Collaborative Research: ABI Development: PathBubbles for Dynamic Visualization and Integration of Biological Information. Funding Program: NSF ABI Development

合作研究：ABI 开发：用于生物信息动态可视化和集成的 PathBubbles。

• 批准号: 1147075
• 负责人: Fiona McCarthy
• 金额: $ 21.87万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147075&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Development; PathBubbles

Many disciplines of modern biology have undergone a revolution in data acquisition. With the advent of high throughput technologies, data is accumulating at a pace that outstrips our ability to convert that data into knowledge. Application of these technologies can provide terabyte amounts of data relevant to a particular biological problem but interpreting that volume of information remains a challenge. A variety of resources are available to help researchers visualize, categorize and ultimately make sense out of their data. Visualization tools such as those in KEGG or Reactome, place data in the context of signaling and metabolic pathways. Many different ontologies, text mining and enrichment analysis tools have been developed to help categorize individual data points into groups. Both visualization and categorization reduce the complexity of the problem and provide insight into the underlying biology. Ultimately, however, people are still in need for the essential steps of integrating, evaluating and, finally, converting these data to human knowledge. What is needed a novel, dynamic approach to pathway visualization along with integrating disparate ontologies and information found in text to improve the researcher?s ability to convert high throughput data into understanding. This will be achieved by developing PathBubbles, a dynamic, interactive pathway visualization tool using the existing Vis- and Code Bubbles as a framework. In addition additional information will be provided by integrating data found in specific ontologies, text-mining tools and expression data to provide gene annotation for use with PathBubbles. Finally, capturing functional information about post-translationally modified proteins from literature and integrating this information into PathBubbles, will assist users in developing testable hypotheses. Humans are visual animals, relying on visual input to sense and orient themselves to the environment. One consequence of this is that humans are very able to recognize patterns in visually displayed information. This work exploits this ability to help biologists analyze thousands of pathway data points by developing a novel web based interface where information is displayed as a graph. This graph will display data from gene studies where each gene product is shown as a dot and the connections between the genes are lines. The dots that represent gene products can be colored depending on the activity of the gene in a particular biological condition. For example, if the gene is expressed at a very high level in a cancer cell compared to a normal cell, the dot will be displayed in red. In addition, the lines may represent a variety of interactions such as binding between gene products or sharing of a small molecule and the type of interaction can be indicated by different line colors. The graphical interface is supported by an extensive database of information about each gene product and each interaction. Users will be able to access that information by simply clicking on the dot or line of interest. A particularly novel aspect of this project is that users will be able to add their own data by using an interface that allows them to create new dots (gene products) and lines (interactions). They will then be able to provide functional information about what happens, for instance, when their gene product interacts with a pre-existing gene product already in the database. Based on this new information, the system will then predict the effect of the user's new gene product on the biological pathways. This will allow users to ask 'what if' questions, using this interface to test hypotheses before doing actual experiments. While the system is being developed in the context of biology, the ability to graphically test different hypotheses will have application to a variety of other disciplines including chemistry, engineering, physics and computer sciences.

现代生物学的许多学科都经历了数据采集的革命。 随着高通量技术的出现，数据积累的速度超过了我们将数据转化为知识的能力。 这些技术的应用可以提供与特定生物问题相关的TB级数据，但解释这些信息量仍然是一个挑战。 各种资源可用于帮助研究人员可视化，分类并最终使其数据有意义。可视化工具，如KEGG或Reactome中的可视化工具，将数据置于信号传导和代谢途径的背景下。 已经开发了许多不同的本体、文本挖掘和丰富分析工具来帮助将单个数据点分类到组中。可视化和分类都降低了问题的复杂性，并提供了对潜在生物学的洞察。 然而，最终，人们仍然需要整合、评估并最终将这些数据转化为人类知识的基本步骤。什么是需要一种新颖的，动态的方法，以路径可视化沿着与整合不同的本体和信息中发现的文本，以提高研究人员？将高通量数据转化为理解的能力。 这将通过开发PathBubbles来实现，PathBubbles是一个动态的、交互式的途径可视化工具，使用现有的维斯和代码气泡作为框架。 此外，将通过整合特定本体中的数据、文本挖掘工具和表达数据来提供额外的信息，以提供与PathBubbles一起使用的基因注释。最后，从文献中捕获关于后修饰蛋白质的功能信息并将这些信息整合到PathBubbles中，将有助于用户开发可测试的假设。人类是视觉动物，依靠视觉输入来感知和定位自己的环境。 这样做的一个结果是，人类非常能够识别视觉显示信息中的模式。 这项工作利用这种能力，帮助生物学家分析成千上万的途径数据点，通过开发一种新的基于网络的界面，其中信息显示为图形。 此图将显示来自基因研究的数据，其中每个基因产物显示为点，基因之间的连接为线。 代表基因产物的点可以根据基因在特定生物条件下的活性而着色。例如，如果与正常细胞相比，癌细胞中的基因表达水平非常高，则点将显示为红色。此外，这些线可以代表各种相互作用，例如基因产物之间的结合或小分子的共享，并且可以通过不同的线颜色来指示相互作用的类型。图形界面由关于每个基因产物和每个相互作用的信息的广泛数据库支持。用户只需点击感兴趣的点或线就可以访问这些信息。 该项目的一个特别新颖的方面是，用户将能够通过使用一个界面添加自己的数据，该界面允许他们创建新的点（基因产物）和线（相互作用）。然后，他们将能够提供关于发生什么的功能信息，例如，当他们的基因产物与数据库中已经存在的基因产物相互作用时。 根据这些新的信息，系统将预测用户的新基因产物对生物途径的影响。 这将允许用户提出“如果”的问题，在进行实际实验之前使用此界面来测试假设。 虽然该系统是在生物学的背景下开发的，但以图形方式测试不同假设的能力将应用于各种其他学科，包括化学，工程，物理和计算机科学。