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

ABSTRACT: 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.

SUBMITTER: Zhao K 

PROVIDER: S-EPMC4054007 | biostudies-other | 2013 Jul

REPOSITORIES: biostudies-other

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

Zhao Keyan K   Lu Zhi-xiang ZX   Park Juw Won JW   Zhou Qing Q   Xing Yi Y  

Genome biology 20130722 7

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  ...[more]