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ulfasQTL: an ultra-fast method of composite splicing QTL analysis

Overview of attention for article published in BMC Genomics, January 2017
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Title
ulfasQTL: an ultra-fast method of composite splicing QTL analysis
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3258-1
Pubmed ID
Authors

Qian Yang, Yue Hu, Jun Li, Xuegong Zhang

Abstract

Alternative splicing plays important roles in many regulatory processes and diseases in human. Many genetic variants contribute to phenotypic differences in gene expression and splicing that determine variations in human traits. Detecting genetic variants that affect splicing phenotypes is essential for understanding the functional impact of genetic variations on alternative splicing. For many situations, the key phenotype is the relative splicing ratios of alternative isoforms rather than the expression values of individual isoforms. Splicing quantitative trait loci (sQTL) analysis methods have been proposed for detecting associations of genetic variants with the vectors of isoform splicing ratios of genes. We call this task as composite sQTL analysis. Existing methods are computationally intensive and cannot scale up for whole genome analysis. We developed an ultra-fast method named ulfasQTL for this task based on a previous method sQTLseekeR. It transforms tests of splicing ratios of multiple genes to a matrix form for efficient computation, and therefore can be applied for sQTL analysis at whole-genome scales at the speed thousands times faster than the existing method. We tested ulfasQTL on the data from the GEUVADIS project and compared it with an existing method. ulfasQTL is a very efficient tool for composite splicing QTL analysis and can be applied on whole-genome analysis with acceptable time.

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The data shown below were collected from the profile of 1 tweeter 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 37%
Researcher 5 14%
Student > Master 4 11%
Student > Bachelor 3 9%
Student > Doctoral Student 2 6%
Other 6 17%
Unknown 2 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 49%
Agricultural and Biological Sciences 9 26%
Computer Science 2 6%
Unspecified 1 3%
Mathematics 1 3%
Other 2 6%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 February 2017.
All research outputs
#9,034,878
of 11,293,566 outputs
Outputs from BMC Genomics
#5,305
of 6,784 outputs
Outputs of similar age
#226,959
of 319,963 outputs
Outputs of similar age from BMC Genomics
#121
of 163 outputs
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