Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities.
Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities.
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Parallel-META 3：用于高效比较微生物群落的综合分类学和功能分析平台
DOI：
10.1038/srep40371
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发表时间：
2017-01-12
影响因子：
4.6
通讯作者：
Su X
中科院分区：
文献类型：
--
作者：
Jing G;Sun Z;Wang H;Gong Y;Huang S;Ning K;Xu J;Su X
后基因组的数量正在迅速增加。然而，当前的元基因组分析方法受到其在大量微生物组中进行深入数据挖掘的能力的限制，这些微生物组中的每个微生物组携带着复杂的群落结构。此外，配置和操作计算流水线的复杂性也阻碍了最终用户高效的数据处理。在这项工作中，我们介绍了一个全面的、全自动的元基因组数据挖掘计算工具包Parally-Meta 3，它具有先进的功能，包括猎枪序列的16S rRNA提取、16S rRNA拷贝数校准、基于16S rRNA的功能预测、多样性统计、生物标记选择、相互作用网络构建、基于向量图的可视化和并行计算。在来自不同研究和平台的5,337个样本上的1,117,555,208个序列上的应用表明，它可以产生与QIIME和PICRUST相似的结果，速度更快，占用的内存更少，这表明它能够揭示大数据集的分类和功能动态模式，并阐明微生物群与环境之间的生态联系。并行META 3是用C/C++和R实现的，并集成到一个执行包中，以便在LINUX和MacOSX下快速安装和轻松访问。二进制和源代码包都可以在http://bioinfo.single-cell.cn/parallel-meta.html.上找到
The number of metagenomes is increasing rapidly. However, current methods for metagenomic analysis are limited by their capability for in-depth data mining among a large number of microbiome each of which carries a complex community structure. Moreover, the complexity of configuring and operating computational pipeline also hinders efficient data processing for the end users. In this work we introduce Parallel-META 3, a comprehensive and fully automatic computational toolkit for rapid data mining among metagenomic datasets, with advanced features including 16S rRNA extraction for shotgun sequences, 16S rRNA copy number calibration, 16S rRNA based functional prediction, diversity statistics, bio-marker selection, interaction network construction, vector-graph-based visualization and parallel computing. Application of Parallel-META 3 on 5,337 samples with 1,117,555,208 sequences from diverse studies and platforms showed it could produce similar results as QIIME and PICRUSt with much faster speed and lower memory usage, which demonstrates its ability to unravel the taxonomical and functional dynamics patterns across large datasets and elucidate ecological links between microbiome and the environment. Parallel-META 3 is implemented in C/C++ and R, and integrated into an executive package for rapid installation and easy access under Linux and Mac OS X. Both binary and source code packages are available at http://bioinfo.single-cell.cn/parallel-meta.html.