Evidence map›Paper›PMID 33510763›Full record

ReviewFrontiers in plant science2020

Using Gene Expression to Study Specialized Metabolism-A Practical Guide.

Riccardo Delli-Ponti, Devendra Shivhare, Marek Mutwil

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Combining Short- and Long-Read Transcriptomes for Targeted Enzyme Discovery.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. Article
  6. Evolution of plant metabolism: the state-of-the-art.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Review
  7. Plant sesquiterpene lactones.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Review
  8. Medicinal plant genomics.BMC genomics · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Flavonoids - flowers, fruit, forage and the future.Journal of the Royal Society of New Zealand · 2023
    Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. 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

3 authors.

Riccardo Delli-PontiSchool of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
Devendra ShivhareSchool of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
Marek MutwilSchool of Biological Sciences, Nanyang Technological University, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plants produce a vast array of chemical compounds that we use as medicines and flavors, but these compounds' biosynthetic pathways are still poorly understood. This paucity precludes us from modifying, improving, and mass-producing these specialized metabolites in suitable bioreactors. Many of the specialized metabolites are expressed in a narrow range of organs, tissues, and cell types, suggesting a tight regulation of the responsible biosynthetic pathways. Fortunately, with unprecedented ease of generating gene expression data and with >200,000 publicly available RNA sequencing samples, we are now able to study the expression of genes from hundreds of plant species. This review demonstrates how gene expression can elucidate the biosynthetic pathways by mining organ-specific genes, gene expression clusters, and applying various types of co-expression analyses. To empower biologists to perform these analyses, we showcase these analyses using recently published, user-friendly tools. Finally, we analyze the performance of co-expression networks and show that they are a valuable addition to elucidating multiple the biosynthetic pathways of specialized metabolism.

Indexed as

clusteringco-expressionenrichmentmetabolismonlinetranscriptomics

Identifiers

PMID33510763
PMCPMC7835209

What OpenQuestion holds

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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.