Dataset Information

MzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data.

ABSTRACT: Although not yet truly 'comprehensive', modern mass spectrometry-based experiments can generate quantitative data for a meaningful fraction of the human proteome. Importantly for large-scale protein expression analysis, robust data pipelines are in place for identification of un-modified peptide sequences and aggregation of these data to protein-level quantification. However, interoperable software tools that enable scientists to computationally explore and document novel hypotheses for peptide sequence, modification status, or fragmentation behavior are not well-developed. Here, we introduce mzStudio, an open-source Python module built on our multiplierz project. This desktop application provides a highly-interactive graphical user interface (GUI) through which scientists can examine and annotate spectral features, re-search existing PSMs to test different modifications or new spectral matching algorithms, share results with colleagues, integrate other domain-specific software tools, and finally create publication-quality graphics. mzStudio leverages our common application programming interface (mzAPI) for access to native data files from multiple instrument platforms, including ion trap, quadrupole time-of-flight, Orbitrap, matrix-assisted laser desorption ionization, and triple quadrupole mass spectrometers and is compatible with several popular search engines including Mascot, Proteome Discoverer, X!Tandem, and Comet. The mzStudio toolkit enables researchers to create a digital provenance of data analytics and other evidence that support specific peptide sequence assignments.

SUBMITTER: Ficarro SB

PROVIDER: S-EPMC5620537 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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mzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data.

Ficarro Scott B SB Alexander William M WM Marto Jarrod A JA

Proteomes 20170801 3

Although not yet truly 'comprehensive', modern mass spectrometry-based experiments can generate quantitative data for a meaningful fraction of the human proteome. Importantly for large-scale protein expression analysis, robust data pipelines are in place for identification of un-modified peptide sequences and aggregation of these data to protein-level quantification. However, interoperable software tools that enable scientists to computationally explore and document novel hypotheses for peptide ...[more]

PMID: 28763045

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Project description:Lipidomics is an important component of most multi-Omics systems biology studies and is largely driven by mass spectrometry (MS). Because lipids are tight regulators of multiple cellular functions, including energy homeostasis, membrane structures and cell signaling, lipidomics can provide a deeper understanding of variations underlying disease states and can become an even more powerful platform when combined with other omics, including genomics or proteomics. However, data analysis, especially in lipid annotation, poses challenges due to the heterogeneity of functional head groups and fatty acyl chains of varying hydrocarbon lengths and degrees of unsaturation. As there are various MS/MS fragmentation sites in lipids that are class-dependent, obtaining MS/MS data that includes as many fragment ions as possible is critical for structural characterization of lipids in lipidomics workflow. Here, we report an improved lipidomics methodology that resulted in increased coverage of lipidome using: 1) An automated data-driven MS/MS acquisition scheme in which inclusion and exclusion lists were automatically generated from the full scan MS of sample injections, followed by creation of updated lists over iterative analyses; and, 2) Incorporation of dual dissociation techniques of higher-energy collision dissociation and collision-induced dissociation for more accurate characterization of phosphatidylcholine species. Inclusion lists were created automatically based on full scan MS signals from samples and through iterative analyses, ions in the inclusion list that were fragmented were automatically moved to the exclusion list in subsequent runs. We confirmed that analytes with low MS response that did not undergo MS/MS events in conventional data-dependent analysis were successfully fragmented using this approach. Overall, this automated data-driven data acquisition approach resulted in a higher coverage of lipidome and the use of dual dissociation techniques provided additional information that was critical in characterizing the side chains of phosphatidylcholine species.

Dataset Information

MzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data.

Publications

mzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data.

Similar Datasets

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