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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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 43 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 33% |
| Student > Master | 6 | 14% |
| Researcher | 5 | 12% |
| Student > Doctoral Student | 4 | 9% |
| Student > Bachelor | 4 | 9% |
| Other | 6 | 14% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 19 | 44% |
| Agricultural and Biological Sciences | 12 | 28% |
| Computer Science | 2 | 5% |
| Mathematics | 1 | 2% |
| Business, Management and Accounting | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 5 | 12% |
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
#18,529,032
of 22,950,943 outputs
Outputs from BMC Genomics
#8,215
of 10,681 outputs
Outputs of similar age
#309,801
of 419,016 outputs
Outputs of similar age from BMC Genomics
#143
of 210 outputs
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