apQuant: Accurate Label-Free Quantification by Quality Filtering,Journal of Proteome Research

当前位置： X-MOL 学术 › J. Proteome Res. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

apQuant: Accurate Label-Free Quantification by Quality Filtering
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2018-11-02 , DOI: 10.1021/acs.jproteome.8b00113
Johannes Doblmann ₁ , Frederico Dusberger ₁ , Richard Imre _{1,

2} , Otto Hudecz _{1,

2} , Florian Stanek ₁ , Karl Mechtler _{1,

2} , Gerhard Dürnberger _{1,

2,

3}

Affiliation

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.

中文翻译：

apQuant：通过质量过滤进行准确的无标签定量

鸟枪法蛋白质组学数据的无标签量化是一种常用的策略，它提供了高动态范围、灵敏度以及无需额外标记工作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，可以显着改善无标记量化结果。我们使用 Percolator 来评估定量肽的质量。这允许基于错误发现率提取准确可靠的定量结果。对我们之前发布的公共数据的方法进行基准测试表明，它大大优于当前可用的算法。apQuant 作为 Proteome Discoverer 的节点免费提供。

更新日期：2018-11-05

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11