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apQuant:通过质量过滤进行准确的无标记定量。

apQuant: Accurate Label-Free Quantification by Quality Filtering.

机构信息

Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria.

Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria.

出版信息

J Proteome Res. 2019 Jan 4;18(1):535-541. doi: 10.1021/acs.jproteome.8b00113. Epub 2018 Nov 2.

Abstract

Label-free quantification of shotgun proteomics data is a frequently used strategy, offering high dynamic range, sensitivity, and the ability to compare a high number of samples without additional labeling effort. Here, we present a bioinformatics approach that significantly improves label-free quantification results. We employ Percolator to assess the quality of quantified peptides. This allows to extract accurate and reliable quantitative results based on false discovery rate. Benchmarking our approach on previously published public data shows that it considerably outperforms currently available algorithms. apQuant is available free of charge as a node for Proteome Discoverer.

摘要

无标记定量蛋白质组学数据是一种常用的策略,它具有高动态范围、灵敏度和无需额外标记即可比较大量样本的能力。在这里,我们提出了一种生物信息学方法,可显著提高无标记定量的结果。我们使用 Percolator 来评估定量肽的质量。这使得可以基于错误发现率提取准确可靠的定量结果。在以前发表的公共数据上对我们的方法进行基准测试表明,它明显优于现有的算法。apQuant 可作为 Proteome Discoverer 的节点免费使用。

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