Evidence map›Paper›PMID 35997060›Full record

ReviewNatural product reports2022

Integrative omics approaches for biosynthetic pathway discovery in plants.

Kumar Saurabh Singh, Justin J J van der Hooft, Saskia C M van Wees, Marnix H Medema

Abstract readReview
In one paragraph

Review in Natural product reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
–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

43 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Ancient origins of alkaloid biosynthesis in medicinal clubmosses.bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Review
  13. Review
  14. Frontiers in plant science · 2026
    Article
  15. Review
  16. Review
  17. Review
  18. Review
  19. 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.

Kumar Saurabh SinghBioinformatics Group, Wageningen University, Wageningen, The Netherlands. marnix.medema@wur.nl.ORCID 0000-0001-8352-5897
Justin J J van der HooftBioinformatics Group, Wageningen University, Wageningen, The Netherlands. marnix.medema@wur.nl.ORCID 0000-0002-9340-5511
Saskia C M van WeesPlant-Microbe Interactions, Institute of Environmental Biology, Utrecht University, The Netherlands. s.vanwees@uu.nl.
Marnix H MedemaBioinformatics Group, Wageningen University, Wageningen, The Netherlands. marnix.medema@wur.nl.ORCID 0000-0002-2191-2821

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.

Indexed as

Biological ProductsBiosynthetic PathwaysGenomicsMetabolomeMetabolomicsPlantsBiological Products

Identifiers

PMID35997060
PMCPMC9491492

What OpenQuestion holds

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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.