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A computational strategy to analyze label-free temporal bottom-up proteomics data.

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.

SUBMITTER: Du X

PROVIDER: S-EPMC2574618 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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A computational strategy to analyze label-free temporal bottom-up proteomics data.

Du Xiuxia X Callister Stephen J SJ Manes Nathan P NP Adkins Joshua N JN Alexandridis Roxana A RA Zeng Xiaohua X Roh Jung Hyeob JH Smith William E WE Donohue Timothy J TJ Kaplan Samuel S Smith Richard D RD Lipton Mary S MS

Journal of proteome research 20080429 7

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 identif ...[more]

PMID: 18442284

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