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MS/MS Mass Spectrometry Filtering Tree for Bile Acid Isomer Annotation.

Mohanty, I.; Xing, S.; Castillo, V.; Agongo, J.; Patan, A.; El Abiead, Y.; Mannochio-Russo, H.; Zuffa, S.; Zemlin, J.; Tronel, A.; Le Gouellec, A.; Soranzo, T.; Wang, C. X.; Iudicello, J. E.; Andalibi, M. S.; Ellis, R. J.; Moore, D. J.; Franklin, D. R.; Sala-Climent, M.; Guma, M.; Voigt, R. M.; Kaddaruh-Dauok, R.; Siegel, D.; Wang, M.; Hagey, L. R.; Dorrestein, P. C.

2025-03-11 bioinformatics
10.1101/2025.03.04.641505 bioRxiv
Show abstract

Bile acids are essential steroids regulating immunity, nutrient absorption, insulin, appetite, and body temperature. Their structural diversity is vast, but due to spectral similarities, MS/MS spectral matching often fails to resolve isomers. This study introduces a proof-of-concept workflow using a mass spectrometry query language filtering tree that distinguishes isomeric bile acids in untargeted LC-MS/MS data. Its application revealed a deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 5%
23.3%
Similar papers in this journal
2
Analytical Chemistry
● 218 papers in training set
Top 0.3%
13.5%
Similar papers in this journal
3
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.1%
7.0%
Similar papers in this journal
4
Metabolites
○ 53 papers in training set
Top 0.1%
7.0%
Similar papers in this journal
50% of probability mass above
5
Journal of Proteome Research
● 234 papers in training set
Top 0.6%
5.7%
Similar papers in this journal
6
Nature Methods
○ 385 papers in training set
Top 2%
5.4%
7
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.8%
8
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.2%
9
Scientific Reports
○ 3612 papers in training set
Top 50%
1.8%
10
iScience
○ 1154 papers in training set
Top 20%
1.6%
11
Scientific Data
○ 209 papers in training set
Top 2%
1.4%
12
Communications Biology
○ 993 papers in training set
Top 20%
1.2%
13
Communications Chemistry
○ 48 papers in training set
Top 0.7%
1.2%
14
Cell Reports Methods
○ 165 papers in training set
Top 2%
1.2%
15
Cell Systems
○ 201 papers in training set
Top 4%
1.1%
16
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.1%
17
Nature Chemical Biology
○ 119 papers in training set
Top 2%
1.0%
18
Nature Metabolism
○ 69 papers in training set
Top 2%
0.9%
19
Briefings in Bioinformatics
◐ 354 papers in training set
Top 7%
0.9%
20
ACS Central Science
● 71 papers in training set
Top 1%
0.9%
21
Cell Chemical Biology
○ 94 papers in training set
Top 2%
0.6%
22
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 43%
0.6%
23
Chemical Science
● 73 papers in training set
Top 2%
0.6%
24
Molecular & Cellular Proteomics
183 papers in training set
Top 2%
0.6%
25
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.6%
26
Microbiome
○ 154 papers in training set
Top 3%
0.5%
27
PLOS ONE
● 5266 papers in training set
Top 64%
0.5%
28
Cell Reports
○ 1498 papers in training set
Top 30%
0.5%
29
Clinical and Translational Science
○ 22 papers in training set
Top 0.9%
0.5%

"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.