PNNL

Abstract

Motivation: Biological systems are in a continual state of flux, which necessitates an understanding of the dynamic nature of protein abundances. The study of protein abundance dynamics has become feasible with recent improvements in mass spectrometry-based quantitative proteomics. However, a number of challenges still re-main related to how best to extract biological information from dy-namic proteomics data; for example, challenges related to extrane-ous variability, missing abundance values, and the identification of significant temporal patterns. Results: This article describes a strategy that addresses the afore-mentioned issues for the analysis of temporal bottom-up proteomics data. The core strategy for the data analysis algorithms and subse-quent data interpretation was formulated to take advantage of the temporal properties of the data. The analysis procedure presented herein was applied to data from a Rhodobacter sphaeroides 2.4.1 time-course study. The results were in close agreement with existing knowledge about R. sphaeroides, therefore demonstrating the utility of this analytical strategy.