利用群体转录组和基因组数据绘制 3'UTR 可变多聚腺苷酸化数量性状基因座。
Using population-scale transcriptomic and genomic data to map 3' UTR alternative polyadenylation quantitative trait loci.
机构信息
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.
The Gertrude H. Sergievsky Center and the Department of Neurology, Columbia University, New York, NY 10032, USA.
出版信息
STAR Protoc. 2022 Jul 19;3(3):101566. doi: 10.1016/j.xpro.2022.101566. eCollection 2022 Sep 16.
3' UTR alternative polyadenylation (APA) quantitative trait loci (3'aQTL) can explain approximately 16.1% of trait-associated non-coding variants and is largely distinct from other molecular QTLs. Here, we describe a bioinformatic protocol for identifying 3'aQTLs through standard RNA-seq and matched genomic data. This protocol allows users to analyze dynamic APA events, identify common genetic variants associated with differential 3' UTR usage, and predict the potential causal variants that affect APA. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).
3'UTR 可变多聚腺苷酸化（APA）数量性状基因座（3'aQTL）可以解释大约 16.1%的与性状相关的非编码变异，并且在很大程度上与其他分子 QTL 不同。在这里，我们描述了一种通过标准 RNA-seq 和匹配基因组数据识别 3'aQTL 的生物信息学方案。该方案允许用户分析动态 APA 事件，识别与 3'UTR 使用差异相关的常见遗传变异，并预测影响 APA 的潜在因果变异。有关此方案的使用和执行的完整详细信息，请参阅 Li 等人。（2021 年）。
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