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LipidLynxX: lipid annotations converter for large scale lipidomics and epilipidomics datasets

Ni, Z.; Fedorova, M.

2020-04-10 bioinformatics
10.1101/2020.04.09.033894 bioRxiv
Show abstract

Modern high throughput lipidomics provides large-scale datasets reporting hundreds of lipid molecular species. However, cross-laboratory comparison, meta-analysis, and systems biology integration of in-house generated and published datasets remain challenging due to a high diversity of used lipid annotation systems, different levels of reported structural information, and shortage in links to data integration resources. To support lipidomics data integration and interoperability of experimental lipidomics with data integration tools, we developed LipidLynxX serving as a hub facilitating data flow from high-throughput lipidomics analysis to systems biology data integration. LipidLynxX provides the possibility to convert, cross-match, and link various lipid annotations to the tools supporting lipid ontology, pathway, and network analysis aiming systems-wide integration and functional annotation of lipidome dynamics in health and disease. LipidLynxX is a flexible, customizable open-access tool freely available for download at https://github.com/SysMedOs/LipidLynxX.

Matching journals

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

1
Analytical Chemistry
● 218 papers in training set
Top 0.2%
19.3%
Similar papers in this journal
2
Journal of Proteome Research
● 234 papers in training set
Top 0.2%
19.3%
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3
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○ 53 papers in training set
Top 0.1%
7.0%
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4
Scientific Reports
○ 3612 papers in training set
Top 13%
5.7%
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50% of probability mass above
5
Nature Communications
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Top 29%
5.1%
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6
Molecular Omics
◐ 23 papers in training set
Top 0.1%
5.1%
7
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.2%
3.7%
8
Briefings in Bioinformatics
◐ 354 papers in training set
Top 3%
2.5%
9
Scientific Data
○ 209 papers in training set
Top 0.9%
2.5%
10
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.2%
11
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.2%
12
Nature Protocols
○ 33 papers in training set
Top 0.2%
2.2%
13
International Journal of Molecular Sciences
○ 494 papers in training set
Top 7%
1.8%
14
Analytica Chimica Acta
○ 17 papers in training set
Top 0.3%
1.0%
15
PLOS Computational Biology
● 1863 papers in training set
Top 19%
1.0%
16
Nucleic Acids Research
◐ 1281 papers in training set
Top 12%
1.0%
17
PROTEOMICS
○ 43 papers in training set
Top 0.6%
1.0%
18
Nature Methods
○ 385 papers in training set
Top 6%
0.9%
19
PLOS ONE
● 5266 papers in training set
Top 57%
0.9%
20
GigaScience
◐ 212 papers in training set
Top 4%
0.9%
21
Biomolecules
○ 100 papers in training set
Top 2%
0.9%
22
Frontiers in Bioinformatics
○ 49 papers in training set
Top 1%
0.9%
23
Molecular & Cellular Proteomics
183 papers in training set
Top 2%
0.5%
24
Frontiers in Plant Science
○ 256 papers in training set
Top 5%
0.5%
25
Communications Chemistry
○ 48 papers in training set
Top 2%
0.5%
26
BMC Bioinformatics
○ 457 papers in training set
Top 6%
0.5%
27
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 5%
0.5%
28
Communications Biology
○ 993 papers in training set
Top 37%
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.