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Entropy Measures Quantify Global Splicing Disorders in Cancer

Overview of attention for article published in PLoS Computational Biology, March 2008
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Title
Entropy Measures Quantify Global Splicing Disorders in Cancer
Published in
PLoS Computational Biology, March 2008
DOI 10.1371/journal.pcbi.1000011
Pubmed ID
Authors

William Ritchie, Samuel Granjeaud, Denis Puthier, Daniel Gautheret

Abstract

Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 8 10%
United Kingdom 1 1%
Germany 1 1%
France 1 1%
Unknown 71 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 30%
Student > Ph. D. Student 17 21%
Professor > Associate Professor 8 10%
Student > Master 7 9%
Student > Doctoral Student 6 7%
Other 15 18%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 50%
Biochemistry, Genetics and Molecular Biology 22 27%
Computer Science 5 6%
Medicine and Dentistry 4 5%
Mathematics 2 2%
Other 3 4%
Unknown 5 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 12 May 2015.
All research outputs
#16,345,315
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#7,027
of 9,043 outputs
Outputs of similar age
#81,612
of 96,239 outputs
Outputs of similar age from PLoS Computational Biology
#35
of 43 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.