Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.

ABSTRACT: Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differential proteins use intermediate filters to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3

SUBMITTER: The M 

PROVIDER: S-EPMC6398204 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature