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Reference data based insights expand understanding of human metabolomes

Julia M Gauglitz; Wout Bittremieux; Candace L Williams; Kelly C Weldon; Morgan W Panitchpakdi; Francesca Di Ottavio; Christine M Aceves; Elizabeth Brown; Nicole C Sikora; Alan K. Jarmusch; Cameron Martino; Anupriya Tripathi; Erfan Sayyari; Justin Shaffer; Roxana Coras; Fernando Vargas; Lindsay DeRight Goldasich; Tara Schwartz; MacKenzie Bryant; Gregory Humphrey; Abigail J. Johnson; Katharina Spengler; Pedro Belda-Ferre; Edgar Diaz; Daniel McDonald; Qiyun Zhu; Dominic S. Nguyen; Emmanuel O. Elijah; Mingxun Wang; Clarisse Marotz; Kate E. Sprecher; Daniela Vargas-Robles; Dana Withrow; Gail Ackerm

2020-07-11 systems biology
10.1101/2020.07.08.194159 bioRxiv
Show abstract

The human metabolome has remained largely unknown, with most studies annotating [~]10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic "dark matter."

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
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○ 53 papers in training set
Top 0.1%
12.4%
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2
Nature Communications
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Top 24%
6.7%
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3
Cell
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Top 1%
5.5%
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4
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○ 14 papers in training set
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5
Bioinformatics
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6
Molecular Systems Biology
○ 162 papers in training set
Top 0.3%
5.4%
7
Journal of Proteome Research
● 234 papers in training set
Top 0.7%
4.0%
8
Scientific Data
○ 209 papers in training set
Top 0.6%
4.0%
9
mSystems
● 394 papers in training set
Top 2%
3.5%
50% of probability mass above
10
iScience
○ 1154 papers in training set
Top 7%
3.2%
11
Analytical Chemistry
● 218 papers in training set
Top 1%
3.1%
12
eLife
● 5828 papers in training set
Top 40%
2.6%
13
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.1%
14
Chemosphere
○ 17 papers in training set
Top 0.2%
1.7%
15
Scientific Reports
○ 3612 papers in training set
Top 52%
1.7%
16
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.3%
1.7%
17
Molecular Omics
◐ 23 papers in training set
Top 0.2%
1.7%
18
Patterns
○ 78 papers in training set
Top 1%
1.5%
19
Cell Reports Methods
○ 165 papers in training set
Top 2%
1.4%
20
Cell Reports
○ 1498 papers in training set
Top 23%
1.1%
21
Nature Methods
○ 385 papers in training set
Top 5%
1.1%
22
Cell Systems
○ 201 papers in training set
Top 4%
1.1%
23
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
1.1%
24
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.0%
25
npj Systems Biology and Applications
○ 125 papers in training set
Top 2%
0.9%
26
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.8%
27
PNAS Nexus
◐ 159 papers in training set
Top 4%
0.8%
28
Molecular & Cellular Proteomics
183 papers in training set
Top 2%
0.7%
29
Cell Metabolism
○ 57 papers in training set
Top 2%
0.6%
30
Angewandte Chemie International Edition
○ 93 papers in training set
Top 2%
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.