Evidence map›Paper›PMID 32178227›Full record

ArticleMetabolites2020

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software.

Jeremy P Koelmel, Xiangdong Li, Sarah M Stow, Mark J Sartain, Adithya Murali, Robin Kemperman, Hiroshi Tsugawa, Mikiko Takahashi, Vasilis Vasiliou, John A Bowden and 3 more

Abstract read
In one paragraph

Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

56 citing papers in PubMed.

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  12. CAMKIIδ Reinforces Lipid Metabolism and Promotes the Development of B Cell Lymphoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
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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

13 authors.

Jeremy P KoelmelDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida, 32610 Gainesville, FL, USA.ORCID 0000-0001-9985-8901
Xiangdong LiAgilent Technologies, 95051 Santa Clara, CA, USA.
Sarah M StowAgilent Technologies, 95051 Santa Clara, CA, USA.ORCID 0000-0002-4297-583X
Mark J SartainAgilent Technologies, 95051 Santa Clara, CA, USA.
Adithya MuraliAgilent Technologies, 95051 Santa Clara, CA, USA.
Robin KempermanDepartment of Chemistry, University of Florida, 32611 Gainesville, FL, USA.
Hiroshi TsugawaRIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.ORCID 0000-0002-2015-3958
Mikiko TakahashiRIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Vasilis VasiliouDepartment of Environmental Health Sciences, Yale School of Public Health, 06520 New Haven, CT, USA.
John A BowdenDepartment of Chemistry, University of Florida, 32611 Gainesville, FL, USA.
Richard A YostDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida, 32610 Gainesville, FL, USA.
Timothy J GarrettDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida, 32610 Gainesville, FL, USA.
Norton KitagawaAgilent Technologies, 95051 Santa Clara, CA, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Mouse Models for Alcohol Metabolism and Tissue InjuryR24AA022057 · NIAAA · YALE UNIVERSITY · PI VASILIOU, VASILIS · 2013 to 2022
$4.5M
NCATS NIH HHS UL1 TR001863NIAAA NIH HHS R24 AA022057
6 · The paper itself

Abstract

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Indexed as

automationion mobilitylipid annotationlipidomicsliquid chromatographymetabolomicssoftwaretandem mass spectrometrytime-of-flight

Identifiers

PMID32178227
PMCPMC7142889

What OpenQuestion holds

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LicenceCC BY
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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.