微生物在自然界中普遍存在并主要以“微生物群落”的形式共存。对包括群落基因型和表型等大量信息在内的微生物组的深入分析，允许我们更好的认识群落的结构和功能，群落对于生态环境的响应及反馈，以及微生物之间的相互影响和调控机制，发掘群落重要功能和在健康与环境等领域的潜在应用途径。然而微生物组数据具有多来源、异质性、数据量大等特点，导致现有分析方法无法全面、深入、高效的解析微生物组大数据。.本项目聚焦于微生物组大数据分析，计划整合包括元基因组数据、群落代谢数据和群落环境参数在内的海量微生物组数据，构建基于物种、功能、代谢产物、环境因素等特征的群落结构和功能数据模型。同时计划设计大数据挖掘算法，研究群落结构和功能特征、群落内部物种相关性网络、群落和环境关联性等，实现大数据驱动的微生物群落数据挖掘和理解。项目的开展有助于加深我们对微生物群落结构、功能、环境响应与反馈等重要问题的理解，促进相关应用研究开展。

Microbes are ubiquitous on earth, and they usually live in the form of communities. Researches in microbiome, which include all genotypic and phenotypic information for the microbial community, would have profound important for understanding of functions, dynamics and interactions of microbial community, as well as their responses and feed-backs for the environments. Microbiome research also has applications in a wide-range of areas such as bio-medicine and environment monitoring. However, microbiome data possesses several properties, including multi-source, heterogeneity and big-data. Yet current methods are not fully capable of analyzing and mining these microbiome big-data, making the understanding of the structural and functional profiles for microbial communities difficult. Therefore, integrated and thorough analysis approach should be taken to fully understand the structure, function, interaction and dynamics of microbial communities...In this work, we plan to conduct research on "Method development for integration and data-mining for microbiome data". We will design taxonomical and functional data models to integrate multi-source and multi-type data from microbiome samples, including metagenomic data, metabolomic data and community meta-data. We will also design high-performance data-mining methods, including community structure and function profiling, species network analysis, and community-environment correlation analysis, for in-depth examination and understanding of microbial communities. This integrated and thorough analysis approach focusing on big-data mining and explanation for microbial community would deepen our understanding of the microbial communities’ structures, functions, as well as their adaptations and feedbacks for the environments, which would also have potential for a wide-range of application areas.