Unknown

Dataset Information

IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.


ABSTRACT: Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature

SUBMITTER: Shen X 

PROVIDER: S-EPMC6003523 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature