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A web application for the unspecific detection of differentially expressed DNA regions in strand-specific expression data: Fig. 1.

Overview of attention for article published in Bioinformatics, June 2015
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Title
A web application for the unspecific detection of differentially expressed DNA regions in strand-specific expression data: Fig. 1.
Published in
Bioinformatics, June 2015
DOI 10.1093/bioinformatics/btv343
Pubmed ID
Authors

José M. Juanes, Ana Miguel, Lucas J. Morales, José E. Pérez-Ortín, Vicente Arnau

Abstract

Genomic technologies allow laboratories to produce large-scale data sets, either through the use of next-generation sequencing or microarray platforms. To explore these data sets and obtain maximum value from the data, researchers view their results alongside all the known features of a given reference genome. To study transcriptional changes that occur under a given condition, researchers search for regions of the genome that are differentially expressed between different experimental conditions. In order to identify these regions several algorithms have been developed over the years, along with some bioinformatic platforms that enable their use. However, currently available applications for comparative microarray analysis exclusively focus on changes in gene expression within known transcribed regions of predicted protein-coding genes, the changes that occur in non-predictable genetic elements, such as non-coding RNAs. Here, we present a web application for the visualization of strand-specific tiling microarray or next-generation sequencing data that allows customized detection of differentially expressed regions all along the genome in an unspecific manner, that allows identification of all RNA sequences, predictable or not. The web application is freely accessible at http://tilingscan.uv.es/ IMPLEMENTATION: TilingScan is implemented in PHP and JavaScript. vicente.arnau@uv.esSupplementary material available at: xxxxxxx.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Student > Bachelor 1 13%
Lecturer 1 13%
Professor 1 13%
Student > Master 1 13%
Other 2 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 50%
Agricultural and Biological Sciences 4 50%

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 21 September 2015.
All research outputs
#9,880,987
of 12,378,687 outputs
Outputs from Bioinformatics
#7,402
of 8,175 outputs
Outputs of similar age
#173,110
of 252,965 outputs
Outputs of similar age from Bioinformatics
#249
of 256 outputs
Altmetric has tracked 12,378,687 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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