使用基于网络的分析平台对大数据存储库中的高通量数据进行数据分析——以表观遗传学研究中QUEST与GenePattern的整合为例
Enabling Data Analysis on High-Throughput Data in Large Data Depository Using Web-Based Analysis Platform - A Case Study on Integrating QUEST with GenePattern in Epigenetics Research.
作者信息
Camerlengo Terry, Ozer Hatice Gulcin, Yan Pearlly, Parvin Jeffrey, Huang Tim, Huang Kun, Perez Francisco, Teng Mingxiang, Li Lang, Liu Yunlong, Kurc Tahsin
机构信息
The Ohio State University Columbus, Ohio, USA.
出版信息
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2009 Nov 1;1-4(Nov 2009):392-395. doi: 10.1109/BIBM.2009.84.
Enabling data analysis in large data depositories for high throughput experimental data such as gene microarrays and ChIP-seq is challenging. In this paper, we discuss three methods for integrating QUEST, a data depository for epigenetic experiments, with a web-based data analysis platform GenePattern. These methods are universal and can serve as an exemplary implementation resolving the dilemma facing many similar database systems in integrating data analysis tools.
在诸如基因微阵列和染色质免疫沉淀测序（ChIP-seq）等高通量实验数据的大型数据存储库中进行数据分析具有挑战性。在本文中，我们讨论了三种将表观遗传实验数据存储库QUEST与基于网络的数据分析平台GenePattern进行集成的方法。这些方法具有通用性，可作为解决许多类似数据库系统在集成数据分析工具时所面临困境的一个示例性实现。
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