Yantai University, Yantai, Shandong 264005, China.
Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China.
J Hazard Mater. 2022 Apr 5;427:127912. doi: 10.1016/j.jhazmat.2021.127912. Epub 2021 Nov 26.
Data mining was one of the most important challenges in natural product analysis and biomarker discovery. In this work, we proposed an integrated data analysis protocol for natural products annotation and identification in data-dependent acquisition. Firstly, natural products and structure-related compounds could be identified by comparing mass spectrum behavior with commercial standard. Secondly, diagnostic fragmentation filtering (DFF) function in MZmine (http://mzmine.github.io/) was investigated for screening specific conjugation compounds with the same neutral loss. Thirdly, we present feature-based molecular networking (FBMN) in GNPS (https://gnps.ucsd.edu/) as a chromatographic feature detection and alignment tool. In addition, FBMN could enable natural products analysis based on molecular networks. This proposed integrated protocol should facilitate metabolomic data mining and biomarker discovery.
数据挖掘是天然产物分析和生物标志物发现中最重要的挑战之一。在这项工作中，我们提出了一种用于数据依赖采集的天然产物注释和鉴定的综合数据分析方案。首先，可以通过将质谱行为与商业标准进行比较来鉴定天然产物和结构相关化合物。其次，研究了 MZmine（http://mzmine.github.io/）中的诊断碎片过滤（DFF）功能，以筛选具有相同中性丢失的特定缀合化合物。第三，我们在 GNPS（https://gnps.ucsd.edu/）中展示了基于特征的分子网络（FBMN）作为色谱特征检测和对齐工具。此外，FBMN 可以基于分子网络进行天然产物分析。该综合方案应有助于代谢组学数据挖掘和生物标志物发现。
