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Review
. 2020 May;39(3):229-244.
doi: 10.1002/mas.21540. Epub 2017 Jul 9.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Lindsay K Pino  1 , Brian C Searle  1 , James G Bollinger  1 , Brook Nunn  1 , Brendan MacLean  1 , Michael J MacCoss  1
Affiliations
Review

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Lindsay K Pino et al. Mass Spectrom Rev. 2020 May.

Abstract

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Keywords: informatics; quantitative mass spectrometry; targeted proteomics.

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Figures

Figure 1
Figure 1. Generalized workflow for quantitative MS assay development
Six main steps are outlined, beginning with the development of a hypothesis and continuing through additional analyses, with examples of the associated Skyline ecosystem features.
Figure 2
Figure 2. Data processing pipeline in Skyline
Skyline derives information from native, vendor-specific file formats or from portable files, producing peak area calculations, and visualizations of the data.
Figure 3
Figure 3. Real-time updating visualizations natively embedded in Skyline
(a) Skyline chromatogram visualizations show the intensity at each resampled retention time point for all fragment ions (displayed as different colored lines identified in the legend) of a precursor, enabling researchers to assess Skyline’s automated peak picking or adjust integration boundaries if necessary. (b) Calculation of coefficient of variation (CV) informs researchers of the reproducibility of peptide peak areas (shown here as the peak area ratio to a global standard) over multiple acquisitions or custom-annotated groups of acquisitions. (c) Real-time updating visualization of precursor retention time across acquisitions enables quick identification of mis-picked peaks over many MS acquisition runs. Out of 42 replicates, the peptide shown here appears to elute three minutes late in one replicate (eighth from the left, marked with arrow) compared to all other replicates, an observation that may prompt the researcher to evaluate that picked peak in the chromatogram visualization pane. (d) Peak area is displayed here as the percentage contributed by each fragment ion of the precursor which allows the researcher to quickly evaluate data quality. For example, the boxed replicate (eighth run from the left, marked with arrow) displays a noticeably different distribution of contributed fragment peak areas, indicating that the picked peak group for this replicate may require further examination.
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