Abraham Paul E, Giannone Richard J, Xiong Weili, Hettich Robert L
Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee.
Curr Protoc Bioinformatics. 2014 Jun 17;46:13.26.1-13.26.14. doi: 10.1002/0471250953.bi1326s46.
Contemporary microbial ecology studies usually employ one or more "omics" approaches to investigate the structure and function of microbial communities. Among these, metaproteomics aims to characterize the metabolic activities of the microbial membership, providing a direct link between the genetic potential and functional metabolism. The successful deployment of metaproteomics research depends on the integration of high-quality experimental and bioinformatic techniques for uncovering the metabolic activities of a microbial community in a way that is complementary to other "meta-omic" approaches. The essential, quality-defining informatics steps in metaproteomics investigations are: (1) construction of the metagenome, (2) functional annotation of predicted protein-coding genes, (3) protein database searching, (4) protein inference, and (5) extraction of metabolic information. In this article, we provide an overview of current bioinformatic approaches and software implementations in metaproteome studies in order to highlight the key considerations needed for successful implementation of this powerful community-biology tool.
当代微生物生态学研究通常采用一种或多种“组学”方法来研究微生物群落的结构和功能。其中，宏蛋白质组学旨在表征微生物群落成员的代谢活动，在遗传潜能和功能代谢之间建立直接联系。宏蛋白质组学研究的成功开展依赖于高质量实验技术和生物信息学技术的整合，以便以一种与其他“元组学”方法互补的方式揭示微生物群落的代谢活动。宏蛋白质组学研究中关键的、定义质量的信息学步骤包括：（1）宏基因组的构建，（2）预测的蛋白质编码基因的功能注释，（3）蛋白质数据库搜索，（4）蛋白质推断，以及（5）代谢信息的提取。在本文中，我们概述了宏蛋白质组研究中当前的生物信息学方法和软件实现，以突出成功应用这一强大的群落生物学工具所需的关键注意事项。
