Unified framework for non-coding mutation analysis based on information theory

我的研究项目开发了解释基因组序列变异的生物信息学方法，特别是调节转录和转录后过程的DNA或RNA元件。迫切需要更好的方法来区分有害变异和良性序列变化。这一建议开发了一个基于信息论的统一框架，用于解释完整基因序列和基因组中的未知意义变异(VU)。核酸结合位点信息含量的变化是亲和力的替代测量，可以用来准确预测有害的突变结合位点。我们已经生产了软件，可以有效地确定完整基因组中个别位置的信息变化。改变外显子定义的突变可以用解释多个结合位点和位点间距离的多方信息模型来推断(即。差距令人惊讶)。我们通过比较基因组中的异常剪接异构体来验证预测的基因表达变化，这些基因组中的异常剪接异构体包含导致误剪接的预测突变与缺乏这些变体的对照。该提议开发了一种用于解释非编码VU的系统，而不考虑绑定事件的类型。影响转录因子结合的突变，RNA结合蛋白的稳定性，以及包含多个RNA结合事件的外显子定义将被建模。为每个蛋白质-核酸相互作用计算信息位置权重矩阵(PWM)。这些模型将使用来自具有相同转录因子的多个细胞系的染色质免疫沉淀数据(来自国际表观基因组联合会)进行交叉验证，以消除低亲和力、非特异性结合事件。RNA结合序列的处理类似于蛋白质交联(公共I-和PAR-Clip)数据。PWMS用于根据信息变化检测突变。合作调控和重叠或相邻站点的组合分析将使用站点集群、缺口惊喜或混合模型的信息密度进行评估。然后将用这些组合方法重新分析突变，以寻找与启动子强度相对应的变化。在预测了有害的单核苷酸变异后，我们将修改我们的病例对照基因表达验证方法，方法是计算一个变异相对于没有这些突变的对照产生所有具有预测的转录因子或RNA结合位点突变的基因表达稳定变化的可能性。该项目最终将开发一个系统，使这一以信息论为基础的统一框架得到广泛应用。该软件将从基因面板和完整的基因组序列中生成一组简短的潜在有害突变。**

My research program develops bioinformatic methods for interpreting genomic sequence variation, specifically DNA or RNA elements that regulate transcription and post-transcriptional processes.There is an acute need for better approaches that distinguish deleterious variants from benign sequence changes. This proposal develops a unified framework, based on information theory, for interpretation of variants of unknown significance (VUS) in complete gene sequences and genomes. Changes in information contents of nucleic acid binding sites are a surrogate measure of affinity and can be used to accurately predict deleterious mutant binding sites. We have produced software to determine information changes at individual sites efficiently in complete genomes. Mutations that alter exon definition can be inferred with multipartite information models that account for multiple binding sites and intersite distances (ie. gap surprisal). We validate predicted gene expression changes by comparing abnormal splice isoforms in genomes containing predicted mutations causing missplicing with controls lacking these variants. This proposal develops a system for interpreting non-coding VUS, regardless of the type of binding event. Mutations affecting transcription factor binding, and RNA-binding protein stabilization of mRNA, and exon definition comprising multiple RNA binding events will be modeled. Information position weight matrices (PWM) are computed for each protein-nucleic acid interaction. These models will be cross-validated using chromatin immunoprecipitation data (from the International Epigenome Consortium) from multiple cell lines with the same transcription factors to eliminate low affinity, non-specific binding events. RNA binding sequences are processed similarly from protein cross-linking (public I- and PAR-CLIP) data. The PWMs are used to detect mutations based on information changes. Combinatorial analysis of cooperative regulation and overlapping or adjacent sites will be evaluated using information densities of site clusters, gap surprisal, or mixed models. Mutations will then be reanalyzed with these combinatorial methods for changes that correspond to promoter strength. After predicting deleterious single nucleotide variants, we will modify our case-control gene expression validation methods by computing the likelihood that a variant produces steady state changes in expression of all genes with predicted transcription factor or RNA binding site mutations, relative to controls lacking these mutations. The project will culminate with development of a system to make this unified information theory-based framework broadly available. This software will generate an abbreviated set of potentially deleterious mutations from gene panels and complete genome sequences.**