Andreas M. Boehm

Precise protein quantification based on peptide quantification using iTRAQ

BackgroundMass spectrometry based quantification of peptides can be performed using the iTRAQ™ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level.ResultsA method comprising sound error estimation and statistical methods is presented that allows precise abundance analysis plus error calculation at the peptide as well as at the protein level. This yields the relevant information that is required for quantitative proteomics. Comparing the performance of our method named Quant with existing approaches the error estimation is reliable and offers information for precise bioinformatic models. Quant is shown to generate results that are consistent with those produced by ProQuant™, thus validating both systems. Moreover, the results are consistent with that of Mascot™ 2.2. The MATLAB® scripts of Quant are freely available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/, each under the GNU Lesser General Public License.ConclusionThe software Quant demonstrates improvements in protein quantification using iTRAQ™. Precise quantification data can be obtained at the protein level when using error propagation and adequate visualization. Quant integrates both and additionally provides the possibility to obtain more reliable results by calculation of wise quality measures. Peak area integration has been replaced by sum of intensities, yielding more reliable quantification results. Additionally, Quant allows the combination of quantitative information obtained by iTRAQ™ with peptide and protein identifications from popular tandem MS identification tools. Hence Quant is a useful tool for the proteomics community and may help improving analysis of proteomic experimental data. In addition, we have shown that a lognormal distribution fits the data of mass spectrometry based relative peptide quantification.

Extractor for ESI quadrupole TOF tandem MS data enabled for high throughput batch processing

BackgroundMass spectrometry based proteomics result in huge amounts of data that has to be processed in real time in order to efficiently feed identification algorithms and to easily integrate in automated environments. We present wiff2dta, a tool created to convert MS/MS data obtained using Applied Biosystems QStar and QTrap 2000 and 4000 series.ResultsComparing the performance of wiff2dta with the standard tools, we find wiff2dta being the fastest solution for extracting spectrum data from ABIs raw file format. wiff2dta is at least 10% faster than the standard tools. It is also capable of batch processing and can be easily integrated in high throughput environments. The program is freely available via http://www.protein-ms.de, http://sourceforge.net/projects/protms/ and is also available from Applied Biosystems.Conclusionswiff2dta offers the possibility to run as stand-alone application or within a batch process as command-line tool integrated in automation and high-throughput environments. It is more efficient than the state-of-the-art tools provided.

iTRAQ analysis of a cell culture model for malignant transformation, including comparison with 2D-PAGE and SILAC.

To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-RasV12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2D-PAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are up-regulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis.

jTraqX: A free, platform independent tool for isobaric tag quantitation at the protein level

Many proteomic studies focus on quantitative aspects, using different stable isotope labeling techniques that require specialized software to analyze the generated data. Here we present jTraqX, an easy‐to‐use tool for processing and visualizing protein quantification data. jTraqX is platform independent and is compatible with all available 4‐plex isobaric tags. jTraqX can be freely downloaded at http://sourceforge.net/projects/protms.

Efficient analysis and extraction of MS/MS result data from Mascot™ result files

BackgroundMascot™ is a commonly used protein identification program for MS as well as for tandem MS data. When analyzing huge shotgun proteomics datasets with Mascot™s native tools, limits of computing resources are easily reached. Up to now no application has been available as open source that is capable of converting the full content of Mascot™ result files from the original MIME format into a database-compatible tabular format, allowing direct import into database management systems and efficient handling of huge datasets analyzed by Mascot™.ResultsA program called mres2x is presented, which reads Mascot™ result files, analyzes them and extracts either selected or all information in order to store it in a single file or multiple files in formats which are easier to handle downstream of Mascot™. It generates different output formats. The output of mres2x in tab format is especially designed for direct high-performance import into relational database management systems using native tools of these systems. Having the data available in database management systems allows complex queries and extensive analysis. In addition, the original peak lists can be extracted in DTA format suitable for protein identification using the Sequest™ program, and the Mascot™ files can be split, preserving the original data format. During conversion, several consistency checks are performed. mres2x is designed to provide high throughput processing combined with the possibility to be driven by other computer programs. The source code including supplement material and precompiled binaries is available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/.ConclusionThe database upload allows regrouping of the MS/MS results using a database management system and complex analyzing queries using SQL without the need to run new Mascot™ searches when changing grouping parameters.

Squash: A Tool for Analyzing, Tuning and Refactoring Relational Database Applications

The performance of a large biological application of relational databases highly depends on the quality of the database schema design, the resulting structure of the tables, and the logical relations between them. We have developed a tool named Squash ( Sql Query Analyzer and Schema EnHancer) for visualizing, analyzing and refactoring database applications.Squash parses the Sql definition of the data-base schema and the queries into an Xml representation called Squash ml , which is then processed in Swi --- Prolog and the integrated Xml query and transformation language Fn Query. Squash comes with a set of predefined methods for tuning the database application according to the load profile, and with methods for proposing refactorings, such as index creation, partitioning, splitting, or further normalization of the database schema. Sql statements are adapted simultaneously upon modification of the schema. Moreover, the declarative Squash framework can be flexibly extended by user---defined methods .

Command line tool for calculating theoretical MS spectra for given sequences

UNLABELLED Scientists usually want to verify the ion matching process of algorithms that look up peptide sequences in DNA or protein databases. The verification step is often done numerically or visually. Not all search algorithms present the appropriate theoretical spectrum information within their results. Thus, the theoretical spectrum for each result should be calculated from the sequence of the matched peptide. We present an operating-system-independent command line tool for this purpose that can be integrated easily into complex as well as existing environments, and can be used to present the theoretical spectrum to the user in either graphical or tabular format by third party products. AVAILABILITY The code is available via the website http://www.protein-ms.de

JSquash: source code analysis of embedded database applications for determining SQL statements

In this paper, we analyse Java source code of embedded database applications by means of static code analysis. If the underlying database schema of such an application is subject to refactoring or database tuning, then the SQL statements in the embedding Java program need to be adapted correspondingly. This should be done mostly automatically, since changing software manually is error-prone and time consuming. For determining the SQL statements that access the database, we can either look at the database logfile, an audit file, or at the Java source code itself. Here, we show how to derive the strings of dynamic SQL statements directly from the Java source code. We do this without using a debugger or a virtual machine technique; instead, we trace the values of variables that contribute to a query string backwards to predict the values of contributing program variables as precisely as possible. We use PROLOGs declarative features and its backtracking mechanism for code analysis, refactoring, and tuning.