Bioinformatics Core

摘要：生物信息学核心 目前的基因和基因组技术产生了大量的数据， 与不相关的基因组变异相关。需要一种新的、广泛适用的信息学方法， 可以在功能网络的背景下整合和解释基因组规模的信息，从而提供 深入了解受驱动人类疾病的突变影响的特定分子过程。本申请 是基于这样的假设，即[A]单基因病因是CDH在家庭中分离的原因， 不同程度的突变，[B]从头突变与大的影响大小是负责一小部分 散发的，大多数是复杂的，CDH病例，和[C]罕见的风险变异，有助于CDH可以发现， 从单身者身上获取基因数据统计遗传学将为我们发现致病变异提供信息，例如， 新生变异的负担测试与测序正常儿童的大型对照组进行比较 从散发性自闭症儿童的未受影响的兄弟姐妹中确定，并作为 Simons Simplex Collection（SSC）.为此，我们调整了分析管道，以识别和 注释变体，结合适当的生物信息学工具。创新的网络分析， 蛋白质-蛋白质相互作用（PPI）和基因共表达是对遗传学研究的重要补充。 在患有CDH等罕见疾病的人类中的变异（项目I）及其用于分析的最佳选择 模式生物（项目二和项目三）。在这里，我们详细介绍了一些特定的工具，方法和方法， 核心小组将提供查询在整个项目期间收集的各种大型数据集的服务。

ABSTRACT: BIOINFORMATICS CORE Current genetic and genomic technologies produce a large amount of data, and it is challenging to distinguish relevant from irrelevant genomic variants. There is a need for new, widely applicable, informatics methods that can integrate and interpret genome-scale information in the context of functional networks, thus providing insight into the specific molecular processes affected by mutations that drive human disease. This application is based on the hypothesis that [A] monogenic etiologies are responsible for CDH segregating in families, with varying degrees of penetrance, [B] de novo mutations with large effect sizes are responsible for a fraction of sporadic, mostly complex, CDH cases, and [C] rare risk variants contributing to CDH can be discovered in genetic data from singletons. Statistical genetics will inform our discovery of causative variants, for example by burden tests for de novo variants compared against a large control group of sequenced normal children ascertained from the unaffected siblings of children with sporadic autism, and made publicly available as the Simons Simplex Collection (SSC). For this purpose, we have adapted analysis pipelines to identify and annotate variants, incorporating appropriate bioinformatics tools. Innovative network analyses based on Protein-Protein Interaction (PPI) and gene co-expression are an essential complement to genetic studies of variants in humans with rare diseases such as CDH (Project I) and their optimal selection for analyses in model organisms (Projects II and III). Here we detail some of the specific tools, methods, and approaches the core will provide to interrogate various large data sets to be collected throughout the duration of the project.