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CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements

Overview of attention for article published in BMC Bioinformatics, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

blogs
2 blogs
twitter
15 tweeters

Citations

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3 Dimensions

Readers on

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26 Mendeley
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Title
CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0698-6
Pubmed ID
Authors

Eman Badr, Lenwood S. Heath

Abstract

Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches. We identified 37 SRE sets that include both enhancer and silencer elements. We show that our results intersect with previous results, including some that are experimental. We also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. We applied CoSREM to RNA-Seq data for multiple tissues to identify combinatorial SREs which may be responsible for exon inclusion or exclusion across tissues. The new algorithm can identify different combinations of splicing enhancers and silencers without assuming a predefined size or limiting the algorithm to find only pairs of SREs. Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 4%
Germany 1 4%
Brazil 1 4%
Unknown 23 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 27%
Researcher 6 23%
Student > Master 4 15%
Student > Postgraduate 3 12%
Professor > Associate Professor 2 8%
Other 3 12%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 31%
Computer Science 6 23%
Agricultural and Biological Sciences 5 19%
Engineering 2 8%
Unknown 5 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 03 March 2016.
All research outputs
#1,056,888
of 15,083,454 outputs
Outputs from BMC Bioinformatics
#284
of 5,554 outputs
Outputs of similar age
#21,804
of 240,354 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 2 outputs
Altmetric has tracked 15,083,454 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,554 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 94% 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 240,354 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them