Evidence map›Paper›PMID 29123162›Full record

ArticleScientific reports2017

LPPtiger software for lipidome-specific prediction and identification of oxidized phospholipids from LC-MS datasets.

Zhixu Ni, Georgia Angelidou, Ralf Hoffmann, Maria Fedorova

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  5. Emerging Roles of Ferroptosis in Skin Pathophysiology.The Journal of investigative dermatology · 2025
    Review
  6. Article
  7. Article
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  11. Review
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  13. Article
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  15. Article
  16. Article
  17. Interpreting the lipidome: bioinformatic approaches to embrace the complexity.Metabolomics : Official journal of the Metabolomic Society · 2021
    Review
  18. Review
  19. A lipidome atlas in MS-DIAL 4.Nature biotechnology · 2020
    Article
  20. Review
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.

Zhixu NiInstitute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Universität Leipzig, Deutscher Platz 5, 04103, Leipzig, Germany.
Georgia AngelidouInstitute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Universität Leipzig, Deutscher Platz 5, 04103, Leipzig, Germany.
Ralf HoffmannInstitute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Universität Leipzig, Deutscher Platz 5, 04103, Leipzig, Germany.
Maria FedorovaInstitute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Universität Leipzig, Deutscher Platz 5, 04103, Leipzig, Germany. maria.fedorova@bbz.uni-leipzig.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oxidized phospholipids (oxPLs) have been recently recognized as important mediators of various and often controversial cellular functions and stress responses. Due to the low concentrations in vivo, oxPL detection is mostly performed by targeted mass spectrometry. Although significantly improving the sensitivity, this approach does not provide a comprehensive view on oxPLs required for understanding oxPL functional activities. While capable of providing information on the diversity of oxPLs, the main challenge of untargeted lipidomics is the absence of bioinformatics tools to support high-throughput identification of previously unconsidered, oxidized lipids. Here, we present LPPtiger, an open-source software tool for oxPL identification from data-dependent LC-MS datasets. LPPtiger combines three unique algorithms to predict oxidized lipidome, generate oxPL spectra libraries, and identify oxPLs from tandem MS data using parallel processing and a multi-scoring identification workflow.

Identifiers

PMID29123162
PMCPMC5680299

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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.