Evidence map›Paper›PMID 21487095›Full record

ArticleThe Plant cell2011

Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.

Thomas Lingner, Amr R Kataya, Gerardo E Antonicelli, Aline Benichou, Kjersti Nilssen, Xiong-Yan Chen, Tanja Siemsen, Burkhard Morgenstern, Peter Meinicke, Sigrun Reumann

Open access · greenAbstract read
In one paragraph

Article in The Plant cell, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
4.9field-weighted citation impact, top 4% 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

62 citing papers in PubMed, 124 citations in OpenAlex.

  1. Building peroxisomes: perspectives on plant peroxins.Biochemical Society transactions · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Analysis of Yeast Peroxisomes via Spatial Proteomics.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  11. Article
  12. Genome-Wide Identification of Polyamine Oxidase (PAO) Family Genes: Roles ofInternational journal of molecular sciences · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Translating theFrontiers in cell and developmental biology · 2022
    Review
  18. Article
  19. Peroxisomal Proteome Mining of Sweet Pepper (Frontiers in plant science · 2022
    Article
  20. Review

2 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

10 authors at 2 institutions in 2 countries.

Thomas LingnerGeorg-August University of Goettingen, Institute for Microbiology, Department of Bioinformatics, D-37077 Goettingen, Germany.
Amr R Kataya
Gerardo E Antonicelli
Aline Benichou
Kjersti Nilssen
Xiong-Yan Chen
Tanja Siemsen
Burkhard Morgenstern
Peter Meinicke
Sigrun Reumann
University of Göttingen · DEUniversity of Stavanger · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

Indexed as

Artificial IntelligenceProtein Sorting SignalsAmino Acid SequenceArabidopsisArabidopsis ProteinsComputational BiologyDatabases, ProteinGenome, PlantModels, BiologicalMolecular Sequence DataPeptidesPeroxisomesProtein TransportReproducibility of ResultsSubcellular FractionsArabidopsis ProteinsPeptidesProtein Sorting Signals

Identifiers

PMID21487095
PMCPMC3101550
OpenAlexW2141512206

What OpenQuestion holds

Textmetadata
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.