Evidence map›Paper›PMID 28890673›Full record

ReviewMetabolomics : Official journal of the Metabolomic Society2017

Navigating freely-available software tools for metabolomics analysis.

Rachel Spicer, Reza M Salek, Pablo Moreno, Daniel Cañueto, Christoph Steinbeck

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Metabolomics : Official journal of the Metabolomic Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 2 pooled it
11.1field-weighted citation impact, top 1% 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

95 citing papers in PubMed, 2 syntheses or guidelines pooled it, 209 citations in OpenAlex.

  1. Pooled it
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  14. Multiplatform untargeted metabolomics.Magnetic resonance in chemistry : MRC · 2023
    Review
  15. Article
  16. Article
  17. Article
  18. "What I wish I had known before starting my PhD".Analytical science advances · 2023
    Article
  19. Article
  20. Chemical shift variations in common metabolites.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2022
    Article

35 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 3 countries.

Rachel SpicerEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.ORCID 0000-0002-2807-8796
Reza M SalekEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.ORCID 0000-0001-8604-1732
Pablo MorenoEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.ORCID 0000-0002-9856-1679
Daniel CañuetoMetabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007 Tarragona, Catalonia Spain.ORCID 0000-0003-2948-0127
Christoph SteinbeckEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK.ORCID 0000-0001-6966-0814
European Bioinformatics Institute · GBInstitut de Recerca Biomèdica Catalunya Sud · ES

Funding

Medical Research Council MR/L01632X/1
6 · The paper itself

Abstract

introductionThe field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.

objectivesTo compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.

methodsThe most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.

resultsA comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.

conclusionThis review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.

Indexed as

BioinformaticsData analysisFreely availableMetabolomicsSoftware

Identifiers

PMID28890673
PMCPMC5550549
OpenAlexW2743576058

What OpenQuestion 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 OpenQuestion graph.