In the present study, a database concept has been developed to address these issues and to offer complete information via a web interface. In our concept, the Oracle based data repository system SQL-LIMS plays the central role in the proteomics workflow and was applied to the proteomes of Mycobacterium tuberculosis, Helicobacter pylori, Salmonella typhimurium and protein complexes such as 20S proteasome. Technical operations of our proteomics labs were used as the standard for SQL-LIMS template creation. By means of a Java based data parser, post-processed data of different approaches, such as LC/ESI-MS, MALDI-MS and 2-D gel electrophoresis (2-DE), were stored in SQL-LIMS. A minimum set of the proteomics data were transferred in our public 2D-PAGE database using a Java based interface (Data Transfer Tool) with the requirements of the PEDRo standardization. Furthermore, the stored proteomics data were extractable out of SQL-LIMS via XML.

The requirements for data storing in SQL*LIMS™ depend on the experimental workflow. As a result, the data management system must contain specifically designed features (Figure (Figure2).2). In order to structure different experiments, SQL*LIMS™ allowed to define studies. During the study initialization, only predefined attributes must be stored to save information about the frame and the goal of the experiment. For sample preparation, flexibility was also very important to track experimental workflows which often included a complex sequence of operations. To meet these requirements, predefined basic sample types were combined in a hierarchical parent-child relationship tree and new attributes can be added to the predefined types. The following protein separation step required a full integration with 2-DE gel image analysis tools (e.g. Topspot, and PDQuest). Data of detected 2-DE spots or 1-DE bands were automatically acquired (uploaded) from image processing tool output files along with gel image pictures. In this case, structured data with fixed formats, such as spot coordinates, intensities or spot volumes, come together with unstructured raw data which have no common format, such as gel images and native report files. The information was spread into all these different records in the storage system, but it was available for the user as a single bulk through a gel viewer widget that allowed the drilling of spot information. In the MS analysis step, both structured and unstructured data must be managed as in the protein separation step. In addition, features for the direct exchange of real-time bi-directional data with MS instruments were provided for the work lists uploading and the peak lists downloading. A more complex strategy for unstructured data management was required due to the massive amounts of raw data with proprietary format. Instead of passively storing all the data in the SQL*LIMS™ database without any chance to extract the content for data searching, raw data files were saved into the storage server and/or the permanent storage supports. The locations can then be tracked into the SQL*LIMS™. For protein identification, MS peak lists were submitted to search database engines such as Mascot, MS-Fit, MS-Tag, and SEQUEST. Queries can be performed directly from SQL*LIMS™ or by using the search engine front-end interface. In both cases, protein identification results were stored in the SQL*LIMS™ database. Again, the need for managing unstructured electronic records was coped. The stored gel information and protein identification results of proteomics studies can now easily be reported by the Report Builder, printed out or sent as an e-mail.

In order to share the experimental results with other laboratories rather easily, the DTT was designed to facilitate the transfer out of the SQL*LIMS™ into the proteome database system (Figure (Figure3).3). The DTT provides a GUI to enable the user to select datasets to be transferred. First, only necessary data records were selected among the vast amount of data stored in the SQL*LIMS™. The DTT displayed the gel data from SQL*LIMS™ corresponding to the identification numbers in the 2-DE database (Figure (Figure3B).3B). The user can choose a gel and the relevant data for the displayed spots. This included for example the sequence coverage, score, rank and molecular weight of identified proteins for each spot on the gel in question (Figure (Figure3C).3C). It is also possible to check newer entries for a spot in the 2-DE database which will be updated in the transfer process. A release number was assigned to selected protein identification data for the transfer. These release numbers can be used to control the degree of accessibility of the data in the 2-DE database. Thus, it was possible to restrict the view for certain release numbers. The usage of the DTT was password protected and all data transactions were logged. If the button "Transfer" was selected and "Save" was pressed, an existing release number was selected for this spot or assigned to a new one. The concept of the release numbers was established in order to control public access and to inform SQL*LIMS™ managers if a new protein could be identified due to new batch searches using the newest database releases. Currently, only data displayed in Figure Figure3C3C can be transferred but it is planned to include MS-data extracted from the ms peak SQL*LIMS™ table (Table 1).

Tandem mass spectrometry has been particularly useful for determining the protein components of complex mixtures. The following strategy was applied to evaluate LC/ESI-MS/MS peak lists data: MS/MS spectra were automatically transformed into peak lists (.dta-files) by SEQUEST and subsequently imported into MS-Screener for generating data matrices. The binary matrices were subjected to hierarchical agglomerative cluster analyses performed by means of the hclust- function within R. To illustrate an example of cluster analyses, Figure Figure44 depicts a result of a dataset comprising 873 MS/MS peak lists. The spectra were derived from a comparative analysis of two rat liver proteasome subtypes [15] measured by ICAT/LC/ESI-MS/MS. The cluster analysis was applied to determine which spectra showed some degree of similarity, i.e. common peptide or experiment specific mass peaks. The analysis resulted in a dendrogram of mass spectra with high similarity formed branches. The subset of spectra framed in Figure Figure44 shared numerous polymer contaminant masses, caused by avidin chromatography which was used for the separation of ICAT labeled peptides. As a result, more than half of the 125 polymer mass spectra have been falsely fitted to a rat specific peptide and the Mascot MS/MS ion search result shown in Figure Figure44 represents a false positive match of a MS/MS spectrum. After removing the polymers containing mass spectra from the data set, the remaining 748 of 873 mass spectra were searched again. Subsequently, only the spectra of relevance and their SEQUEST or Mascot identification results were stored in SQL*LIMS™. The strategy outlined in this section demonstrated the capability to clean peak list data sets from contaminants in order to improve the reliability of identifications and to reduce the number of stored data.

High-throughput MALDI-MS PMF was performed as follows: Spots of interest were excised from 2-DE gels, transferred into 96-well microtiter plates, and digested with trypsin using a spot-cutter (Proteome Works, Bio-Rad, Hercules, CA, USA). Subsequently, equal volume of resulting peptides and α-cyano-4-hydroxycinnamic acid (CHCA) were mixed and spotted onto MALDI templates by the Ettan spot-handling workstation (Amersham Biosciences, Uppsala, Schweden). Subsequently, MALDI spectra were internally calibrated and the resulting peak lists exported using the "Peak-to-Mascot" script of the 4700 Explorer software (Version 2.0) (Applied Biosystems, Foster City, USA). The parameters applied for this process were optimized (signal-to-noise ratio, mass range, peak density, etc.). Afterwards, the MS-Screener program was used to determine and to remove common contaminant masses.

The program MS-Screener (Version 1.0.1) was applied to evaluate large datasets of peak lists. This program comprised 162 Java classes and has been developed for Java 2 Runtime Environment (Version J2RE 1.4.1; http://java.sun.com/). MS-Screener offers a multi-platform support for Linux, Solaris and Microsoft Windows including a helpful graphical user interface (GUI). Graphical representations of peak lists as plot-views have been integrated using the JFreeChart class library (Version 0.9.13) http://www.jfree.org/jfreechart/index.html published under the GNU Lesser General Public License. MS-Screener facilitates the import and export of ASCII files (.pkm (GRAMS, Applied Biosystems, Framingham, USA), .pkt (Data Explorer, Applied Biosystems, Framingham, USA), .txt (Peak-to-Mascot, 4700 Explorer, Applied Biosystems, Framingham, USA) and .dta (SEQUEST, Thermo Finnigan, San Jose, USA)) and data exchange via other interfaces. MS-Screener was used for many tasks, e.g. the detection of common mass peaks, the elimination of contaminant masses, and the calculation of the half decimal places rule [14]. Furthermore, it was used to generate peak lists matrices as a prerequisite for cluster analyses using R. Moreover, the recalibration of binary peak lists and a peak pair comparison tool to determine ICAT ratios were applied. The MS-Screener results were transformed in tab-separated files (.txt) to transfer the data into SQL*LIMS™.

For protein identifications, 2-DE spots were analyzed by MALDI-MS or MS/MS or ESI-MS/MS [16,18-20]. In most cases, spots to be identified were digested by trypsin prior to MS analysis [21]. MALDI-MS was carried out using a Voyager Elite MALDI-TOF mass spectrometer or a 4700 Proteomics Analyzer MALDI-TOF/TOF (both from Applied Biosystems, Framingham, USA). Protein identifications were achieved by database comparisons using search algorithms such as Mascot [22] or MS-FIT http://prospector.ucsf.edu, whereby Mascot was available as in-house version. Searches were accomplished either individually or in batch mode (analysis of large datasets). In the latter case, Mascot-Daemon http://www.matrixscience.com was used as batch interface. Individual searches were performed by the Mascot web-front end or the SQL-LIMS™ clients, respectively, and both were connected with in-house Mascot server. The search parameters applied have previously been described [21]. Moreover, proteins were separated and identified by large-scale on-line LC/ESI-MS/MS. The protein samples were prepared as described [23] and measured by LCQ ion trap mass spectrometer (Thermo Finnigan, San Jose, USA). For peptide identifications, the generated MS/MS spectra were evaluated using the SEQUEST analysis program and/or Mascot. In order to quantify differences between 20S proteasome subtypes [15,24] and proteomes of M. tuberculosis and bovis BCG [23], proteins were labelled with the ICAT reagent and analyzed by LC/ESI-MS/MS. To calculate the relative ratios, MS-spectra were evaluated by the program Xpress. Furthermore, a complementary approach was used to detect differences in protein abundance, which combines ICAT and 2-DE and were quantified by the program MS-Screener [24]. An iterative search procedure was applied for in-depth analysis of large 2-DE/MALDI-MS datasets [14].

The workflow described above requires a suitable system for the integration and management of raw and processed experimental data. These issues were addressed by the Laboratory Information Management System (LIMS) in combination with an implemented SQL*LIMS™ Proteomics Solution, customized for our proteomics research laboratory. The implemented solution was based on the Applied Biosystems™ product suite for life science, including a core application (SQL*LIMS™). The latter was designed for analytical laboratories, Pharma R&D and manufacturing environments. Furthermore, components specifically designed for microtiter plates (SQL*GT™) and proteomics (Proteomics Solution) data management were implemented. Operating flexibility and extensibility of this solution has minimized the requirement for code customization. The SQL*LIMS™ users are allowed to enter new or to amend existing workflows and to open interfaces providing an add-on and built-in mechanism for the integration of MS instruments and third-party tools. A highly integrated environment has been addressed from the very beginning as a key factor to enhance productivity by streamlining time consuming operations such as MS data exchange (work list uploading and peak list downloading) or protein search engines querying.