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

我的研究项目开发用于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元素。迫切需要更好的方法来区分有害变异和良性序列变化。本文提出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（VUS）。核酸结合位点信息含量的变化是亲和力的替代度量，可用于准确预测有害突变体结合位点。我们已经开发了一种软件，可以有效地确定完整基因组中单个位点的信息变化。改变外显子定义的突变可以通过考虑多个结合位点和位点间距离的多部信息模型来推断。差距惊异)。我们通过比较基因组中含有导致错误剪接的预测突变的异常剪接异构体与缺乏这些变体的对照，验证了预测的基因表达变化。本提案开发了一个系统来解释非编码VUS，而不考虑绑定事件的类型。影响转录因子结合的突变，mRNA的RNA结合蛋白稳定性，以及包含多个RNA结合事件的外显子定义将被建模。计算每个蛋白质-核酸相互作用的信息位置权重矩阵（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盟）进行交叉验证，以消除低亲和力，非特异性结合事件。从蛋白质交联（公开I-和PAR-CLIP）数据中类似地处理RNA结合序列。pwm用于检测基于信息变化的突变。合作调控和重叠或相邻站点的组合分析将使用站点集群的信息密度、间隙惊喜或混合模型进行评估。然后将用这些组合方法重新分析突变，以确定与启动子强度相对应的变化。在预测有害的单核苷酸变异后，我们将修改病例对照基因表达验证方法，通过计算变异产生具有预测转录因子或RNA结合位点突变的所有基因表达稳态变化的可能性，相对于缺乏这些突变的对照。该项目最终将开发一个系统，使这个统一的基于信息理论的框架广泛可用。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.**