Visible Post-Data Analysis Protocol for Natural Mycotoxin Production.
Yanshen LiJinyao QuYucheng LinGuozhu LuYanli YouGui-Bin JiangYongning WuPublished in: Journal of agricultural and food chemistry (2020)
Fungal natural products are routinely analyzed using target detection protocols by comparing to commercial standards. However, discovery of new products suffers from a lack of high-throughput analytical techniques. Post-data process techniques have become popular tools for natural product confirmations and mycotoxin family analysis. In this work, a visible post-data process procedure with MZmine, GNPS, and Xcalibur was used for efficient analysis of high-resolution mass spectrometry. Conjugated products were screened with an optimized diagnostic fragmentation filtering module in MZmine and further confirmed with Xcalibur by comparing to unconjugated commercial standards. MS/MS spectral data were processed and used to establish a feature based on a molecular networking map in GNPS (Global Natural Products Social Molecular Networking; https://gnps.ucsd.edu), for visualization of fungal natural product families. The results demonstrate the potential of combining MZmine-, GNPS-, and Xcalibur-based methods for visible analysis of fungal natural products.
Keyphrases
- data analysis
- high throughput
- electronic health record
- high resolution mass spectrometry
- liquid chromatography
- ms ms
- big data
- randomized controlled trial
- healthcare
- machine learning
- minimally invasive
- small molecule
- photodynamic therapy
- ultra high performance liquid chromatography
- deep learning
- magnetic resonance
- optical coherence tomography
- tandem mass spectrometry
- single cell
- risk assessment
- mass spectrometry
- single molecule
- label free
- human health
- quantum dots
- contrast enhanced
- sensitive detection