Evidence map›Paper›PMID 21543443›Full record

ArticleBioinformatics (Oxford, England)2011

Improved similarity scores for comparing motifs.

Emi Tanaka, Timothy Bailey, Charles E Grant, William Stafford Noble, Uri Keich

Abstract readEvaluation Study
In one paragraph

Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Dissecting thebioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

5 authors.

Emi TanakaSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW Australia. e.tanaka@maths.usyd.edu.au
Timothy Bailey
Charles E Grant
William Stafford Noble
Uri Keich

Funding

The MEME suite of motif-based sequence analysis toolsR01RR021692 · NCRR · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD · 2005 to 2011
$2.1M
NCRR NIH HHS R01 RR021692
6 · The paper itself

Abstract

motivationA question that often comes up after applying a motif finder to a set of co-regulated DNA sequences is whether the reported putative motif is similar to any known motif. While several tools have been designed for this task, Habib et al. pointed out that the scores that are commonly used for measuring similarity between motifs do not distinguish between a good alignment of two informative columns (say, all-A) and one of two uninformative columns. This observation explains why tools such as Tomtom occasionally return an alignment of uninformative columns which is clearly spurious. To address this problem, Habib et al. suggested a new score [Bayesian Likelihood 2-Component (BLiC)] which uses a Bayesian information criterion to penalize matches that are also similar to the background distribution.

resultsWe show that the BLiC score exhibits other, highly undesirable properties, and we offer instead a general approach to adjust any motif similarity score so as to reduce the number of reported spurious alignments of uninformative columns. We implement our method in Tomtom and show that, without significantly compromising Tomtom's retrieval accuracy or its runtime, we can drastically reduce the number of uninformative alignments. AVAILABILITY AND IMPLEMENTATION: The modified Tomtom is available as part of the MEME Suite at http://meme.nbcr.net.

Indexed as

Sequence Analysis, DNAAlgorithmsBayes TheoremSequence AlignmentSoftware

Identifiers

PMID21543443
PMCPMC3106196

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.