Collaborative Research: ABI Development: PathBubbles for Dynamic Visualization and Integration of Biological Information

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: 1147029
• 负责人: Carl Schmidt
• 金额: $ 43.04万
• 依托单位: University of Delaware
• 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 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147029&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获取的革命。 随着高吞吐量技术的出现，数据正在以一种超过我们将数据转换为知识的能力的速度。 这些技术的应用可以提供与特定生物学问题相关的数据数量的数据，但是解释信息的数量仍然是一个挑战。 可以帮助研究人员可视化，分类并最终使他们的数据有意义。可视化工具（例如KeGG或Reactome），将数据放在信号和代谢途径的上下文中。 已经开发了许多不同的本体论，文本挖掘和丰富分析工具，以帮助将各个数据点分为组。可视化和分类都降低了问题的复杂性，并提供了对基本生物学的见解。 然而，最终，人们仍然需要整合，评估，评估，最后将这些数据转换为人类知识的基本步骤。需要一种新颖的动态方法来可视化，以及整合文本中发现的不同本体和信息，以提高研究人员将高吞吐量数据转化为理解的能力。 这将通过开发PathBubbles来实现 - 一种动态的，交互式途径可视化工具，使用现有的Vis和Code Bubbles作为框架。 此外，将通过整合特定本体论，文本挖掘工具和表达数据的数据来提供其他信息，以提供与PathBubbles一起使用的基因注释。最后，从文献中捕获有关翻译后修改后修改的蛋白质的功能信息，并将这些信息整合到chaterbubbles中，将帮助用户开发可检验的假设。人类是视觉动物，依靠视觉输入来感知并将自己定向到环境。 结果之一是，人类能够在视觉上显示的信息中识别模式。 这项工作利用了这种能力，可以通过开发一个基于Web的新型界面来帮助生物学家分析成千上万的途径数据点，该界面显示为图形。 该图将显示来自基因研究的数据，其中每个基因产物被显示为DOT，并且基因之间的连接是线条。 代表基因产物的点可以根据特定的生物学条件下的基因活性进行着色。例如，如果与正常细胞相比，该基因在癌细胞中以很高的水平表达，则DOT将以红色显示。此外，这些线可能代表各种相互作用，例如基因产物之间的结合或共享小分子，并且相互作用的类型可以用不同的线颜色指示。图形界面由有关每个基因产物和每种相互作用的广泛信息数据库支持。用户将可以通过单击点或关注线来访问该信息。 该项目的一个特别新颖的方面是，用户将能够使用允许他们创建新的点（基因产品）和行（交互）的接口来添加自己的数据。然后，他们将能够提供有关发生的功能信息，例如，当它们的基因产品与数据库中已经存在的基因产品相互作用时。 基于这些新信息，系统将预测用户新基因产品对生物途径的影响。 这将允许用户问“如果”问题，然后使用此接口在进行实际实验之前测试假设。 在生物学的背景下开发系统时，以图形方式检验不同假设的能力将应用于其他各种学科，包括化学，工程，物理和计算机科学。