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Analyzing allele specific RNA expression using mixture models

Overview of attention for article published in BMC Genomics, August 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 (76th percentile)

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13 tweeters

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Title
Analyzing allele specific RNA expression using mixture models
Published in
BMC Genomics, August 2015
DOI 10.1186/s12864-015-1749-0
Pubmed ID
Authors

Rong Lu, Ryan M Smith, Michal Seweryn, Danxin Wang, Katherine Hartmann, Amy Webb, Wolfgang Sadee, Grzegorz A Rempala

Abstract

Measuring allele-specific RNA expression provides valuable insights into cis-acting genetic and epigenetic regulation of gene expression. Widespread adoption of high-throughput sequencing technologies for studying RNA expression (RNA-Seq) permits measurement of allelic RNA expression imbalance (AEI) at heterozygous single nucleotide polymorphisms (SNPs) across the entire transcriptome, and this approach has become especially popular with the emergence of large databases, such as GTEx. However, the existing binomial-type methods used to model allelic expression from RNA-seq assume a strong negative correlation between reference and variant allele reads, which may not be reasonable biologically. Here we propose a new strategy for AEI analysis using RNA-seq data. Under the null hypothesis of no AEI, a group of SNPs (possibly across multiple genes) is considered comparable if their respective total sums of the allelic reads are of similar magnitude. Within each group of "comparable" SNPs, we identify SNPs with AEI signal by fitting a mixture of folded Skellam distributions to the absolute values of read differences. By applying this methodology to RNA-Seq data from human autopsy brain tissues, we identified numerous instances of moderate to strong imbalanced allelic RNA expression at heterozygous SNPs. Findings with SLC1A3 mRNA exhibiting known expression differences are discussed as examples. The folded Skellam mixture model searches for SNPs with significant difference between reference and variant allele reads (adjusted for different library sizes), using information from a group of "comparable" SNPs across multiple genes. This model is particularly suitable for performing AEI analysis on genes with few heterozygous SNPs available from RNA-seq, and it can fit over-dispersed read counts without specifying the direction of the correlation between reference and variant alleles.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 41 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%
Unknown 39 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 34%
Researcher 7 17%
Student > Master 5 12%
Student > Bachelor 2 5%
Other 2 5%
Other 5 12%
Unknown 6 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 51%
Biochemistry, Genetics and Molecular Biology 7 17%
Computer Science 3 7%
Mathematics 2 5%
Business, Management and Accounting 1 2%
Other 2 5%
Unknown 5 12%

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 19 January 2016.
All research outputs
#3,029,634
of 12,680,068 outputs
Outputs from BMC Genomics
#1,620
of 7,469 outputs
Outputs of similar age
#55,470
of 233,721 outputs
Outputs of similar age from BMC Genomics
#1
of 3 outputs
Altmetric has tracked 12,680,068 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,469 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.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them