GLiMMPS：使用 RNA-seq 数据进行剪接调控变异的稳健统计模型。

Genome Biol. 2013 Jul 22;14(7):R74. doi: 10.1186/gb-2013-14-7-r74.

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.

为了描述选择性剪接的遗传变异，我们开发了 GLiMMPS，这是一种从 RNA-seq 数据中检测剪接数量性状基因座（sQTL）的强大统计方法。GLiMMPS 考虑了测序覆盖的个体变异和 RNA-seq 数据中普遍存在的噪声。对模拟和真实 RNA-seq 数据集的分析表明，GLiMMPS 优于竞争统计模型。对 26 个随机选择的 GLiMMPS sQTL 的定量 RT-PCR 测试的验证率为 100%。随着人群规模的 RNA-seq 研究变得越来越实惠和流行，GLiMMPS 为阐明人类和模式生物中选择性剪接的遗传变异提供了一种有用的工具。