A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets.

ABSTRACT: Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target-decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target-decoy-based protein FDR estimation approach,

SUBMITTER: Savitski MM 

PROVIDER: S-EPMC4563723 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature