Identifying novel molecular insights from life science massive data

从生命科学海量数据中识别新的分子见解

• 批准号: RGPIN-2018-06224
• 负责人: Droit, Arnaud
• 金额: $ 1.68万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=649608
• 关键词: Identifying; novel; molecular; insights; life

Background***Biological datasets have grown exponentially in size and complexity making them part of the world of big data. Hundreds of repositories organize biological research and make results available to biologists to be subjected to additional analyses. At the moment, the data from these repositories cannot be optimally exploited because of incompatible data types and of the separation of data into specialized repositories that cannot be combined. Therefore, navigation through many databases is required to perform searches and researchers do not always have the expertise or computational knowledge to integrate the different sources of data.******Research Problem***Gene expression experiments obtained from high throughput technologies (i.e. sequencing and microarray) are widely used to obtain gene signatures to define a particular phenotype. To be valuable, gene signatures are required to provide information on specific molecular process involved and a larger system-view context. In addition, these signatures need to go beyond the simple integration of datasets or databases, to provide valid and accurate insight on biological function and pathways of new data. To answer these needs, we have developed Kibio.science, a new portal that combines Elasticsearch, a search engine, and Kibi, a web visualization plugin, to create the relational searches between biological databases. With this proposal, we will implement analytical tools to take advantage of high-throughput data, clinical and pathological information.******Impacts***Kibio.science is a centralized platform that make it easy to visualize, interpret and perform biological analysis of multiple data types. The major contribution of this research program will be to provide analytical tools that facilitate functional interpretation and visualization of experimental data (e.g. gene signatures). We will create new statistical models to measure quality and accuracy of gene signatures in addition to provide statistics on co-mentions/correlations of biomolecules. Furthermore, to enhance the analytical power of kibio.science, specialized dynamic visualizations dedicated to clinical or research purposes will be implemented. Combined with new analytic tools, this will provide ways of translating massive life science data into actionable knowledge.

生物数据集的规模和复杂性呈指数级增长，使其成为大数据世界的一部分。数百个储存库组织生物研究，并将结果提供给生物学家进行进一步分析。目前，由于数据类型不兼容以及数据被分散到无法合并的专门储存库，这些储存库中的数据无法得到最佳利用。因此，需要在许多数据库中导航才能执行搜索，研究人员并不总是拥有整合不同数据源的专业知识或计算知识。研究问题 * 从高通量技术（即测序和微阵列）获得的基因表达实验被广泛用于获得基因签名以定义特定表型。为了有价值，基因签名需要提供有关所涉及的特定分子过程和更大的系统视图背景的信息。此外，这些特征需要超越数据集或数据库的简单集成，以提供对新数据的生物功能和途径的有效和准确的见解。为了满足这些需求，我们开发了Kibio.science，这是一个新的门户网站，它结合了Elasticsearch（搜索引擎）和Kibi（网络可视化插件），以创建生物数据库之间的关系搜索。通过这一提议，我们将实施分析工具，以利用高通量数据、临床和病理信息。***Kibio.science是一个集中式平台，可以轻松可视化，解释和执行多种数据类型的生物分析。该研究计划的主要贡献将是提供分析工具，促进功能解释和实验数据（例如基因签名）的可视化。我们将创建新的统计模型来衡量基因签名的质量和准确性，并提供有关生物分子共同提及/相关性的统计数据。此外，为了增强kibio.science的分析能力，将实现专用于临床或研究目的的专用动态可视化。结合新的分析工具，这将提供将大量生命科学数据转化为可操作知识的方法。