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Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data

Overview of attention for article published in PeerJ, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

blogs
1 blog
twitter
8 X users
facebook
1 Facebook page

Citations

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9 Dimensions

Readers on

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47 Mendeley
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Title
Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data
Published in
PeerJ, November 2015
DOI 10.7717/peerj.1419
Pubmed ID
Authors

Jose E. Kroll, Jihoon Kim, Lucila Ohno-Machado, Sandro J. de Souza

Abstract

Motivation. Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background. Results. A software suite named Splicing Express was created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills. Splicing Express performs automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool named Splooce. As a final result, Splicing Express creates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show that Splicing Express is able to perform all tasks in a straightforward way, identifying well-known specific events. Availability and Implementation. Splicing Express is written in Perl and is suitable to run only in UNIX-like systems. More details can be found at: http://www.bioinformatics-brazil.org/splicingexpress.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Japan 1 2%
Spain 1 2%
Luxembourg 1 2%
Unknown 42 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 32%
Student > Ph. D. Student 13 28%
Student > Master 6 13%
Student > Postgraduate 4 9%
Other 3 6%
Other 5 11%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 45%
Biochemistry, Genetics and Molecular Biology 13 28%
Computer Science 6 13%
Engineering 3 6%
Veterinary Science and Veterinary Medicine 1 2%
Other 2 4%
Unknown 1 2%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 10 December 2015.
All research outputs
#2,751,130
of 22,833,393 outputs
Outputs from PeerJ
#3,003
of 13,268 outputs
Outputs of similar age
#47,581
of 386,484 outputs
Outputs of similar age from PeerJ
#60
of 219 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,268 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.4. This one has done well, scoring higher than 77% of its peers.
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 386,484 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 219 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.