Evidence map›Paper›PMID 42680974›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Lipid and Metabolite Annotation Using Lipid Data Analyzer.

Leonida M Lamp, Jürgen Hartler, Gerhard G Thallinger

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Leonida M LampInstitute of Pharmaceutical Sciences, University of Graz, Graz, Austria.
Jürgen HartlerInstitute of Pharmaceutical Sciences, University of Graz, Graz, Austria.
Gerhard G ThallingerInstitute of Biomedical Informatics, Graz University of Technology, Graz, Austria. gerhard.thallinger@tugraz.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Lipid Data Analyzer (LDA) is a platform-independent software tool for the automated identification and quantification of lipid species and other metabolites in both untargeted and targeted mass spectrometry (MS) data. LDA mirrors the decision-making process of trained MS experts, enabling automated, high-throughput analyses across any instrumental setup through customizable, rule-based logic. These decision rule sets represent chemical and structural logic, yielding annotation results of high reliability. Furthermore, decision rules can be easily extended to accommodate new lipid classes, adduct forms, or fragmentation mechanisms. This makes LDA particularly effective for identifying novel lipid species or structural isomers that are often missed by traditional approaches, and enables users without a bioinformatics background to easily adapt to evolving analytical protocols. Moreover, LDA supports the identification of double bond positions and other modifications, such as oxidations. Here, we present a step-by-step guide and practical tips for efficient operation of LDA, and provide an introductory discussion of common pitfalls in lipidomics data analysis.

Indexed as

LipidomicsLipidsMetabolomicsSoftwareComputational BiologyHumansLipid MetabolismMass SpectrometryLipidsChromatographydouble bond localizationLDALipid identificationLipidomicsMass spectrometryMetabolomicsOxidized lipidsUntargeted

Identifiers

PMID42680974

What OpenQuestion holds

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Registered trials

None linked

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.