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HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

Overview of attention for article published in Genome Medicine, September 2017
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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 (81st percentile)

Mentioned by

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18 tweeters
facebook
1 Facebook page

Citations

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26 Dimensions

Readers on

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46 Mendeley
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Title
HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data
Published in
Genome Medicine, September 2017
DOI 10.1186/s13073-017-0473-6
Pubmed ID
Authors

Martin L. Buchkovich, Chad C. Brown, Kimberly Robasky, Shengjie Chai, Sharon Westfall, Benjamin G. Vincent, Eric T. Weimer, Jason G. Powers

Abstract

The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. We created HLAProfiler ( https://github.com/ExpressionAnalysis/HLAProfiler ), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/ .

Twitter Demographics

The data shown below were collected from the profiles of 18 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 24%
Student > Bachelor 8 17%
Student > Ph. D. Student 7 15%
Student > Postgraduate 4 9%
Other 3 7%
Other 5 11%
Unknown 8 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 17%
Biochemistry, Genetics and Molecular Biology 8 17%
Computer Science 6 13%
Medicine and Dentistry 6 13%
Immunology and Microbiology 4 9%
Other 6 13%
Unknown 8 17%

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 26 September 2018.
All research outputs
#2,806,668
of 21,192,559 outputs
Outputs from Genome Medicine
#613
of 1,347 outputs
Outputs of similar age
#54,226
of 295,622 outputs
Outputs of similar age from Genome Medicine
#4
of 4 outputs
Altmetric has tracked 21,192,559 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,347 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.6. This one has gotten more attention than average, scoring higher than 54% 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 295,622 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 81% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.