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plantMASST - Community-driven chemotaxonomic digitization of plants

Gomes, P. W. P.; Mannochio-Russo, H.; Schmid, R.; Zuffa, S.; Damiani, T.; Quiros-Guerrero, L.-M.; Caraballo-Rodriguez, A. M.; Zhao, H. N.; Yang, H.; Xing, S.; Charron-Lamoureux, V.; Chigumba, D. N.; Sedio, B. E.; Myers, J. A.; Allard, P.-M.; Harwood, T. V.; Tamayo-Castillo, G.; Kang, K. B.; Defossez, E.; Koolen, H. H. F.; da Silva, M. N.; e Silva, C. Y. Y.; Rasmann, S.; Walker, T. W. N.; Glauser, G.; Chaves-Fallas, J. M.; David, B.; Kim, H.; Lee, K. H.; Kim, M. J.; Choi, W. J.; Keum, Y.-S.; de Lima, E. J. S. P.; de Medeiros, L. S.; Bataglion, G. A.; Costa, E. V.; da Silva, F. M. A.; Carvalho,

2024-05-15 plant biology
10.1101/2024.05.13.593988 bioRxiv
Show abstract

Understanding the distribution of hundreds of thousands of plant metabolites across the plant kingdom presents a challenge. To address this, we curated publicly available LC-MS/MS data from 19,075 plant extracts and developed the plantMASST reference database encompassing 246 botanical families, 1,469 genera, and 2,793 species. This taxonomically focused database facilitates the exploration of plant-derived molecules using tandem mass spectrometry (MS/MS) spectra. This tool will aid in drug discovery, biosynthesis, (chemo)taxonomy, and the evolutionary ecology of herbivore interactions.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

1
Scientific Data
○ 209 papers in training set
Top 0.1%
73.0%
Similar papers in this journal
50% of probability mass above
2
Nature Communications
○ 5641 papers in training set
Top 34%
3.3%
Similar papers in this journal
3
Metabolites
○ 53 papers in training set
Top 0.4%
2.5%
Similar papers in this journal
4
Frontiers in Plant Science
○ 256 papers in training set
Top 3%
2.1%
Similar papers in this journal
5
Analytical Chemistry
● 218 papers in training set
Top 1%
2.0%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 57%
1.5%
7
New Phytologist
○ 346 papers in training set
Top 4%
1.2%
8
Journal of Proteome Research
● 234 papers in training set
Top 1%
1.2%
9
Journal of Agricultural and Food Chemistry
● 15 papers in training set
Top 0.4%
1.2%
10
Horticulture Research
◐ 47 papers in training set
Top 0.9%
0.9%
11
Journal of Natural Products
● 15 papers in training set
Top 0.2%
0.9%
12
The Plant Journal
○ 215 papers in training set
Top 3%
0.9%
13
Nature Biotechnology
○ 172 papers in training set
Top 4%
0.9%
14
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.5%
0.9%
15
eLife
● 5828 papers in training set
Top 68%
0.6%
16
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.6%
17
Nature Metabolism
○ 69 papers in training set
Top 2%
0.6%
18
Molecular Omics
◐ 23 papers in training set
Top 0.4%
0.6%
19
Communications Biology
○ 993 papers in training set
Top 33%
0.6%
20
Food & Function
● 13 papers in training set
Top 0.4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.