GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

被引：51

作者：

Zhao, Keyan ^{[1
,2
]}

Lu, Zhi-xiang ^{[1
,2
]}

Park, Juw Won ^{[1
,2
]}

Zhou, Qing ^{[3
]}

Xing, Yi ^{[1
,2
]}

机构：

[1] Univ Calif Los Angeles, Dept Microbiol Immunol & Mol Genet, Los Angeles, CA 90095 USA

[2] Univ Iowa, Dept Internal Med, Iowa City, IA 52242 USA

[3] Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA

来源：

GENOME BIOLOGY | 2013年 / 14卷 / 07期

关键词：

RNA-seq; alternative splicing; sQTL; exon; generalized linear mixed model; GENOME-WIDE ASSOCIATION; DIFFERENTIAL EXPRESSION ANALYSIS; GENE-EXPRESSION; VARIANTS; DISEASE; LOCI; SUSCEPTIBILITY; IDENTIFICATION; TRANSCRIPTOME; POLYMORPHISMS;

D O I：

10.1186/gb-2013-14-7-r74

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.

引用

页数：15

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