Gene structure-based splice variant deconvolution using a microarry platform
Gene structure-based splice variant deconvolution using a microarry platform
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DOI：
10.1093/bioinformatics/btg1044
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发表时间：
2003-07-01
期刊：
影响因子：
5.8
通讯作者：
Haussler, David
中科院分区：
文献类型：
--
作者：
Wang, Hui;Hubbell, Earl;Haussler, David
动机：选择性剪接允许单个基因产生多个mRNAs，这些mRNAs可以翻译成功能和结构不同的蛋白质。一个基因可以有多个不同浓度的变种共存。估计每个变异体的相对丰度对于研究潜在的生物功能很重要。微阵列是测量基因表达的标准工具。但大多数设计和分析都没有考虑到剪接变体。结果：受Li和Wong(2001)的启发，我们提出了一种基于基因结构的算法来确定已知剪接变体的相对丰度。使用基因结构作为约束条件，在多个实验中对探针强度进行建模。模型参数通过最大似然估计(MLE)过程/框架获得。该算法产生每个变体的相对浓度，以及与每个探针相关联的亲和项。算法的验证是通过一组受控的尖峰实验以及使用人类剪接变体阵列的内源性组织样本来执行的。
Motivation: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling.Results: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.