학술논문
Avant-garde: an automated data-driven DIA data curation tool
data-independent acquisition
data-independent acquisition
Document Type
Report
Author
Source
Nature Methods. December 2020, Vol. 17 Issue 12, p1237, 8 p.
Subject
Language
English
ISSN
1548-7091
Abstract
Author(s): Alvaro Sebastian Vaca Jacome [sup.1] , Ryan Peckner [sup.1] [sup.4] , Nicholas Shulman [sup.2] , Karsten Krug [sup.1] , Katherine C. DeRuff [sup.1] , Adam Officer [sup.1] , Karen [...]
Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data. A computational tool, Avant-garde, automates refinement of data-independent acquisition mass spectrometry-based quantitative proteomics data.
Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data. A computational tool, Avant-garde, automates refinement of data-independent acquisition mass spectrometry-based quantitative proteomics data.