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AllelicImbalance: an R/ bioconductor package for detecting, managing, and visualizing allele expression imbalance data from RNA sequencing

Overview of attention for article published in BMC Bioinformatics, June 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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Title
AllelicImbalance: an R/ bioconductor package for detecting, managing, and visualizing allele expression imbalance data from RNA sequencing
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0620-2
Pubmed ID
Authors

Jesper R. Gådin, Ferdinand M. van’t Hooft, Per Eriksson, Lasse Folkersen

Abstract

One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases. We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons. The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United States 1 2%
Sweden 1 2%
Germany 1 2%
Unknown 54 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 29%
Student > Ph. D. Student 10 17%
Student > Postgraduate 5 9%
Student > Master 5 9%
Student > Bachelor 3 5%
Other 12 21%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 34%
Biochemistry, Genetics and Molecular Biology 19 33%
Computer Science 4 7%
Medicine and Dentistry 3 5%
Veterinary Science and Veterinary Medicine 2 3%
Other 3 5%
Unknown 7 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 August 2018.
All research outputs
#4,492,400
of 22,811,321 outputs
Outputs from BMC Bioinformatics
#1,694
of 7,284 outputs
Outputs of similar age
#57,012
of 264,930 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 114 outputs
Altmetric has tracked 22,811,321 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 76% 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 264,930 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 78% of its contemporaries.
We're also able to compare this research output to 114 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 67% of its contemporaries.