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Computational identification of harmful mutation regions to the activity of transposable elements

Overview of attention for article published in BMC Genomics, November 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

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4 tweeters
wikipedia
1 Wikipedia page

Citations

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1 Dimensions

Readers on

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17 Mendeley
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Title
Computational identification of harmful mutation regions to the activity of transposable elements
Published in
BMC Genomics, November 2017
DOI 10.1186/s12864-017-4227-z
Pubmed ID
Authors

Lingling Jin, Ian McQuillan, Longhai Li

Abstract

Transposable elements (TEs) are interspersed DNA sequences that can move or copy to new positions within a genome. TEs are believed to promote speciation and their activities play a significant role in human disease. In the human genome, the 22 AluY and 6 AluS TE subfamilies have been the most recently active, and their transposition has been implicated in many inherited human diseases and in various forms of cancer. Therefore, understanding their transposition activity is very important and identifying the factors that affect their transpositional activity is of great interest. Recently, there has been some work done to quantify the activity levels of active Alu TEs based on variation in the sequence. Given this activity data, an analysis of TE activity based on the position of mutations is conducted. A method/simulation is created to computationally predict so-called harmful mutation regions in the consensus sequence of a TE; that is, mutations that occur in these regions decrease the transpositional activity dramatically. The methods are applied to the most active subfamily, AluY, to identify the harmful regions, and seven harmful regions are identified within the AluY consensus with q-values less than 0.05. A supplementary simulation also shows that the identified harmful regions covering the AluYa5 RNA functional regions are not occurring by chance. This method is then applied to two additional TE families: the Alu family and the L1 family, to computationally detect the harmful regions in these elements. We use a computational method to identify a set of harmful mutation regions. Mutations within the identified harmful regions decrease the transpositional activity of active elements. The correlation between the mutations within these regions and the transpositional activity of TEs are shown to be statistically significant. Verifications are presented using the activity of AluY elements and the secondary structure of the AluYa5 RNA, providing evidence that the method is successfully identifying harmful mutation regions.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 29%
Student > Master 5 29%
Student > Ph. D. Student 4 24%
Professor 1 6%
Student > Doctoral Student 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 35%
Computer Science 4 24%
Biochemistry, Genetics and Molecular Biology 2 12%
Unspecified 1 6%
Mathematics 1 6%
Other 3 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 16 December 2017.
All research outputs
#2,891,653
of 12,302,005 outputs
Outputs from BMC Genomics
#1,558
of 7,213 outputs
Outputs of similar age
#94,108
of 346,440 outputs
Outputs of similar age from BMC Genomics
#100
of 539 outputs
Altmetric has tracked 12,302,005 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,213 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 78% 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 346,440 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 539 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.