Developing integrative approaches for identifying disease-causing genes and dysfunctional networks

 描述：癌症是遗传变异积累的结果。大型全基因组规模重测序项目，如癌症基因组图谱（TCGA）已经启动，以全面编目与癌症相关的基因组突变和表观遗传修饰。识别致癌基因和途径以深入了解疾病机制，从而促进早期诊断和最佳治疗至关重要。然而，由于复杂的生物学相互作用和疾病的异质性，识别致癌基因及其功能通路仍然具有挑战性。致病基因中的基因突变可以干扰影响执行某些生物功能的一组基因表达的信号通路。我们将一组这样的基因称为功能模块。我们假设，驱动突变，即导致癌症进展的突变，可能会影响常见的疾病相关功能模块，并且可以从基因表达数据和蛋白质相互作用数据中重建突变与模块扰动信号之间的因果关系。在这个项目中，我们将开发一种新的方法来推断致病基因和网络，通过整合来自多种类型的数据，包括基因组变异，基因表达和蛋白质相互作用的信息。我们首先动态识别疾病相关模块，由一组相互作用的基因，然后开发一个基于贝叶斯的方法来推断致病基因的疾病相关模块。然后，通过开发一种基于随机搜索的方法，我们可以确定连接致病基因和基因模块的路径。因此，从路径推断出与疾病相关的途径。此外，我们将把这些通路与人类相互作用组整合起来，以发现更高层次的疾病相关网络。此外，我们将开发基于机器学习的分类器，利用本项目中确定的分子特征（如差异表达的基因模块和致病基因）预测疾病类型和临床结果。我们的计算框架和分类器将通过网络服务器提供给研究界。PI担任大学生物信息学项目主任，拥有丰富的教学和研究经验。该项目的一个目标也是为学生提供科学研究培训，并帮助学生通过参与该项目获得生物学见解。学生将从PI学习实用的科学计算技能，并在PI的指导下开发自己的计算方法来解决特定的生物医学问题。因此，该项目将成为生物信息学中一个有效的学习-研究模式。

 DESCRIPTION: Cancer is a consequence of the accumulation of genetic alterations. Large whole-genome scale resequencing projects such as The Cancer Genome Atlas (TCGA) have been launched in an effort to comprehensively catalog the genomic mutations and epigenetic modifications that are associated with cancer. It is essential to identify cancer-causing genes and pathways to gain insight into the disease mechanisms and hence facilitate early diagnosis and optimal treatment. However, identifying cancer-causing genes and their functional pathways remains challenging due to the complex biological interactions and the heterogeneity of diseases. Genetic mutations in disease-causing genes can disturb signaling pathways that impact the expression of a set of genes performing certain biological functions. We refer to a set of such genes as a functional module. We hypothesize that driver mutations, that is, mutations that lead to cancer progression, are likely to affect common disease-associated functional modules, and the causal relationship between the mutations and the perturbed signals of the modules can be reconstructed from gene expression data and protein interaction data. In this project, we will develop a novel approach to infer disease-causing genes and networks by integrating information from multiple types of data including genomic variations, gene expression and protein interactions. We first dynamically identify disease-associated modules that consist of a set of interacting genes, then develop a Bayesian-based approach to infer causative genes from the disease-associated modules. Then, by developing a stochastic search based method, we can determine the paths connecting causative genes and gene modules. As a result, disease- related pathways are inferred from the paths. Furthermore, we will integrate those pathways with the human interactome to discover higher-level disease-associated networks. In addition, we will develop machine learning based classifiers to predict disease types and clinical outcomes utilizing the molecular signatures identified in this project, such as differentially expressed gene modules and causative genes. Our computational framework and classifiers will be made available to the research community via a webserver. The PI serves as the university bioinformatics program director and has extensive teaching and research experience. A goal of this project is also to provide scientific research training to students and o help students to gain biological insight through their involvement with the project. Students will learn practical scientific computing skills from the PI and develop their own computational approaches to solving specific biomedical problems under the guidance of the PI. Thus the project will serve as an effective learning-research model in bioinformatics.