Chen Yuzhu, Li Jian, Zhang Yufeng, Zhang Mingqian, Sun Zheng, Jing Gongchao, Huang Shi, Su Xiaoquan
College of Computer Science and Technology Qingdao University Qingdao Shandong China.
Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology Chinese Academy of Sciences Qingdao Shandong China.
Imeta. 2022 Mar 6;1(1):e1. doi: 10.1002/imt2.1. eCollection 2022 Mar.
Massive microbiome sequencing data has been generated, which elucidates associations between microbes and their environmental phenotypes such as host health or ecosystem status. Outstanding bioinformatic tools are the basis to decipher the biological information hidden under microbiome data. However, most approaches placed difficulties on the accessibility to nonprofessional users. On the other side, the computing throughput has become a significant bottleneck of many analytical pipelines in processing large-scale datasets. In this study, we introduce Parallel-Meta Suite (PMS), an interactive software package for fast and comprehensive microbiome data analysis, visualization, and interpretation. It covers a wide array of functions for data preprocessing, statistics, visualization by state-of-the-art algorithms in a user-friendly graphical interface, which is accessible to diverse users. To meet the rapidly increasing computational demands, the entire procedure of PMS has been optimized by a parallel computing scheme, enabling the rapid processing of thousands of samples. PMS is compatible with multiple platforms, and an installer has been integrated for full-automatic installation.
大量的微生物组测序数据已经产生,这些数据阐明了微生物与其环境表型之间的关联,如宿主健康或生态系统状态。出色的生物信息学工具是解读隐藏在微生物组数据之下的生物学信息的基础。然而,大多数方法给非专业用户的使用带来了困难。另一方面,计算吞吐量已成为许多分析流程在处理大规模数据集时的一个重大瓶颈。在本研究中,我们介绍了并行元分析套件(PMS),这是一个用于快速、全面地分析、可视化和解读微生物组数据的交互式软件包。它在用户友好的图形界面中涵盖了一系列用于数据预处理、统计和可视化的功能,这些功能采用了最先进的算法,不同用户均可使用。为了满足快速增长的计算需求,PMS的整个流程已通过并行计算方案进行了优化,能够快速处理数千个样本。PMS与多个平台兼容,并集成了一个安装程序用于全自动安装。