Crop Metabolome Web v1.0
Is an integrated platform for crop metabolome databases and analysis tools that is dedicated to metabolic profiling, metabolite identification, referencing standards, and repository and dissemination of crop metabolomics data. It currently contains three functional components: 1) metabolite reference libraries (MS/MS spectral tag [MS2T] libraries) for major crops (currently six available) and the associated tool 'MS2TBlast' to annotate crop samples; 2) a repository for dissemination of curated crop metabolomics data; 3) analytical tools 'CompoundLibBlast', 'FlavoBLAST', 'CropMetaDiscover', and 'PesticiSCAN', for crops metabolite profiling and discovery, and pesticidal residue detection for toxicity evaluation.
Invitation to collaborate on expanding crop coverage :
We are inviting people to collaborate on expanding coverage on other crops, like sorghum, millet, beet, coffee, tea, tomato, peanut, rapeseed, olive, citrus, pineapple, banana, apple, plum, apricot, peach, date, grape, etc. Please contact us if you are willing to participate!
- CropMetaDB: Repository of raw metabolomics data for different crops.
- MS2T libraries: Metabolite reference libraries for each of different crops.
- Agrochemical spectral library: the spectral reference library generated for pesticides in US EPA Pesticide Ecotoxicity Database and EU Pesticides Database.
- MS2TBlast: Searching MS2T libraries for identification of metabolite peaks in acquired crop samples.
- CompoundLibBlast: Searching bioactive compound reference libraries to identify a metabolite by matching MS/MS spectra.
- FlavoBLAST: Detecting flavonoid derivatives by matching in-source fragment ions of a flavonoid motif library.
- CropMetaDiscover: Integrated pipeline for annotation of crop sample MS spectra using MS2T libraries.
- PesticiSCAN: Detection of pesticide residues in crop samples by searching agrochemical spectral libraries for matching MS/MS spectra.
Citing CropMetaWeb :
1. CropMetaWeb: an integrated platform for deep metabolomic analysis of crop metabolic functions. (Submitted)
2. Expanding the coverage of the metabolic landscape in cultivated rice with integrated computational approaches. Genomics Proteomics Bioinformatics 2021; S1672-0229(21)00030-9. doi: 10.1016/j.gpb.2020.06.018.