Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
