Evidence map›Paper›PMID 18793470›Full record

ArticleBMC bioinformatics2008

MTAP: the motif tool assessment platform.

Daniel Quest, Kathryn Dempsey, Mohammad Shafiullah, Dhundy Bastola, Hesham Ali

Abstract read
In one paragraph

Article in BMC bioinformatics, 2008. 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
–field-weighted citation impact
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

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.

  1. Article
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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

5 authors.

Daniel QuestCollege of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu
Kathryn Dempsey
Mohammad Shafiullah
Dhundy Bastola
Hesham Ali

Funding

ZINC FINGER-INSPIRED FLUORESCENT CHEMOSENSORSP20RR016469 · NCRR · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI TURPEN, JAMES B · 2001 to 2011
$36.2M
NCRR NIH HHS P20 RR016469NCRR NIH HHS P20RR16469
6 · The paper itself

Abstract

backgroundIn recent years, substantial effort has been applied to de novo regulatory motif discovery. At this time, more than 150 software tools exist to detect regulatory binding sites given a set of genomic sequences. As the number of software packages increases, it becomes more important to identify the tools with the best performance characteristics for specific problem domains. Identifying the correct tool is difficult because of the great variability in motif detection software. Consequently, many labs spend considerable effort testing methods to find one that works well in their problem of interest.

resultsIn this work, we propose a method (MTAP) that substantially reduces the effort required to assess de novo regulatory motif discovery software. MTAP differs from previous attempts at regulatory motif assessment in that it automates motif discovery tool pipelines (something that traditionally required many manual steps), automatically constructs orthologous upstream sequences, and provides automated benchmarks for many popular tools. As a proof of concept, we have run benchmarks over human, mouse, fly, yeast, E. coli and B. subtilis.

conclusionMTAP presents a new approach to the challenging problem of assessing regulatory motif discovery methods. The most current version of MTAP can be downloaded from http://biobase.ist.unomaha.edu/

Indexed as

AlgorithmsSoftwareBase SequenceMolecular Sequence DataRegulatory Sequences, Nucleic AcidSequence Analysis, DNA

Identifiers

PMID18793470
PMCPMC2537577

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Registered trials

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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.