Del Prete Eugenio, Facchiano Angelo, Profumo Aldo, Angelini Claudia, Romano Paolo
Institute for Applied Mathematics, National Research Council, Naples, Italy.
Institute of Food Sciences, National Research Council, Avellino, Italy.
Front Genet. 2021 Mar 29;12:635814. doi: 10.3389/fgene.2021.635814. eCollection 2021.
Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes' fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at .
质谱分析法是一种广泛应用的技术，在蛋白质组学领域有重大影响。基质辅助激光解吸电离飞行时间质谱（MALDI-TOF）是质谱分析中的一种组合技术，在表征来自不同来源的生物样本方面有许多应用，比如癌症生物标志物的鉴定、食品欺诈检测、运动员体液中兴奋剂物质的鉴定等等。以质谱形式存在的大量数据常常受到不同噪声源的影响而产生偏差和改变。因此，提取表征样本的最相关特征往往具有挑战性，需要结合多种计算方法。在此，我们介绍GeenaR，这是一种新颖的网络工具，它为预处理、分析、可视化和比较MALDI-TOF质谱提供了完整的工作流程。GeenaR用户友好，为质谱分析提供了许多不同的功能，并且支持可重复性研究，因为它生成一份包含功能参数、结果以及用于处理质谱的代码的人类可读报告。首先，我们阐述GeenaR中可用的功能。然后，我们描述其内部结构。最后，我们通过展示两个与卵巢癌和结直肠癌相关的案例研究来证明其在分析肿瘤学数据集方面的能力。GeenaR可在[具体网址]获取。
