Quality assessment and interference detection in targeted mass spectrometry data using machine learning.

ABSTRACT: Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromat

SUBMITTER: Toghi Eshghi S 

PROVIDER: S-EPMC6173846 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature