Collaborative Proposal: ABI Innovation:A Graph Based Approach for the Genome Wide Prediction of Conditionaly Essential Genes

一个人如何在基因组的尺度上识别和描述一组对生物体在特定条件下生长和繁荣至关重要的基因？预测这类基因是生物信息学的一个基本目标；该项目旨在创建方法和工具，以制作这些功能基因的准确列表。该方法将表型预测与关于细胞功能生物网络的知识相结合，以推断新的知识。这里开发的网络分析方法可以很容易地转移并应用于各种各样的数据集，以回答从推断基因表型关联到检测社交网络上的社区的广泛问题，这些扩展与网络科学社区高度相关。此外，该项目使用状态空间模型和降维技术对时间基因表达数据进行的最先进的分析普遍适用于任何基因组-例如组织特异性基因与普遍表达基因。除了在研究生物体酵母中推进功能基因组学知识外，这些工具还将通过提供大规模系统级鉴定和表型分子表征，对个人基因组学研究等领域的研究产生影响。最后，本项目为生物信息学教育提供了新的创新工具。用更专业的术语来说，这个项目的主要目标是开发新的数学模型和方法，在给定一组基因或整个基因组的情况下，可以推断出它们的表型，并表明这些基因是否对生物体的生存是必要的。具体来说，信息将在两个层面上整合：表型和分子。在表型水平上，生物网络的结构将用于为基因分配表型属性，并识别具有相似基本表型的基因组。在分子水平上，结果的表型预测将通过识别由相似活动模式控制的基本基因组来改进。在这两个层面上的信息整合将导致一个全面的基因表型特征和一组精炼的条件必需基因。所得预测结果将在两种酵母系统中进行实验验证。与此项目相关的所有工具和数据集将通过genopheno.gersteinlab.org免费提供。

How does one identify, and characterize at the genome scale, the set of genes that is essential for an organism to grow and thrive under particular conditions? Predicting such sets of genes is a fundamental goal in bioinformatics; this project aims to create methods and tools for making accurate lists of such functional genes. The approach combines phenotype prediction with knowledge about the functional biological networks in cells to infer new knowledge. The network analysis methods developed here can be easily transferred and applied to a large variety of datasets to answer a wide range of questions from inferring gene-phenotype associations to detecting communities on social networks, extensions highly relevant to the network science community. Moreover, the project's state-of-the-art analysis of temporal gene expression data using state-space models and dimensionality reduction techniques is universally applicable to any groups of genes - e.g. tissue specific vs universally expressed genes. In addition to advancing functional genomics knowledge in the study organism, yeast, the tools will have an impact on research in fields like personal genomics research, by providing a large-scale system-level identification and molecular characterization of phenotypes. Finally, this project provides new and innovative tools for education in bioinformatics. In more technical terms, this project's major goal is to develop new mathematical models and methods that, given a set of genes or an entire genome, can infer their phenotypes and suggest whether or not these genes are necessary for the organism survival. Specifically, information will be integrated on two levels: phenotypic and molecular. At the phenotypic level the structure of biological networks will be used to assign phenotypic attributes to genes and identify sets of genes that share similar essential phenotypes. At the molecular level, the resulted phenotype predictions will be refined by identifying groups of essential genes governed by similar activity patterns. The integration of the information on these two levels will result in a comprehensive gene-phenotype characterization and a refined group of conditionally essential genes. The resulting predictions will be validated experimentally in two yeast systems. All the tools and datasets associated with this project will be made freely available through genopheno.gersteinlab.org.