ulfasQTL: an ultra-fast method of composite splicing QTL analysis.
ulfasQTL: an ultra-fast method of composite splicing QTL analysis.
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DOI：
10.1186/s12864-016-3258-1
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发表时间：
2017-01-25
期刊：
影响因子：
4.4
通讯作者：
Zhang X
中科院分区：
文献类型：
--
作者：
Yang Q;Hu Y;Li J;Zhang X
选择性剪接在人类许多调节过程和疾病中起着重要作用。许多遗传变异导致基因表达和剪接的表型差异，从而决定人类性状的变化。检测影响剪接表型的遗传变异对于理解遗传变异对选择性剪接的功能影响至关重要。在许多情况下，关键表型是不同亚型的相对剪接比率，而不是单个亚型的表达值。剪接数量性状位点（Splicing quantitative trait loci, sQTL）分析方法已被提出用于检测遗传变异与基因同种异构体剪接比率载体之间的关联。我们将此任务称为复合sql分析。现有的方法计算量大，不能扩展到全基因组分析。为此，我们在sQTLseekeR方法的基础上开发了一种超快速方法ulfasQTL。该方法将多个基因剪接率的测试转换为矩阵形式，便于高效计算，因此可用于全基因组尺度的sQTL分析，速度比现有方法快数千倍。我们在GEUVADIS项目的数据上测试了ulfasQTL，并将其与现有方法进行了比较。ulfasQTL是一种非常有效的复合剪接QTL分析工具，可以在可接受的时间内应用于全基因组分析。
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.