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      OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

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          Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra.

          To take advantage of the potential quantitative benefits offered by tandem mass spectrometry, we have modified the method in which tandem mass spectrum data are acquired in 'shotgun' proteomic analyses. The proposed method is not data dependent and is based on the sequential isolation and fragmentation of precursor windows (of 10 m/z) within the ion trap until a desired mass range has been covered. We compared the quantitative figures of merit for this method to those for existing strategies by performing an analysis of the soluble fraction of whole-cell lysates from yeast metabolically labeled in vivo with (15)N. To automate this analysis, we modified software (RelEx) previously written in the Yates lab to generate chromatograms directly from tandem mass spectra. These chromatograms showed improvements in signal-to-noise ratio of approximately three- to fivefold over corresponding chromatograms generated from mass spectrometry scans. In addition, to demonstrate the utility of the data-independent acquisition strategy coupled with chromatogram reconstruction from tandem mass spectra, we measured protein expression levels in two developmental stages of Caenorhabditis elegans.
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            Using iRT, a normalized retention time for more targeted measurement of peptides.

            Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.
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              Is Open Access

              mzML—a Community Standard for Mass Spectrometry Data*

              Mass spectrometry is a fundamental tool for discovery and analysis in the life sciences. With the rapid advances in mass spectrometry technology and methods, it has become imperative to provide a standard output format for mass spectrometry data that will facilitate data sharing and analysis. Initially, the efforts to develop a standard format for mass spectrometry data resulted in multiple formats, each designed with a different underlying philosophy. To resolve the issues associated with having multiple formats, vendors, researchers, and software developers convened under the banner of the HUPO PSI to develop a single standard. The new data format incorporated many of the desirable technical attributes from the previous data formats, while adding a number of improvements, including features such as a controlled vocabulary with validation tools to ensure consistent usage of the format, improved support for selected reaction monitoring data, and immediately available implementations to facilitate rapid adoption by the community. The resulting standard data format, mzML, is a well tested open-source format for mass spectrometer output files that can be readily utilized by the community and easily adapted for incremental advances in mass spectrometry technology.
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                Author and article information

                Journal
                Nat. Biotechnol.
                Nature biotechnology
                1546-1696
                1087-0156
                Mar 2014
                : 32
                : 3
                Affiliations
                [1 ] 1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2] PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland. [3].
                [2 ] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
                [3 ] 1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2] Biognosys AG, Schlieren, Switzerland.
                [4 ] 1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2] PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland.
                [5 ] SyBIT project of SystemsX.ch, ETH Zurich, Zurich, Switzerland.
                [6 ] Department of Immunotechnology, Lund University, Lund, Sweden.
                [7 ] 1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2] Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland. [3] Faculty of Science, University of Zurich, Zurich, Switzerland.
                Article
                nbt.2841
                10.1038/nbt.2841
                24727770
                c4e7251c-8031-45d3-bbe2-c388fdb60287
                History

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