Evidence map›Paper›PMID 23320104›Full record

ArticlePloS one2013

Spatial promoter recognition signatures may enhance transcription factor specificity in yeast.

Richard W Lusk, Michael B Eisen

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
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 1 country.

Richard W LuskDepartment of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America.
Michael B Eisen
Howard Hughes Medical Institute · USUniversity of Michigan–Ann Arbor · US

Funding

Modeling the Evolution and Function of Drosophila cis-Regulatory ModulesR01HG002779 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI EISEN, MICHAEL BRUCE · 2003 to 2012
$5.3M
Howard Hughes Medical InstituteNHGRI NIH HHS HG002779NHGRI NIH HHS R01 HG002779
6 · The paper itself

Abstract

The short length and high degeneracy of sites recognized by DNA-binding transcription factors limit the amount of information they can carry, and individual sites are rarely sufficient to mediate the regulation of specific targets. Computational analysis of microbial genomes has suggested that many factors function optimally when in a particular orientation and position with respect to their target promoters. To investigate this further, we developed and trained spatial models of binding site positioning and applied them to the genome of the yeast Saccharomyces cerevisiae. We found evidence of non-random organization of sites within promoters, differences in binding site density, or both for thirty-eight transcription factors. We show that these signatures allow transcription factors with substantial differences in binding site specificity to share similar promoter specificities. We illustrate how spatial information dictating the positioning and density of binding sites can in principle increase the information available to the organism for differentiating a transcription factor's true targets, and we indicate how this information could potentially be leveraged for the same purpose in bioinformatic analyses.

Indexed as

Promoter Regions, GeneticAlgorithmsBinding SitesDNA, FungalGene DeletionGenes, FungalMarkov ChainsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsTranscription FactorsUp-RegulationDNA, FungalSaccharomyces cerevisiae ProteinsTranscription Factors

Identifiers

PMID23320104
PMCPMC3540036
OpenAlexW2050794250

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.