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Deutsch E.W.,Institute for Systems Biology | Chambers M.,Vanderbilt University | Neumann S.,Leibniz Institute of Plant Biochemistry | Levander F.,Lund University | And 15 more authors.
Molecular and Cellular Proteomics | Year: 2012

Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc. Source


Schmidt A.,ETH Zurich | Schmidt A.,Center for Systems Physiology and Metabolic Diseases | Schmidt A.,University of Basel | Beck M.,ETH Zurich | And 8 more authors.
Molecular Systems Biology | Year: 2011

Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre-determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC-MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date. © 2011 EMBO and Macmillan Publishers Limited. Source


Deutsch E.W.,Institute for Systems Biology | Mendoza L.,Institute for Systems Biology | Shteynberg D.,Institute for Systems Biology | Farrah T.,Institute for Systems Biology | And 13 more authors.
Proteomics | Year: 2010

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge. Source


Claassen M.,ETH Zurich | Claassen M.,Center for Systems Physiology and Metabolic Diseases | Aebersold R.,ETH Zurich | Buhmann J.M.,ETH Zurich
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Comprehensive characterization of a proteome defines a fundamental goal in proteomics. In order to maximize proteome coverage for a complex protein mixture, i.e. to identify as many proteins as possible, various different fractionation experiments are typically performed and the individual fractions are subjected to mass spectrometric analysis. The resulting data are integrated into large and heterogeneous datasets. Proteome coverage prediction refers to the task of extrapolating the number of protein discoveries by future measurements conditioned on a sequence of already performed measurements. Proteome coverage prediction at an early stage enables experimentalists to design and plan efficient proteomics studies. To date, there does not exist any method that reliably predicts proteome coverage from integrated datasets. We present a generalized hierarchical Pitman-Yor process model that explicitly captures the redundancy within integrated datasets. We assess the proteome coverage prediction accuracy of our approach applied to an integrated proteomics dataset for the bacterium L. interrogans and we demonstrate that it outperforms ad hoc extrapolation methods and prediction methods designed for non-integrated datasets. Furthermore, we estimate the maximally achievable proteome coverage for the experimental setup underlying the L. interrogans dataset. We discuss the implications of our results to determine rational stop criteria and their influence on the design of efficient and reliable proteomics studies. © Springer-Verlag Berlin Heidelberg 2010. Source


Shteynberg D.,Institute for Systems Biology | Deutsch E.W.,Institute for Systems Biology | Lam H.,Hong Kong University of Science and Technology | Eng J.K.,University of Washington | And 8 more authors.
Molecular and Cellular Proteomics | Year: 2011

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline , it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organismspecific composite data sets. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc. Source

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