Modeling mRNA splicing by exon definition in silico

解释基因变异的影响是破译全基因组序列中最具挑战性和最重要的问题之一。我的实验室开发了生物信息学理论、软件和应用程序来理解和预测DNA序列变化的后果。该框架使用信息理论将蛋白质与DNA或RNA之间相互作用强度的差异与它们对基因表达的影响联系起来。信息论可以用来检测和量化细胞中被蛋白质识别和结合的DNA或RNA中的信号。该项目将开发和评估自然细胞过程的数学模型，以从未加工的RNA转录本序列中识别基因的基本蛋白质编码单元，称为外显子。这些转录本的正确加工称为mRNA剪接。我们将使用信息论来定义这些信号，然后结合单个外显子内的多个信号来区分和量化正确处理的mRNA转录物和潜在的诱饵。这些预测将与已发表的表达基因序列数据库进行比较，以确定这些模型的准确性。在此开发的模型、方法和软件将有助于预测人类和其他物种的基因表达模式。这些资源最终将用于预测影响正常mRNA剪接的常见序列变异的研究。

Interpretation of the effects of variation in genes is among the most challenging and important problems to be addressed in deciphering the sequences of complete genomes. My laboratory has developed bioinformatic theory, software, and applications to understand and predict the consequences of DNA sequence changes. This framework uses information theory to relate differences between the strengths of interactions between proteins and DNA or RNA to their effects on gene expression. Information theory can be used to detect and quantify signals in DNA or RNA that are recognized and bound by proteins in the cell. This project will develop and evaluate mathematical models for the natural cellular process to identify elemental protein-coding units of genes, termed exons, from the sequences of unprocessed RNA transcripts. Correct processing of these transcripts is called mRNA splicing. We will use information theory to define these signals, and then combine multiple signals within individual exons to distinguish and quantify correctly processed mRNA transcripts from potential decoys. The predictions will be compared with published sequence databases of expressed genes to determine the accuracy of these models. The models, methods, and software developed in this will be useful for predicting gene expression patterns in humans and other species. These resources will eventually be used in studies of common sequence variants that are predicted to affect normal mRNA splicing.