In the talk we describe the freely available software library OpenMS which is 
currently under development at the Freie UniversitÃ¤t Berlin and the 
Eberhardt-Karls UniversitÃ¤t TÃ¼bingen. We give an overview of the goals and 
problems in differential proteomics with HPLC and then describe in detail the 
implemented approaches for signal processing, peak detection and data 
reduction currently employed in OpenMS. After this we describe methods to 
identify the differential expression of peptides and propose strategies to avoid MS/MS identification of peptides of interest.  We give an overview of the
capabilities and design principles of OpenMS and demonstrate its ease of use. 
Finally we describe projects in which OpenMS will be or was already deployed 
and thereby demonstrate its versatility

Kohlbacher, Oliver

Lange, Eva

Pfeifer, Nico

Reinert, Knut

Schulz-Trieglaff, Ole

Sturm, Marc

Dagstuhl Research Online Publication Server

OpenMS – A Framework for QuantitativeHPLC/MS-Based ProteomicsKnut Reinert1, Oliver Kohlbacher2,Clemens Gro¨pl1, Eva Lange1, OleSchulz-Trieglaff1,Marc Sturm2 and Nico Pfeifer21 Algorithmische Bioinformatik, Fachbereich Mathematik und Informatik, FU Berlin,Takustr. 9, 14195 Berlin, Germany,reinert@inf.fu-berlin.de2 Simulation biologischer Systeme, WSI/ZBIT,Eberhard-Karls-Universita¨t Tu¨bingen, Sand 14, 72076 Tu¨bingen, Germany,oliver.kohlbacher@uni-tuebingen.deAbstract. One of the main goals of proteomics research is the discov-ery of novel diagnostic markers and therapeutic targets. Currently, massspectrometry is the main platform for analyzing complex protein sam-ples. Lately, HPLC/MS-based approaches have gained considerable inter-est due to their larger potential for full automation when compared togel-based techniques. Particularly, multi-dimensional HPLC-MS meth-ods have a great potential as a platform for differential quantification ofproteins in complex mixtures. However, computational methods to ana-lyze these automated analyses at a large scale are yet to be developed.The development of these methods should encompass new methods fordata reduction, data interpretation, data management and visualization.We propose an algorithmic framework for a fully automated differen-tial analysis of HPLC/MS samples, which goes beyond the currentlyestablished pairwise comparison of samples towards a statistically soundanalysis of larger sample numbers. In this short paper we outline theframework in its current state and lay out future plans.Keywords. computational proteomics, quantitative analysis, C++, soft-ware framework, mass spectrometry1 IntroductionHigh-performance liquid chromatography (HPLC) prior to mass-spetrometric(MS) analysis has become a standard technique for high-throughput proteomics[1]. Complex peptide or protein samples can be analyzed, and in combinationwith labeling techniques and MS-based protein identification, differential studiescan be carried out effectively. The data processing pipeline for high throughputanalysis of proteins using mass spectrometry requires efficient algorithms to ex-tract the information of interest from a large volume of data (see Fig. 1 for anexample of quantitative data obtained from a mass spectrometer).There are other ideas of software frameworks for computational proteomics.Closest to our ideas is the Trans-Proteomic Pipeline (TPP) [2]. The TPP makesDagstuhl Seminar Proceedings 05471Computational Proteomicshttp://drops.dagstuhl.de/opus/volltexte/2006/5462 Knut ReinertFig. 1. Part of a raw map. The data is separated in retention time and massover charge.use of open XML file formats for storage of data at the raw spectral data,peptide, and protein levels. The TPP integrates other tools developed at theISB into a coherent framework. Among these tools are PeptideProphet [3] whichvalidates peptides assigned to MS/MS spectra, XPRESS [4] and ASAPRatio [5]that quantify peptides and proteins in differentially labelled samples, Pep3D [6]enables a view of the raw spectral data, and ProteinProphet [7] infers sampleproteins.OpenMS is an open source software framework implemented in C++. It pro-vides effective data structures and algorithms for the analysis of multi-dimensionalHPLC-MS data. Data processing is based on a hierarchical concept where eachstep reduces the amount of data by at least one order of magnitude. However, areference to the data of earlier processing steps is kept for use in following steps.Two main data reduction steps are already implemented. The first is an en-hanced wavelet-based peak picking algorithm, which includes baseline reductionand noise removal. The second is the aggregation of peaks into more complex fea-tures, e.g. isotopic patterns or collections of charge variants. The large amountsof data are managed by a database system. For import of mzData, the HUPOPSI standard [8], and various import/export formats are supported. Additionalapplications such as a data viewer and a workflow system for the analysis pipelineare under development. The framework has proven its usefulness for proteomicsanalyses of biomarkers in complex matrices in exemplary studies [9,10]. In thefollowing we will first introduce the main components of OpenMS and close witha brief overview of ongoing work.05731 Computational Proteomics 32 Components of OpenMS2.1 Peak PickingSignal processing includes baseline reduction, noise removal, and peak picking.For baseline filtering, we apply morphological methods and wavelet-based algo-rithms for smoothing. For peak picking, raw data points that belong to a peakhave to be detected, and important features like their centroid position, heightand area have to be determined (see Fig. 2 for an illustration of the peak pickingprocess). To find the centroid positions we use the continuous wavelet transfor-mation adapting the wavelet to the theoretical peak shape. The algorithm allowsthe clear separation even of largely overlapping peaks. The parameters of thebest fitting asymmetric Lorentzian or hyperbolic-secant-squared functions yieldvaluable information about the original peak shape for further analysis. For moredetails see [11].Fig. 2. Converting raw data into sticks.4 Knut Reinert2.2 Feature FindingFeature Finding is a central concept of the OpenMS approach. We identify allpeaks belonging to the same chemical species (for instance a peptide chargevariant) in a multi-dimensional map, e.g. parts of several MS scans at differentretention times. The data may consist of picked peaks or profile data. Sophis-ticated and efficient algorithms are then used to extract peptide or protein in-formation from the LC/MS data by collecting isotopic patterns, elution profiles,charge variants, etc.By analyzing the measured compounds we assign quality values to each di-mension of description of such a feature. They are used in the final statisticalanalysis as well as to evaluate the overall quality of LC/MS measurements. Thegoal here to be as generic as possible, allowing us to formulate many biochemi-cal questions as multi-dimensional search problems and solve them using similaralgorithms and data structures. Fig. 3 shows the fit of a two-dimensional dis-tribution to the measured data. A detailed description of the feature findingalgorithm has been published elsewhere [9].Fig. 3. Fit of the model to the raw data.05731 Computational Proteomics 52.3 Map MappingMap mapping is the process in which maps of peaks or features from differ-ent measurements are superimposed for comparison. The first step is to find atransformation that moves features from one map close to corresponding fea-tures in the other one. A standard geometric hashing approach is sufficient inmost cases; more complex (alignment-based) calibration schemes have also beenimplemented. In the second step we determine a combinatorial matching andextract groups of corresponding features for differential analysis. In our conceptwe can then track ambiguities and inconsistencies detected back to an earlierstage of the data reduction pipeline.Fig. 4. Screenshot of SpecView.2.4 ViewerThe MS data viewer SpecView comes with OpenMS and is an example for howclasses of the OpenMS framework can be used to build up powerful applications.SpecView can visualize 1D and 2D MS spectra both from files or a database. Therepresentation of the spectra is configurable and images can be saved or printeddirectly from the viewer. Fig. 4 shows two instances of SpecView. The one inthe front holds two 1D spectra. In the background a 2D spectrum is displayedwith a colored surface, contour lines and peaks drawn as colored dots.6 Knut Reinert2.5 Database, XML, WorkflowLarge-scale laboratories need their data to be annotated, analyzed and stored.The huge amount of data makes database support an essential feature of nearlyevery proteomics application. OpenMS comes with database support throughthe QT SQL module, which allows the use of a variety of relational and objectrelational databases (PostgreSQL, MySQL, DB2 and many more). The databasemodel of OpenMS is being adapted to the MIAPE and PSI-OM standard. Be-sides the database several file formats for loading and storing data persistentlyare supported. Examples of these formats are AndiMS, DTA, mzXML, Mascotresult files and Sequest result files. A workflow system for combining severalapplications into an analysis pipeline for MS data is in progress.3 Conlusion and OutlookWe have presented a C++ framework for the quantitative analysis of HPLC/MSdata which is still under very active development. Nevertheless, many compo-nents are already functional and can be used to easily develop powerfull analysisapplications. Our aim is to add more functionality to OpenMS and then con-duct various analyses on HPLC-MS data from fields such as diagnostics or sys-tems biology. OpenMS should allow the researchers to quickly prototype analysispipelines, allow for more complex experimental setups, and reduce the depen-dence on vendor-supplied analysis software.References1. McDonald, W., Yates, J.r.: Shotgun proteomics and biomarker discovery. Dis.Markers 18 (2002) 99–1052. Keller, A., Eng, J., Zhang, N., jun Li, X., Abersold, R.: A uniform proteomicsms/ms analysis platform utilizing open xml file formats. Molecular Systems Biology(2005)3. Keller, A., Nesvizhskii, A., Kolker, E., Aebersold, R.: Empirical statistical modelto estimate the accuracy of peptide identifications made by ms/ms and databasesearch. Anal Chem 74 (2002) 5383–53924. Han, D., Eng, J., Zhou, H., R, A.: Quantitative profiling of differentiation-inducedmicrosomal proteins using isotopecoded affinity tags and mass spectrometry. NatBiotechnology 19 (2001) 946–9515. Li, X.j., Zhang, H., Ranish, J., Aebersold, R.: Automated statistical analysis ofprotein abundance ratios from data generated by stable isotope dilution and tan-dem mass spectrometry. Anal Chem 75 (2003) 6648–66576. Li, X.j., Pedrioli, P., Eng, J., Martin, D., Yi, E., Lee, H., Aebersold, R.: A toolto visualize and evaluate data obtained by liquid chromatography/electrosprayionization/mass spectrometry. Anal Chem 76 (2004) 3856–38607. Nesvizhskii, A., Keller, A., Kolker, E., Aebersold, R.: A statistical model foridentifying proteins by tandem mass spectrometry. Anal Chem 75 (2003) 4646–465805731 Computational Proteomics 78. Orchard, S., Hermjakob, H., Julian, R.J., Runte, K., Sherman, D., Wojcik, J., Zhu,W., Apweiler, R.: Common interchange standards for proteomics data: Publicavailability of tools and schema. Proteomics 4 (2004) 490–4919. Gro¨pl, C., Lange, E., Reinert, K., Kohlbacher, O., Sturm, M., Huber, C., Mayr,B., Klein, C.: Algorithms for the automated absolute quantification of diagnosticmarkers in complex proteomics samples. In Berthold, M., Glen, R., Diederichs, K.,Kohlbacher, O., Fischer, I., eds.: Proceedings of the 1st Symposium on Computa-tional Life Sciences (CLS 2005). Volume 3695 of Lecture Notes in Bioinformatics(LNBI)., Springer (2005) 151–16110. Mayr, B.M., Kohlbacher, O., Reinert, K., Sturm, M., Gro¨pl, C., Lange, E., Klein,C., Huber, C.G.: Absolute myoglobin quantitation in serum by combining two-dimensional liquid chromatography-electrospray ionization mass spectrometry andnovel data analysis algorithms. J. Proteome Res. 5 (2006) 414–42111. Lange, E., Gro¨pl, C., Reinert, K., Kohlbacher, O., Hildebrandt, A.: High accuracypeak-picking of proteomics data using wavelet techniques. In: Proceedings of the11th Pacific Symposium on Biocomputing (PSB-06). (2006) 243–254

OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics

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OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics

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