Evidence map›Paper›PMID 21172036›Full record

ArticleBMC bioinformatics2010

Computing DNA duplex instability profiles efficiently with a two-state model: trends of promoters and binding sites.

Miriam R Kantorovitz, Zoi Rapti, Vladimir Gelev, Anny Usheva

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 87% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

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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

4 authors at 3 institutions in 1 country.

Miriam R KantorovitzDepartment of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA. ruth@math.uiuc.edu
Zoi Rapti
Vladimir Gelev
Anny Usheva
University of Illinois Urbana-Champaign · USBeth Israel Deaconess Medical Center · USHarvard University · US

Funding

YY1 FUNCTIONS IN VASCULAR SMOOTH MUSCLE CELLSR01HL062458 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI USHEVA-SIMIDJIYSKA, ANNY · 2000 to 2008
$2.6M
Promoter Prediction Using the Opening Profile of DNAR01GM073911 · NIGMS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI USHEVA-SIMIDJIYSKA, ANNY · 2005 to 2009
$1.3M
NHLBI NIH HHS R01 HL062458NIGMS NIH HHS GM073911NIGMS NIH HHS R01 GM073911
6 · The paper itself

Abstract

backgroundDNA instability profiles have been used recently for predicting the transcriptional start site and the location of core promoters, and to gain insight into promoter action. It was also shown that the use of these profiles can significantly improve the performance of motif finding programs.

resultsIn this work we introduce a new method for computing DNA instability profiles. The model that we use is a modified Ising-type model and it is implemented via statistical mechanics. Our linear time algorithm computes the profile of a 10,000 base-pair long sequence in less than one second. The method we use also allows the computation of the probability that several consecutive bases are unpaired simultaneously. This is a feature that is not available in other linear-time algorithms. We use the model to compare the thermodynamic trends of promoter sequences of several genomes. In addition, we report results that associate the location of local extrema in the instability profiles with the presence of core promoter elements at these locations and with the location of the transcription start sites (TSS). We also analyzed the instability scores of binding sites of several human core promoter elements. We show that the instability scores of functional binding sites of a given core promoter element are significantly different than the scores of sites with the same motif occurring outside the functional range (relative to the TSS).

conclusionsThe time efficiency of the algorithm and its genome-wide applications makes this work of broad interest to scientists interested in transcriptional regulation, motif discovery, and comparative genomics.

Indexed as

Promoter Regions, GeneticAlgorithmsBinding SitesComputational BiologyDNAHumansModels, StatisticalNucleic Acid DenaturationTranscription Initiation SiteDNA

Identifiers

PMID21172036
PMCPMC3018474
OpenAlexW2107932587

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.