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A haplotype-based normalization technique for the analysis and detection of allele specific expression

Overview of attention for article published in BMC Bioinformatics, September 2016
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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 (76th percentile)

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8 X users
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1 patent

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Title
A haplotype-based normalization technique for the analysis and detection of allele specific expression
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1238-8
Pubmed ID
Authors

Alan Hodgkinson, Jean-Christophe Grenier, Elias Gbeha, Philip Awadalla

Abstract

Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects. Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking. The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 25%
Student > Ph. D. Student 7 16%
Student > Bachelor 4 9%
Professor 4 9%
Student > Master 3 7%
Other 7 16%
Unknown 8 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 30%
Biochemistry, Genetics and Molecular Biology 11 25%
Computer Science 6 14%
Medicine and Dentistry 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 9%
Unknown 7 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 05 March 2020.
All research outputs
#4,339,171
of 23,798,792 outputs
Outputs from BMC Bioinformatics
#1,626
of 7,444 outputs
Outputs of similar age
#69,307
of 324,604 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 121 outputs
Altmetric has tracked 23,798,792 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,444 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 78% 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 324,604 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 121 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.