GLiMMPS: Robust statistical model for regulatory variation of alternative splicing using RNA-Seq data

To characterize the genetic variation of alternative splicing, researchers at UCLA have developed 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 data sets 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.

RNA-Seq

Availability – Source code and Supplemental data related to the GLiMMPS manuscript is available at: http://www.mimg.ucla.edu/faculty/xing/glimmps/

Contact – yxing@ucla.edu

  • Zhao K, Lu ZX, Park JW, Zhou Q, Xing Y. (2013) GLiMMPS: Robust statistical model for regulatory variation of alternative splicing using RNA-Seq data. Genome Biol 14(7), R74. [abstract]