Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics.

Journal Article (Journal Article)

Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a 10 year history supporting six major instrument vendors. Designed initially for proteomics analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring, high-resolution mass spectrometry, and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence-centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline, such as peak picking, chromatographic alignment, and transition selection, have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable instrument vendor software tool, and present additional real-world applications. Further, we include step-by-step instructions on using Skyline for small molecule quantitative method development and data analysis on data acquired with a variety of mass spectrometers from multiple instrument vendors.

Full Text

Duke Authors

Cited Authors

  • Adams, KJ; Pratt, B; Bose, N; Dubois, LG; St John-Williams, L; Perrott, KM; Ky, K; Kapahi, P; Sharma, V; MacCoss, MJ; Moseley, MA; Colton, CA; MacLean, BX; Schilling, B; Thompson, JW; Alzheimer’s Disease Metabolomics Consortium,

Published Date

  • April 3, 2020

Published In

Volume / Issue

  • 19 / 4

Start / End Page

  • 1447 - 1458

PubMed ID

  • 31984744

Pubmed Central ID

  • PMC7127945

Electronic International Standard Serial Number (EISSN)

  • 1535-3907

Digital Object Identifier (DOI)

  • 10.1021/acs.jproteome.9b00640


  • eng

Conference Location

  • United States