Evidence map›Paper›PMID 37584663›Full record

ArticleAnalytical chemistry2023

LORA, Lipid Over-Representation Analysis Based on Structural Information.

Michaela Vondrackova, Dominik Kopczynski, Nils Hoffmann, Ondrej Kuda

Abstract read
In one paragraph

Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Michaela VondrackovaInstitute of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague, Czechia.
Dominik KopczynskiInstitute of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria.ORCID 0000-0001-5885-4568
Nils HoffmannForschungszentrum Jülich, Institute of Bio- and Geosciences (IBG-5), 52428 Jülich, Germany.ORCID 0000-0002-6540-6875
Ondrej KudaInstitute of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague, Czechia.ORCID 0000-0001-7034-4536

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increasing number of lipidomic studies, there is a need for an efficient and automated analysis of lipidomic data. One of the challenges faced by most existing approaches to lipidomic data analysis is lipid nomenclature. The systematic nomenclature of lipids contains all available information about the molecule, including its hierarchical representation, which can be used for statistical evaluation. The Lipid Over-Representation Analysis (LORA) web application (https://lora.metabolomics.fgu.cas.cz) analyzes this information using the Java-based Goslin framework, which translates lipid names into a standardized nomenclature. Goslin provides the level of lipid hierarchy, including information on headgroups, acyl chains, and their modifications, up to the "complete structure" level. LORA allows the user to upload the experimental query and reference data sets, select a grammar for lipid name normalization, and then process the data. The user can then interactively explore the results and perform lipid over-representation analysis based on selected criteria. The results are graphically visualized according to the lipidome hierarchy. The lipids present in the most over-represented terms (lipids with the highest number of enriched shared structural features) are defined as Very Important Lipids (VILs). For example, the main result of a demo data set is the information that the query is significantly enriched with "glycerophospholipids" containing "acyl 20:4" at the "

Indexed as

GlycerophospholipidsLipidsLipidomicsSoftwareGlycerophospholipidsLipids

Identifiers

PMID37584663
PMCPMC10469370

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.