Evidence map›Paper›PMID 33868595›Full record

ReviewComputational and structural biotechnology journal2021

Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants?

Federico Scossa, Alisdair R Fernie

Open access · goldAbstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

12 citing papers in PubMed, 17 citations in OpenAlex.

  1. Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026
    Review
  2. Article
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  5. Applications of ancestral sequence reconstruction for understanding the evolution of plant specialized metabolism.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Review
  6. Review
  7. Article
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  9. Review
  10. Article
  11. Review
  12. Ancestral Sequence Reconstruction for Exploring Alkaloid Evolution.Methods in molecular biology (Clifton, N.J.) · 2022
    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

2 authors at 2 institutions in 3 countries.

Federico ScossaMax-Planck-Institute of Molecular Plant Physiology (MPI-MP), 14476 Potsdam-Golm, Germany.
Alisdair R FernieMax-Planck-Institute of Molecular Plant Physiology (MPI-MP), 14476 Potsdam-Golm, Germany.
Center of Plant Systems Biology and Biotechnology · BGCereal Research Centre · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whilst substantial research effort has been placed on understanding the interactions of plant proteins with their molecular partners, relatively few studies in plants - by contrast to work in other organisms - address how these interactions evolve. It is thought that ancestral proteins were more promiscuous than modern proteins and that specificity often evolved following gene duplication and subsequent functional refining. However, ancestral protein resurrection studies have found that some modern proteins have evolved

Indexed as

Ancestral sequence reconstructionAPR, ancestral protein resurrectionASR, ancestral sequence reconstructionCDS, coding sequenceEvolutionGenomicsGR, glucocorticoid receptorGWAS, genome wide association studyInDel, insertion/deletionMCMC, Markov Chain Monte CarloMetabolismML, maximum likelihoodMP, maximum parsimonyMR, mineralcorticoid receptorMSA, multiple sequence alignmentNJ, neighbor-joiningPhylogeneticsPlantsSFS, site frequency spectrum

Identifiers

PMID33868595
PMCPMC8039532
OpenAlexW3138062293

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.