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SHEAR: sample heterogeneity estimation and assembly by reference

Overview of attention for article published in BMC Genomics, January 2014
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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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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16 X users
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1 Google+ user

Citations

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7 Dimensions

Readers on

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55 Mendeley
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1 CiteULike
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Title
SHEAR: sample heterogeneity estimation and assembly by reference
Published in
BMC Genomics, January 2014
DOI 10.1186/1471-2164-15-84
Pubmed ID
Authors

Sean R Landman, Tae Hyun Hwang, Kevin AT Silverstein, Yingming Li, Scott M Dehm, Michael Steinbach, Vipin Kumar

Abstract

Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 7%
United Kingdom 1 2%
Netherlands 1 2%
Sweden 1 2%
Unknown 48 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 36%
Student > Ph. D. Student 13 24%
Other 4 7%
Student > Master 4 7%
Student > Bachelor 4 7%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 49%
Computer Science 7 13%
Biochemistry, Genetics and Molecular Biology 5 9%
Engineering 2 4%
Medicine and Dentistry 2 4%
Other 3 5%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 11 April 2015.
All research outputs
#3,837,763
of 25,706,302 outputs
Outputs from BMC Genomics
#1,368
of 11,305 outputs
Outputs of similar age
#43,610
of 325,141 outputs
Outputs of similar age from BMC Genomics
#26
of 207 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,305 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 87% 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 325,141 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 86% of its contemporaries.
We're also able to compare this research output to 207 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.