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cisExpress: motif detection in DNA sequences

Overview of attention for article published in Bioinformatics, June 2013
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Article details
Title
cisExpress: motif detection in DNA sequences
Published in
Bioinformatics, June 2013
DOI 10.1093/bioinformatics/btt366
Pubmed ID
Authors

Martin Triska, David Grocutt, James Southern, Denis J. Murphy, Tatiana Tatarinova

Abstract

One of the major challenges for contemporary bioinformatics is the analysis and accurate annotation of genomic datasets to enable extraction of useful information about the functional role of DNA sequences. This article describes a novel genome-wide statistical approach to the detection of specific DNA sequence motifs based on similarities between the promoters of similarly expressed genes. This new tool, cisExpress, is especially designed for use with large datasets, such as those generated by publicly accessible whole genome and transcriptome projects. cisExpress uses a task farming algorithm to exploit all available computational cores within a shared memory node. We demonstrate the robust nature and validity of the proposed method. It is applicable for use with a wide range of genomic databases for any species of interest.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 3%
Netherlands 1 3%
United Kingdom 1 3%
France 1 3%
Spain 1 3%
Unknown 31 86%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 11 31%
Researcher 11 31%
Lecturer 3 8%
Professor 3 8%
Professor > Associate Professor 3 8%
Other 2 6%
Unknown 3 8%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 18 50%
Biochemistry, Genetics and Molecular Biology 9 25%
Mathematics 2 6%
Computer Science 2 6%
Social Sciences 1 3%
Other 0 0%
Unknown 4 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 June 2013.
All research outputs
#22,758,309
of 25,371,288 outputs
Outputs from Bioinformatics
#12,334
of 12,808 outputs
Outputs of similar age
#185,465
of 209,398 outputs
Outputs of similar age from Bioinformatics
#180
of 197 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 209,398 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 197 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.