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Portland, OR, United States

Jones A.R.,University of Liverpool | Eisenacher M.,Ruhr University Bochum | Mayer G.,Ruhr University Bochum | Kohlbacher O.,University of Tubingen | And 16 more authors.
Molecular and Cellular Proteomics | Year: 2012

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc. Source


Seymour S.L.,AB SCIEX | Farrah T.,Institute for Systems Biology | Binz P.-A.,Swiss Institute of Bioinformatics | Binz P.-A.,University of Lausanne | And 12 more authors.
Proteomics | Year: 2014

Inferring which protein species have been detected in bottom-up proteomics experiments has been a challenging problem for which solutions have been maturing over the past decade. While many inference approaches now function well in isolation, comparing and reconciling the results generated across different tools remains difficult. It presently stands as one of the greatest barriers in collaborative efforts such as the Human Proteome Project and public repositories such as the PRoteomics IDEntifications (PRIDE) database. Here we present a framework for reporting protein identifications that seeks to improve capabilities for comparing results generated by different inference tools. This framework standardizes the terminology for describing protein identification results, associated with the HUPO-Proteomics Standards Initiative (PSI) mzIdentML standard, while still allowing for differing methodologies to reach that final state. It is proposed that developers of software for reporting identification results will adopt this terminology in their outputs. While the new terminology does not require any changes to the core mzIdentML model, it represents a significant change in practice, and, as such, the rules will be released via a new version of the mzIdentML specification (version 1.2) so that consumers of files are able to determine whether the new guidelines have been adopted by export software. © 2014 WILEY-VCH Verlag GmbH & Co. Source


Cologna S.M.,U.S. National Institutes of Health | Crutchfield C.A.,U.S. National Institutes of Health | Searle B.C.,Proteome Software Inc | Blank P.S.,U.S. National Institutes of Health | And 7 more authors.
Journal of Proteome Research | Year: 2015

Protein quantification, identification, and abundance determination are important aspects of proteome characterization and are crucial in understanding biological mechanisms and human diseases. Different strategies are available to quantify proteins using mass spectrometric detection, and most are performed at the peptide level and include both targeted and untargeted methodologies. Discovery-based or untargeted approaches oftentimes use covalent tagging strategies (i.e., iTRAQ, TMT), where reporter ion signals collected in the tandem MS experiment are used for quantification. Herein we investigate the behavior of the iTRAQ 8-plex chemistry using MALDI-TOF/TOF instrumentation. The experimental design and data analysis approach described is simple and straightforward, which allows researchers to optimize data collection and proper analysis within a laboratory. iTRAQ reporter ion signals were normalized within each spectrum to remove peptide biases. An advantage of this approach is that missing reporter ion values can be accepted for purposes of protein identification and quantification without the need for ANOVA analysis. We investigate the distribution of reporter ion peak areas in an equimolar system and a mock biological system and provide recommendations for establishing fold-change cutoff values at the peptide level for iTRAQ data sets. These data provide a unique data set available to the community for informatics training and analysis. © 2015 American Chemical Society. Source


Searle B.C.,University of Washington | Searle B.C.,Proteome Software Inc | Egertson J.D.,University of Washington | Bollinger J.G.,University of Washington | And 2 more authors.
Molecular and Cellular Proteomics | Year: 2015

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40- 85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc. Source

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