Turning Data into Whole Cell Ontology Models for Functional Analysis

描述（由申请人提供）：生物信息学的圣杯是创造全细胞模型，具有增强人类理解和促进发现的能力。为此，一个成功且被广泛使用的努力是基因本体（GO），这是一个大规模的项目，手动将基因注释为描述分子功能，生物过程和细胞成分的术语，并提供术语之间的关系，例如捕获“小核糖体亚基”和“大核糖体亚基”聚集在一起形成“核糖体”。氧化石墨烯被广泛用于了解一个基因或一组基因的功能。不幸的是，GO受限于手工创建和更新所需的工作量。它只存在于经过充分研究的生物体中，即使这样，每个生物体也只有一种通用形式，其总体基因组覆盖范围有限，并且偏向于充分研究的基因和功能。使用GO无法了解未表征的基因或发现新功能，也无法快速为新生物体组装本体模型，更不用说特定的细胞类型或疾病状态了。这项提议的研究将改变这种状况。工作已经表明，酿酒酵母中基因和蛋白质相互作用的大型网络可以用来计算推断一个本体，其覆盖范围和能力相当于人工策划的氧化石墨烯细胞成分本体。尽管如此，这第一次尝试在使用的实验数据类型和推断更普遍有用的生物过程本体的能力方面受到限制。本文将运用机器学习方法，将多种类型的实验数据整合到本体模型构建中，并分析每个实验提供的生物信息类型，揭示那些对捕获生物过程信息最有信息量的实验。此外，本文探索的高通量实验数据到本体范式将用于开发一种计算工具，以突出当前高通量实验数据分析方法无法实现的新型假设。初步工作表明氧化石墨烯可用于预测合成致死基因对，即单个非必需基因，但当一起敲除时导致细胞死亡。鉴于癌症的高突变率，这些对提供了潜在的癌症药物靶点，因为药物可能靶向突变癌细胞中必需的基因产物，而不是其他细胞，从而只杀死癌细胞。由于数据驱动的本体论不会受到偏见和覆盖问题的阻碍，并且专门用于捕获功能关系，因此本提案将探索数据驱动的本体论将比GO更适合帮助预测合成致命对的想法。为此，将开发算法来构建酵母DNA修复的数据驱动本体，并使用该本体来预测合成的致死基因对。总体而言，本提案将发展计算和实验路线图，以构建基因功能的全细胞模型-一个本体论-并使用该模型发现有用的生物学-合成致死对。

DESCRIPTION (provided by applicant): A holy grail of bioinformatics is the creation of whole-cell models with the ability to enhance human understanding and facilitate discovery. To this end, a successful and widely-used effort is the Gene Ontology (GO), a massive project to manually annotate genes into terms describing molecular functions, biological processes and cellular components and provide relationships between terms, e.g. capturing that "small ribosomal subunit" and "large ribosomal subunit" come together to make "ribosome". GO is widely used to understand the function of a gene or group of genes. Unfortunately, GO is limited by the effort required to create and update it by hand. It exists only for well-studied organisms and even then in only one, generic form per organism with limited overall genome coverage and a bias towards well-studied genes and functions. It is not possible to learn about an uncharacterized gene or discover a new function using GO, and one cannot quickly assemble an ontology model for a new organism, let alone a specific cell-type or disease-state. This proposed research will change this state of affairs. Already, work has shown that large networks of gene and protein interactions in Saccharomyces cerevisiae can be used to computationally infer an ontology whose coverage and power are equivalent to those of the manually-curated GO Cellular Component ontology. Still, this first attempt was limited in the types of experimental data used and its ability to infer the more generally useful Biological Process ontology. Here machine learning approaches will be applied to integrate many types of experimental data into ontology model construction and analyze the type of biological information provided by each experiment, revealing those experiments most informative for capturing Biological Process information. Furthermore, the high-throughput experimental data to ontology paradigm explored here will be used to develop a computational tool to highlight novel types of hypotheses that are inaccessible by current high-throughput experimental data analysis methods. Preliminary work has shown GO to be useful for prediction of synthetic lethal pairs of genes, i.e. genes that are individually non-essential but when knocked out together cause cell death. Given the high mutation rate in cancer, these pairs provide potential cancer drug targets, as a drug may target a gene product which is now essential in the mutated cancer cells but not other cells, thereby killing only cancer cells. Because data-driven ontologies are not as hindered by issues with bias and coverage and are specifically designed to capture only functional relationships, this proposal will explore the idea that data-driven ontologies will be better suited to help predict synthetic lethal pairs than GO. To this end, algorithms will be developed to construct a data-driven ontology of yeast DNA repair and use this ontology to predict synthetic lethal pairs of genes. Overall, this proposal will develop the computational and experimental roadmap to construct a whole-cell model of gene function - an ontology - and use the model to discover useful biology - synthetic lethal pairs.