SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods
SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods
复制标题
SILVER：通过质量控制方法进行稳定同位素标记 LC-MS 数据定量分析的有效工具
DOI：
10.1093/bioinformatics/btt726
复制
发表时间：
2014-02-15
期刊：
影响因子：
5.8
通讯作者：
Zhu, Yunping
中科院分区：
文献类型：
--
作者：
Chang, Cheng;Zhang, Jiyang;Zhu, Yunping
摘要
随着实验技术的进步，不同的稳定同位素标记方法已广泛应用于定量蛋白质组学。在这里，我们提出了一种名为 SILVER 的有效工具，用于处理稳定同位素标记质谱数据。 SILVER 分别在光谱、肽和蛋白质水平上实施了定量质量控制的新方法。使用几个新的量化置信度过滤器和指数来提高量化结果的准确性。使用大型数据集和两个标准数据集验证了 SILVER 的性能，并与 MaxQuant 和 Proteome Discoverer 进行了比较。结果表明，SILVER 显示出高精度和鲁棒性，同时消耗的处理时间要少得多。此外，SILVER 还提供用户友好的界面，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。
可用性和实施
SILVER 及其源代码可根据 GNU 通用公共许可证 v3.0 在 http://bioinfo.hupo.org.cn/silver 上免费获取。
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.