Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA 92093, USA; Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.
Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Cell Syst. 2019 Mar 27;8(3):267-273.e3. doi: 10.1016/j.cels.2019.02.003. Epub 2019 Mar 13.
Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.
系统生物学不仅需要基因组规模的数据，还需要将这些数据整合到可解释模型中的方法。此前，我们开发了将组学数据组织成细胞成分和途径结构化层次结构的方法，称为“数据驱动本体”。这种层次结构再现了已知的细胞子系统并发现了新的子系统。为了广泛促进这种类型的建模，我们报告了一个名为“数据驱动本体工具包”（DDOT）的软件库的开发，该库由一个用于组装和分析本体的 Python 包（https://github.com/idekerlab/ddot）和一个用于可视化本体的网络应用程序（http://hiview.ucsd.edu）组成。使用 DDOT，我们通过将基因 - 疾病映射与从组学数据中得出的基因相似性网络集成，为 652 种疾病编制了一个本体总集。例如，范可尼贫血症的本体在其 194 个基因和 74 个子系统的层次结构中描述了已知和新的疾病机制。DDOT 提供了一个简单的接口，可在网络数据交换上在线共享本体。
