Jay Jeremy J, Chesler Elissa J
The Jackson Laboratory, Bar Harbor, ME, USA.
Methods Mol Biol. 2014;1101:13-29. doi: 10.1007/978-1-62703-721-1_2.
Functional genomics experiments and analyses give rise to large sets of results, each typically quantifying the relation of molecular entities including genes, gene products, polymorphisms, and other genomic features with biological characteristics or processes. There is tremendous utility and value in using these data in an integrative fashion to find convergent evidence for the role of genes in various processes, to identify functionally similar molecular entities, or to compare processes based on their genomic correlates. However, these gene-centered data are often deposited in diverse and non-interoperable stores. Therefore, integration requires biologists to implement computational algorithms and harmonization of gene identifiers both within and across species. The GeneWeaver web-based software system brings together a large data archive from diverse functional genomics data with a suite of combinatorial tools in an interactive environment. Account management features allow data and results to be shared among user-defined groups. Users can retrieve curated gene set data, upload, store, and share their own experimental results and perform integrative analyses including novel algorithmic approaches for set-set integration of genes and functions.
功能基因组学实验与分析产生了大量结果,每个结果通常量化包括基因、基因产物、多态性及其他基因组特征在内的分子实体与生物学特性或过程之间的关系。以整合的方式利用这些数据,以找到基因在各种过程中作用的趋同证据、识别功能相似的分子实体或基于基因组相关性比较各过程,具有巨大的实用价值。然而,这些以基因为中心的数据常常存于多样且不可互操作的存储库中。因此,整合需要生物学家实施计算算法并统一物种内部及跨物种的基因标识符。基于网络的GeneWeaver软件系统在交互式环境中将来自多样功能基因组学数据的大型数据存档与一套组合工具结合在一起。账户管理功能允许在用户定义的组之间共享数据和结果。用户可以检索经过整理的基因集数据、上传、存储和共享自己的实验结果,并进行整合分析,包括用于基因和功能集-集整合的新型算法方法。