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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A Skyline Plugin for Pathway-Centric Data Browsing.

Michael G Degan1, Lillian Ryadinskiy1, Grant M Fujimoto1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.

Journal of the American Society for Mass Spectrometry
|August 18, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a Skyline plugin to simplify targeted proteomics assay design using pathway-centric views. It aids biologists in selecting proteins and designing quantitative experiments for mass spectrometry, improving adoption of proteomics protocols.

Keywords:
BioinformaticsData analysisSystems biologyTargeted proteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Mass Spectrometry

Background:

  • Targeted proteomics methods like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) require accessible assay design for routine use.
  • Biological hypotheses often involve protein interactions, networks, and pathways, necessitating a pathway-centric approach to experimental design.

Purpose of the Study:

  • To develop a plugin for the Skyline tool that facilitates targeted proteomics assay design by presenting mass spectrometry data in a pathway-centric view.
  • To assist wet-bench biologists in browsing available data and designing quantitative proteomics experiments.

Main Methods:

  • A plugin for the Skyline software was developed, integrating public mass spectrometry data and a pathway-centric visualization.
  • The plugin utilizes the Pacific Northwest National Laboratory (PNNL) Biodiversity Library and the draft human proteome.
  • Users can import personal data for pathway navigation before importing into Skyline for assay design (transition selection).

Main Results:

  • The plugin enables users to browse public mass spectrometry data within a biological pathway context.
  • It facilitates the selection of proteins and their corresponding mass spectra for targeted proteomics assay design in Skyline.
  • The plugin supports hypothesis-driven data-independent acquisition (DIA) data analysis by narrowing down proteins of interest via pathway views.

Conclusions:

  • The developed Skyline plugin simplifies targeted proteomics assay design by offering a pathway-centric view of mass spectrometry data.
  • This tool enhances the adoption of targeted proteomics protocols by aligning experimental design with biological hypotheses.
  • The plugin supports both SRM/PRM assay design and DIA data analysis, leveraging extensive public and user-provided proteomic datasets.