A Computational Strategy to Analyze Label-Free Temporal Bottom-Up Proteomics Data<SUP>§</SUP>

J. Proteome Res., 7 (7), 2595–2604, 2008. 10.1021/pr0704837
Web Release Date: April 29, 2008

A Computational Strategy to Analyze Label-Free Temporal Bottom-Up Proteomics Data^§

Xiuxia Du,^‡ Stephen J. Callister,^‡ Nathan P. Manes,^‡ Joshua N. Adkins,^‡ Roxana A. Alexandridis,^§ Xiaohua Zeng,^∥ Jung Hyeob Roh,^∥ William E. Smith,^∥ Timothy J. Donohue,^§ Samuel Kaplan,^∥ Richard D. Smith,^‡ and Mary S. Lipton^*^‡

Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, Department of Bacteriology, University of Wisconsin—Madison, Madison, Wisconsin 53706, and Department of Microbiology & Molecular Genetics, Medical School, University of Texas—Houston, Houston, Texas 77225

Received July 31, 2007

Abstract:

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 remain related to how best to extract biological information from dynamic proteomics data, for example, challenges related to extraneous variability, missing abundance values, and the identification of significant temporal patterns. This paper describes a strategy that addresses these issues and demonstrates its values for analyzing temporal bottom-up proteomics data using data from a Rhodobacter sphaeroides 2.4.1 time-course study.

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