Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS.

ABSTRACT: Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which, in turn, is detrimental for accurate quantification. The nonoutlier fragment ion (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high-priority fragment ions, these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative d

SUBMITTER: Bilbao A 

PROVIDER: S-EPMC4739871 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature