SILVER：一种高效的工具，用于带有质量控制方法的稳定同位素标记 LC-MS 数据定量分析。
SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods.
机构信息
Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Institute of Radiation Medicine and Department of Bioinformatics, National Engineering Research Center for Protein Drugs, Beijing 102206, Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073 and Department of Chemistry, Institutes of Biomedical Sciences, 130 DongAn Road, Fudan University, Shanghai 200032, P.R. China.
出版信息
Bioinformatics. 2014 Feb 15;30(4):586-7. doi: 10.1093/bioinformatics/btt726. Epub 2013 Dec 15.
SUMMARY
With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses.
AVAILABILITY AND IMPLEMENTATION
SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.
摘要
随着实验技术的进步，不同的稳定同位素标记方法已被广泛应用于定量蛋白质组学。在这里，我们介绍了一种名为 SILVER 的有效工具，用于处理稳定同位素标记质谱数据。SILVER 在谱、肽和蛋白质水平上分别实现了用于定量质量控制的新方法。使用了几种新的定量置信度筛选器和指标，以提高定量结果的准确性。使用大规模数据集和两个标准数据集对 SILVER 的性能进行了验证和比较，结果表明 SILVER 具有很高的准确性和鲁棒性，同时消耗的处理时间更少。此外，SILVER 还提供了用于参数设置、结果可视化、手动验证和一些有用的统计分析的用户友好界面。
可用性和实现
SILVER 及其源代码可根据 GNU 通用公共许可证 v3.0 版在 http://bioinfo.hupo.org.cn/silver 上免费获取。
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