Missing Value Monitoring to Address Missing Values in Quantitative Proteomics.

Abstract

Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis by classical data dependent proteomics methods (DDA). Moreover, DDA runs in shotgun proteomics experiments are plenty of missing values among the replicates due to the stochastic nature of the acquisition method, thus hampering the robustness of the quantitative analysis. Here, we have overcome these obstacles designing a robust workflow named missing value monitoring (MvM) in order to follow low abundance proteins dynamics.