Su Xiaoquan, Jing Gongchao, Zhang Yufeng, Wu Shunyao
College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071 China.
Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101 China.
Comput Struct Biotechnol J. 2020 Aug 1;18:2075-2080. doi: 10.1016/j.csbj.2020.07.020. eCollection 2020.
During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the "microbiome data space".
在过去十年中，为了研究微生物群落特征与环境之间的动态关联，已产生了大量的微生物组测序数据。如何精确且高效地解读大规模微生物组数据并进一步从中获益，已成为当前微生物组研究最关键的瓶颈之一。在本综述中，我们聚焦于跨研究微生物组数据集分析的三个关键步骤，包括微生物组特征分析、数据整合和数据挖掘。通过介绍当前的生物信息学方法并讨论其局限性，我们展望了这三个步骤计算方法开发的机遇，并提出了多组学数据分析的可行解决方案，以便从不同角度全面理解和快速研究微生物组，这可能通过提供更广阔的“微生物组数据空间”视角来推动数据驱动的研究。
