Evidence map›Paper›PMID 37715285›Full record

ArticleJournal of cheminformatics2023

rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation.

Gerard Baquer, Lluc Sementé, Pere Ràfols, Lucía Martín-Saiz, Christoph Bookmeyer, José A Fernández, Xavier Correig, María García-Altares

Open access · goldAbstract read
In one paragraph

Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
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

9 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
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

8 authors at 4 institutions in 2 countries.

Gerard BaquerDepartment of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain. gerard.baquer@alumni.urv.cat.
Lluc SementéDepartment of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain.
Pere RàfolsDepartment of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain. pere.rafols@urv.cat.
Lucía Martín-SaizDepartment of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Spain.
Christoph BookmeyerDepartment of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain.
José A FernándezDepartment of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Spain.
Xavier CorreigDepartment of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain.
María García-AltaresDepartment of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain.
Universidad Rovira i Virgili · ESUniversity of the Basque Country · ESInstitut d'Investigació Sanitària Pere Virgili · ESUniversity of Münster · DE

Funding

Agency for Management of University and Research Grants of the Generalitat de Catalunya (AGAUR) 2018 BP 00188Marie Skłodowska-Curie - European Union's Horizon 2020 713679Spanish Ministry of Economy and Competitivity RTI2018096061-B-100Universitat Rovira i Virgili 2017PMF-PIPF-60
6 · The paper itself

Abstract

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.

Indexed as

AnnotationBioinformaticsCheminformaticsComputationIn-source decayIn-source fragmentationLipidsMALDIMass spectrometry imaging

Identifiers

PMID37715285
PMCPMC10504721
OpenAlexW4386773933

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.