Shai Ofer, Morris Quaid D, Blencowe Benjamin J, Frey Brendan J
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada M5S 3G8.
Bioinformatics. 2006 Mar 1;22(5):606-13. doi: 10.1093/bioinformatics/btk028. Epub 2006 Jan 10.
Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organism's proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues.
GenASAP was applied to data obtained from a custom microarray designed for the monitoring of 3126 AS events in mouse cells and tissues. The microarray design included probes specific for exon body and junction sequences formed by the splicing of exons. Our results show that GenASAP provides accurate predictions for over one-third of the total events, as verified by independent RT-PCR assays.
可变剪接（Alternative splicing，AS）是后生动物基因表达中的常见步骤，通过该步骤，基因的外显子以不同组合进行剪接，从而产生多种成熟mRNA的异构体。可变剪接的作用是增加生物体蛋白质组的复杂性并调节基因表达。尽管其很重要，但可变剪接及其调控的潜在机制尚未得到充分理解，尤其是在全局基因表达模式的背景下。我们在此提出一种称为可变剪接阵列平台生成模型（Generative model for the Alternative Splicing Array Platform，GenASAP）的算法，该算法可以使用从定制微阵列生成的数据预测数千个外显子跳跃事件的可变剪接水平。GenASAP在无监督概率模型中使用贝叶斯学习，以从微阵列数据中准确预测可变剪接水平。GenASAP能够学习微阵列数据的杂交图谱，同时对噪声过程以及缺失或异常数据进行建模。GenASAP已成功应用于哺乳动物细胞和组织中可变剪接的全局发现和分析。
GenASAP应用于从为监测小鼠细胞和组织中的3126个可变剪接事件而设计的定制微阵列获得的数据。该微阵列设计包括针对外显子体以及由外显子剪接形成的连接序列的特异性探针。我们的结果表明，经独立的逆转录-聚合酶链反应（RT-PCR）分析验证，GenASAP对超过三分之一的总事件提供了准确的预测。
