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GLiMMPS | Statistical model for regulatory variation of alternative splicing using RNA-seq data

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. 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. GLiMMPS provides a useful tool for genome-wide identification of sQTLs from population-scale RNA-seq datasets.

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GLiMMPS classification

GLiMMPS specifications

Unique identifier:
Command line interface
Operating system:
Computer skills:
lme4, MASS
Software type:
Restrictions to use:
Programming languages:
Perl, Python, R
Source code URL:

GLiMMPS distribution


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GLiMMPS support



  • Yi Xing <>


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Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA; Department of Internal Medicine, University of Iowa, Iowa City, IA, USA; Department of Statistics, University of California, Los Angeles, CA, USA

Funding source(s)

NIH grant R01GM088342, Burroughs Wellcome Fund grant 1008841.01, a March of Dimes Foundation Basil O’Connor Starter Scholar Research Award #5-FY10-60, and an NIH T32 postdoctoral fellow training grant T32HL007638

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