一种用于分析无标记自下而上蛋白质组学时间数据的计算策略。
A computational strategy to analyze label-free temporal bottom-up proteomics data.
作者信息
Du Xiuxia, Callister Stephen J, Manes Nathan P, Adkins Joshua N, Alexandridis Roxana A, Zeng Xiaohua, Roh Jung Hyeob, Smith William E, Donohue Timothy J, Kaplan Samuel, Smith Richard D, Lipton Mary S
机构信息
Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA.
出版信息
J Proteome Res. 2008 Jul;7(7):2595-604. doi: 10.1021/pr0704837. Epub 2008 Apr 29.
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.
摘要
生物系统处于不断变化的状态，这就需要了解蛋白质丰度的动态性质。随着基于质谱的定量蛋白质组学的最新进展，蛋白质丰度动态的研究已变得可行。然而，在如何从动态蛋白质组学数据中最佳地提取生物学信息方面，仍存在一些挑战，例如与无关变异性、缺失丰度值以及识别显著时间模式相关的挑战。本文描述了一种解决这些问题的策略，并使用来自球形红细菌2.4.1时间进程研究的数据，展示了其在分析自下而上的时间蛋白质组学数据方面的价值。
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本文引用的文献
1
VIPER: an advanced software package to support high-throughput LC-MS peptide identification.
Bioinformatics. 2007 Aug 1;23(15):2021-3. doi: 10.1093/bioinformatics/btm281. Epub 2007 Jun 1.
2
Quantitative proteomics by stable isotope labeling and mass spectrometry.
Methods Mol Biol. 2007;367:209-18. doi: 10.1385/1-59745-275-0:209.
3
Normalization regarding non-random missing values in high-throughput mass spectrometry data.
Pac Symp Biocomput. 2006:315-26.
4
Robust algorithm for alignment of liquid chromatography-mass spectrometry analyses in an accurate mass and time tag data analysis pipeline.
Anal Chem. 2006 Nov 1;78(21):7397-409. doi: 10.1021/ac052197p.
5
Early events of Bacillus anthracis germination identified by time-course quantitative proteomics.
Proteomics. 2006 Oct;6(19):5199-211. doi: 10.1002/pmic.200600314.
6
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J Microbiol Methods. 2006 Dec;67(3):424-36. doi: 10.1016/j.mimet.2006.04.021. Epub 2006 Jul 7.
7
Estimating probabilities of peptide database identifications to LC-FTICR-MS observations.
Proteome Sci. 2006 Feb 24;4:1. doi: 10.1186/1477-5956-4-1.
8
PRISM: a data management system for high-throughput proteomics.
Proteomics. 2006 Mar;6(6):1783-90. doi: 10.1002/pmic.200500500.
9
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J Proteome Res. 2006 Feb;5(2):277-86. doi: 10.1021/pr050300l.
10
Advances in proteomics data analysis and display using an accurate mass and time tag approach.
Mass Spectrom Rev. 2006 May-Jun;25(3):450-82. doi: 10.1002/mas.20071.
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