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中文摘要：
高通量实验方法的发展导致大量基因组、转录组、代谢组等组学数据的出现，组学数据的整合为全面了解生物学系统提供了条件．但是，由于当前实验技术手段的限制，高通量组学数据大多存在系统偏差，数据类型和可靠程度也各不相同，这给组学数据的整合带来了困难．本文以转录组、蛋白质组和代谢组为重点，综述了近年来组学数据整合方面的研究进展，包括新的数据整合方法和分析平台．虽然现存的数据统计和网络分析的方法有助于发现不同组学数据之间的关联，但是生物学意义上的深层次的数据整合还有待于生物、数学、计算机等各种领域的全面发展．
英文摘要：
The development of high-throughput experimental techniques has led to the emergence of a large amount of omics data, including transcriptome, proteome and metabonome data. Integrating such omics data provided a chance to comprehend the biological system fully. However, there were systematic biases in high-throughput data due to the limit of experimental technique, and data types and reliability degree varied in different omics data, which brought difficulties to the integration of different omics data. In this paper, we focused on transcriptome, proteome and metabonome to review the progress in the area of omics data integration in the recent years, including new methods and analysis tools. Even though the existing approaches of data statistic and network analysis could help uncover the relationship among different omics data, thorough integration of data from the biological aspect would depend on the further development in multiple areas, such as biology, mathematics and computer.
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