School of Medicine.
SUSTech Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China.
Bioinformatics. 2020 Jun 1;36(12):3913-3915. doi: 10.1093/bioinformatics/btaa200.
Liquid chromatography-mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement.
To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies.
The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva.
Supplementary data are available at Bioinformatics online.
基于液相色谱-质谱的非靶向代谢组学通常用于定性和定量分析复杂生物样本中大量代谢物信号。然而，许多流行的软件通常会将数据集的假阳性峰检测为代谢物信号，从而导致不可靠的测量。
为了减少假阳性的调用，我们开发了一个名为 CPVA 的交互式网络工具，用于可视化和准确注释非靶向代谢组学数据中检测到的峰。我们使用基于色谱图的策略通过可视化峰形态学指标来展开色谱峰的特征，并且具有额外的功能来注释加合物、同位素和污染物。CPVA 是一个免费的、用户友好的工具，可帮助用户识别峰背景噪声和污染物，从而减少假阳性或冗余峰的调用，从而提高非靶向代谢组学研究的数据质量。
CPVA 可在 http://cpva.eastus.cloudapp.azure.com 上免费获得。源代码和安装说明可在 GitHub 上获得：https://github.com/13479776/cpva。
补充数据可在生物信息学在线获得。
