Evidence map›Paper›PMID 16855291›Full record

ArticleNucleic acids research2006

SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent.

Norman E Davey, Denis C Shields, Richard J Edwards

Abstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Article
  2. Article
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  4. Article
  5. Review
  6. The Australasian dingo archetype:bioRxiv : the preprint server for biology · 2023
    Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Intrinsically disordered domains: Sequence ➔ disorder ➔ function relationships.Protein science : a publication of the Protein Society · 2019
    Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Computational identification of MoRFs in protein sequences.Bioinformatics (Oxford, England) · 2015
    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.

Norman E DaveyConway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland.
Denis C Shields
Richard J Edwards

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many important interactions of proteins are facilitated by short, linear motifs (SLiMs) within a protein's primary sequence. Our aim was to establish robust methods for discovering putative functional motifs. The strongest evidence for such motifs is obtained when the same motifs occur in unrelated proteins, evolving by convergence. In practise, searches for such motifs are often swamped by motifs shared in related proteins that are identical by descent. Prediction of motifs among sets of biologically related proteins, including those both with and without detectable similarity, were made using the TEIRESIAS algorithm. The number of motif occurrences arising through common evolutionary descent were normalized based on treatment of BLAST local alignments. Motifs were ranked according to a score derived from the product of the normalized number of occurrences and the information content. The method was shown to significantly outperform methods that do not discount evolutionary relatedness, when applied to known SLiMs from a subset of the eukaryotic linear motif (ELM) database. An implementation of Multiple Spanning Tree weighting outperformed two other weighting schemes, in a variety of settings.

Indexed as

Amino Acid MotifsSoftwareEvolution, MolecularHumansProteinsSequence Analysis, ProteinProteins

Identifiers

PMID16855291
PMCPMC1524906

What OpenQuestion holds

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LicenceCC BY-NC
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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.