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A Bayesian approach for estimating allele-specific expression from RNA-Seq data with diploid genomes

Overview of attention for article published in BMC Genomics, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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Title
A Bayesian approach for estimating allele-specific expression from RNA-Seq data with diploid genomes
Published in
BMC Genomics, January 2016
DOI 10.1186/s12864-015-2295-5
Pubmed ID
Authors

Naoki Nariai, Kaname Kojima, Takahiro Mimori, Yosuke Kawai, Masao Nagasaki

Abstract

RNA-sequencing (RNA-Seq) has become a popular tool for transcriptome profiling in mammals. However, accurate estimation of allele-specific expression (ASE) based on alignments of reads to the reference genome is challenging, because it contains only one allele on a mosaic haploid genome. Even with the information of diploid genome sequences, precise alignment of reads to the correct allele is difficult because of the high-similarity between the corresponding allele sequences. We propose a Bayesian approach to estimate ASE from RNA-Seq data with diploid genome sequences. In the statistical framework, the haploid choice is modeled as a hidden variable and estimated simultaneously with isoform expression levels by variational Bayesian inference. Through the simulation data analysis, we demonstrate the effectiveness of the proposed approach in terms of identifying ASE compared to the existing approach. We also show that our approach enables better quantification of isoform expression levels compared to the existing methods, TIGAR2, RSEM and Cufflinks. In the real data analysis of the human reference lymphoblastoid cell line GM12878, some autosomal genes were identified as ASE genes, and skewed paternal X-chromosome inactivation in GM12878 was identified. The proposed method, called ASE-TIGAR, enables accurate estimation of gene expression from RNA-Seq data in an allele-specific manner. Our results show the effectiveness of utilizing personal genomic information for accurate estimation of ASE. An implementation of our method is available at http://nagasakilab.csml.org/ase-tigar .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Japan 1 1%
Spain 1 1%
Germany 1 1%
Unknown 64 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 23%
Student > Ph. D. Student 14 20%
Student > Master 13 19%
Student > Bachelor 4 6%
Student > Doctoral Student 4 6%
Other 10 14%
Unknown 8 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 46%
Biochemistry, Genetics and Molecular Biology 19 28%
Medicine and Dentistry 4 6%
Immunology and Microbiology 2 3%
Computer Science 1 1%
Other 2 3%
Unknown 9 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 February 2016.
All research outputs
#3,674,561
of 22,837,982 outputs
Outputs from BMC Genomics
#1,436
of 10,655 outputs
Outputs of similar age
#64,531
of 394,936 outputs
Outputs of similar age from BMC Genomics
#30
of 243 outputs
Altmetric has tracked 22,837,982 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 86% 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 394,936 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 83% of its contemporaries.
We're also able to compare this research output to 243 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.