QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments.,Journal of Proteome Research

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QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments.
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2019-04-11 , DOI: 10.1021/acs.jproteome.9b00072
Joris Van Houtven _{1,

2,

3} , Annelies Agten ₂ , Kurt Boonen _{1,

3} , Geert Baggerman _{1,

3} , Jef Hooyberghs _{1,

4} , Kris Laukens _{5,

6} , Dirk Valkenborg _{1,

2,

3}

Affiliation

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net ) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

中文翻译：

QCQuan：用于自动评估标记质谱实验的蛋白质表达和数据质量的Web工具。

在组学学科尤其是蛋白质组学和生物标记物发现的背景下，使用基于标记的串联质谱（MS）对临床样品进行分析可能会受到样品制备效果或测量过程本身的影响，从而导致错误的结果。使用基于混合模型的最新方法来检测和纠正这些错误可能会花费大量的（计算）时间。基于MS的蛋白质组学实验室具有很高的通量，需要通过快速区分高质量数据和低质量数据来避免量化管道中的瓶颈。为此，我们开发了一个易于使用的网络工具QCQuan（可从qcquan.net上获得），该工具基于CONSTANd归一化算法构建。它会自动为用户提供探索性和质量控制信息，并基于保守的简单统计信息进行差异表达分析。在本文中，我们详细描述了构成工作流程的科学相关步骤，并根据三个参考数据集评估了其定性和定量性能。我们发现，QCQuan可为高质量和低质量数据集的科学价值提供清晰准确的指示。而且，与使用已建立的可靠软件组装的可比工作流程相比，它在第三数据集上的定量效果要好得多。在本文中，我们详细描述了构成工作流程的科学相关步骤，并根据三个参考数据集评估了其定性和定量性能。我们发现，QCQuan可为高质量和低质量数据集的科学价值提供清晰准确的指示。而且，与使用已建立的可靠软件组装的可比工作流程相比，它在第三数据集上的定量效果要好得多。在本文中，我们详细描述了构成工作流程的科学相关步骤，并根据三个参考数据集评估了其定性和定量性能。我们发现，QCQuan可为高质量和低质量数据集的科学价值提供清晰准确的指示。而且，与使用已建立的可靠软件组装的可比工作流程相比，它在第三数据集上的定量效果要好得多。

更新日期：2019-04-12

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