mzidentml TABLE 2 COMPARING M2LITE TO OTHER TOOLS mzidentml integration* Batch processes Outputs pepxml Outputs PSM table M2Lite Yes Yes Yes Yes Yes Thermo-msf-parser No Not applicable No No Yes ProCon Yes No No No No * Does generated mzidentml file integrate into IDPicker? M2Lite combines the effectiveness of two open-source programing languages, Java and R, to improve its efficiency. In developing M2Lite, that JDBC s efficiency is significantly diminished with complex database queries was hypothesized. And that multiple runtime object instances compromise efficiency was also speculated. Moreover, since database query results are returned as tables, that R program s highly optimized write.table() function combined with Java s highly efficient IO (Input-Output) classes would significantly improve file conversion was hypothesized. Therefore, M2Lite was implemented to utilize optimized SQL (Structured Query Language) scripts to retrieve stored information with minimal database calls. M2Lite initiates in Java. For very large and complicated database queries, M2Lite delegates its SQL calls to R which utilizes functions implemented in the RSQLite package [17] to assemble and retrieve database information of interest. For small to medium sized database queries, M2Lite utilizes the Xerial SQLite JDBC (Java Database Connectivity) library [18]. To minimize runtime memory footprint and facilitate inter-program communication, database table objects retrieved in the R program are outputted to a runtime initiated swap directory as shown in Fig. 2. The Java component of M2Lite reads these and the within-java-program-retrieved tables into related Java objects which are subsequently outputted in the mzidentml format as shown in Fig. 1. Fig. 2 M2Lite s component diagram. M2Lite consists of two major program components the Java program and the R program components. The Java program makes calls to the R program which outputs its results to a runtime initiated swap directory from which the Java program subsequently retrieves inputs for subsequent processing. To demonstrate M2Lite s reliability that it accurately represents Proteome Discoverer s results, the values reported for identified peptide spectrum matches (PSMs) in the M2Lite generated mzidentml file was compared with those reported by Proteome Discoverer s tab-delimited exported file. When the correlations was computed, a perfect correlation of 1 for m/z (massto-charge) and Scan Order values between mzidentml and tab-delimited file from an MSF file as shown in Fig. 3 was observed. However, a correlation of for compared instances of reported XCorr values was observed. This is because M2Lite reports the exact values stored in the MSF file while Proteome Discoverer reports approximated XCorr values

5 M2Lite retrieved value Proteome Discoverer exported value Fig. 3 Verification of spectral values from M2Lite derived mzidentml. Scatterplots show the relationship between M2Lite derived mzidentml identified PSM values and Proteome Discoverer exported PSM values. The blue lines indicate median values in respective M2Lite and Proteome Discoverer instances. The reported correlation indicates that M2Lite perfectly represents proteome discoverer s identification results. To compare the peptide assignments from Proteome Discoverer with that of MyriMatch [14] and to demonstrate M2Lite s ability to fulfill the goal of making downstream analyses independent of vendor tool, the M2Lite derived mzidentml file together with MyriMatch derived mzidentml file into IDPicker [15] as shown in Figs. 1 and 4 was successfully imported

6 Fig. 4 Integrating M2Lite derived mzidentml file with MyriMatch derived file in downstream analyses tool - IDPicker. Snapshot of IDPicker assembly image shows Protein, Spectrum and Filter History views. Other views include Peptide, Modification, and Analysis View. Summary of respective search engine identifications can be seen in the Spectrum view window. At filtering Q-value of 0.02, 1781 unique peptides were identified in common, 337 unique peptides were identified only by MyriMatch and 206 unique peptides were identified only by Proteome Discoverer shown in Fig. 5. Fig. 5 Proteome Discoverer versus MyriMatch peptide identifications. Venn diagram shows the peptides identified in common between Proteome Discoverer and MyriMatch. Significant overlap is observed. To ascertain that M2Lite appropriately assigns native spectra ID, the averaged itraq reporter ion ratios for identified peptides in common between our M2Lite derived mzidentml and those of the MyriMatch derived mzidentml file was compared. For each reported spectra ID in the mzidentml files, the corresponding reporter ion values were retrieved from the parent.raw converted mzml file. Correlations ranging between 0.92 and 0.93 as shown in Fig. 6 were observed

7 MyriMatch reporter ion ratio M2Lite (Proteome Discoverer) reporter ion ratio Fig. 6 Quantitative analysis using M2Lite derived reporter ion values. Scatterplots show relationship between averaged itraq reporter ion intensity (117:114, 116:114, 115:114) ratios of peptides found in common in Proteome Discoverer (M2Lite derived mzidentml) and MyriMatch. To validate M2Lite s better efficiency at conversion, the time cost for converting an MSF file with M2Lite against that of ProCon as shown in Table 3 and Fig. 7 was compared. TABLE 3 COMPARING M2LITE S SPEED OF CONVERSION TO PROCON S Tool Input File Size Conversion Time (min) M2Lite 232, 280 Kilobytes ProCon version , 280 Kilobytes ProCon version , 280 Kilobytes

8 Conversion Time (min) Time in minutes M2Lite ProCon version Software ProCon version Fig. 7 M2Lite versus ProCon conversion speed M2Lite converts significantly faster. Although there is an observed improvement in file conversion with the newer version of ProCon (version versus version ), its performance still remains incomparable to M2Lite. It is important to note that ProCon is still being developed and a stable alpha version is anticipated to have some better improvement than its current versions. ProCon by default accesses MSF s SQLite database using class objects defined by JDBC. It is possible that JDBC s inadequacies might be compromising its performance. M2Lite averted this possible limitation by delegating its complex queries to R. To evaluate M2Lite s performance, the total time cost over ten iterations of file conversion was estimated. Performance on two separate.msf file sizes were computed as shown in Table 4 and Fig. 8. A direct relationship is observed between file size and conversion time. Also, there is a linear relationship between total conversion time and number of iterations. Although minimal, M2Lite appears stable with increasing computation demand. TABLE 4 M2LITE S PERFORMANCE (TIME COST IN MILLISECONDS) PER FILE SIZE Iteration(s) 232, 280 Kilobytes 120, 412 Kilobytes

9 232, 280 Kilobytes 120, 412 Kilobytes Total time expended (milliseconds) Number of Iteration(s) Fig. 8 M2Lite conversion total time cost per file size M2Lite s source code is available as open-source at and its compiled binaries and documentation can be freely downloaded at These are made available under the BSD 3-Clause open source license. ACKNOWLEDGEMENT This work was supported by the National Institutes of Health, National Cancer Institute, Clinical Proteomic Tumor Analysis Consortium (CPTAC, U24CA160036) and the Early Detection Research Network (EDRN, U01CA152813), National Heart, Lung, and Blood Institute, Programs of Excellence in Glycosciences (PEG, P01HL107153). The authors acknowledge the feedbacks from David Tabb and Matt Chambers of the Tabb Lab, Vanderbilt University Medical Center Nashville, TN The authors have declared no financial/commercial conflict of interest. REFERENCES [1] Y. Zhang, B. R. Fonslow, B. Shan, M. Baek and J. R. Yates III, Protein analysis by shotgun/bottom-up proteomics, Chem Rev., vol. 113(4), p. 2343, [2] A. R. Jones, M. Eisenacher, G. Mayer, et al., The mzidentml data standard for mass spectrometry-based proteomics results, Molecular & Cellular Proteomics, vol. 11(7), [3] G. Mayer, L.Montecchi-Palazzi, D. Ovelleiro, et al., The HUPO proteomics standards initiative- mass spectrometry controlled vocabulary, Database (Oxford), [4] J. K. Eng, A. L. McCormack and J. R. Yates Iii, An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database, J Am Soc Mass Spectrom, vol. 5(11), pp , [5] D. N. Perkins, D. J. Pappin, D. M. Creasy and J. S. Cottrell, Probability-based protein identification by searching sequence databases using mass spectrometry data, Electrophoresis, vol. 20(18), p. 3551, [6] Thermo fisher scientific incorporated. Accessed 04/23, [7] Market reports online. Accessed 04/23, [8] Persistence market research. Accessed 04/23, [9] N. Colaert, H. Barsnes, M. Vaudel, et al., Thermo-msf-parser: An open source java library to parse and visualize thermo proteome discoverer msf files, Journal of Proteome Research, vol. 10(8), p. 3840, [10] Proteomics conversion tool (ProCon). Accessed 04/23, [11] E. Deutsch, mzml: a single, unifying data format for mass spectrometer output, Proteomics. vol. 8(14), pp , [12] L. Martens, M. Chambers, M. Sturm, et al., mzml--a community standard for mass spectrometry data, Mol Cell Proteomics, vol. 10(1), [13] D. Kessner, M. Chambers, R. Burke, D. Agus and P. Mallick, ProteoWizard: open source software for rapid proteomics tools development,

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