Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample’s metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.

直接输注基于非靶向质谱的代谢组学可以快速了解样品的代谢活性。但是，由于检测到的m / z值的数组很大并且难以确定重要的m / z的优先级并同时注释其假定的身份，分析通常会变得很复杂。为了应对这一挑战，我们开发了MetaboShiny，这是一个基于R / RShiny的新型代谢组学软件包，具有数据分析，基于数据库和公式预测的注释和可视化功能。为了证明这一点，我们复制并进一步探索了针对肺癌患者尿液样本的MetaboLights代谢组学生物信息学研究。MetaboShiny可对基于非靶向质谱的直接输注的代谢组学数据进行快速，严格的分析和解释。