Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

ABSTRACT: Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and

SUBMITTER: Weisser H 

PROVIDER: S-EPMC5547443 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature