Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2017 Jan 12;7:40371.
doi: 10.1038/srep40371.

Parallel-META 3: Comprehensive Taxonomical and Functional Analysis Platform for Efficient Comparison of Microbial Communities

Free PMC article
Comparative Study

Parallel-META 3: Comprehensive Taxonomical and Functional Analysis Platform for Efficient Comparison of Microbial Communities

Gongchao Jing et al. Sci Rep. .
Free PMC article


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


Figure 1
Figure 1. Overall workflow of Parallel-META 3.
All analysis steps were implemented in C/C++ and/or R with optimized parallel computing, and well configured in to a fully automatic pipeline package.
Figure 2
Figure 2. The running time and memory consumption of Parallel-META 3 compared to the benchmark software of QIIME and PICRUSt with 16S rRNA amplicon datasets.
Figure 3
Figure 3
Shannon index α diversity of 16S rRNA amplicon datasets validated by QIIME at the taxonomical genus level (A): Dataset 1, (B): Dataset 2 and (C): Dataset (3) and by PICRUSt at the functional pathway level (D): Dataset 1, (E): Dataset 2 and (F): Dataset (3). Simpson index results were shown in Figure S1 in Supplementary file S1.
Figure 4
Figure 4
The β diversity patterns of 16S rRNA amplicon datasets based on Meta-Storms distances validated by QIIME (A): Dataset 1, (B): Dataset 2 and (C): Dataset (3) and by PICRUSt (D): Dataset 1, (E): Dataset 2 and (F): Dataset (3).
Figure 5
Figure 5. Correlations of NTSI values calculated respectively by Parallel-META 3 and PICRUSt on 16S rRNA amplicon datasets.
Figure 6
Figure 6. Community diversity variations among body sites.
(A and B): Partition of β diversity on taxonomy and functions based on Meta-Storms distances of Dataset 1; (C and D): Shannon index α diversity of Dataset 1 on genus level and pathway level; (E and F): Comparison of 16S rRNA reads from V1-V3 region (Dataset 1) and V3-V5 region (Dataset 2) on taxonomical α diversity and β diversity. Diversity analysis of Dataset 2 was included in Figure S2 of supplementary file S1.
Figure 7
Figure 7. The most abundant genera of each of the body sites as identified by biomarker analysis.
Figure 8
Figure 8. Taxonomy diversity among microbiota samples of adults and children from three countries.
α diversity was measured by Shannon index and Simpson index (A and B) for Malawi, (D and E) for US, (G and H) for Venezuela); β diversity was illustrated in PCoA based on Meta-Storms distance (C) for Malawi, (F) for US and (I) for Venezuela). Refer to Figure S3 in Supplementary file S1 for diversity of functional profiles.
Figure 9
Figure 9. Functional pathways that are differentially distributed between adults and children microbiome.
Figure 10
Figure 10. Significant taxonomical variation of microbial community compositions was observed among gut samples from different countries of all age stages.
Meta-Storms distances between the Malawi samples and the Venezuela samples were smaller than that between other pairs. Functional profiles exhibited a similar pattern (refer to Figure S4 in Supplementary file S1 for details).
Figure 11
Figure 11. Community structure patterns parsed from metagenomic shotgun sequences of Dataset 4.
(A) Simulation design at the genus level; (B). Parallel-META 3 analysis results at the genus level; (C). β diversity comparison via PCA among Parallel-META 3 (16S rRNA copy number calibration enabled; the default option), Parallel-META 3 (16S rRNA copy number calibration disabled) and simulation design.

Similar articles

See all similar articles

Cited by 17 articles

See all "Cited by" articles


    1. Segata N. et al. . Computational meta’omics for microbial community studies. Mol Syst Biol 9, 666 (2013). - PMC - PubMed
    1. Turnbaugh P. J. & Gordon J. I. The core gut microbiome, energy balance and obesity. J Physiol 587, 4153–4158 (2009). - PMC - PubMed
    1. Sunagawa S. et al. . Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015). - PubMed
    1. Kyrpides N. C., Eloe-Fadrosh E. A. & Ivanova N. N. Microbiome data science: understanding our microbial planet. Trends in Microbiology 24, 425–427 (2016). - PubMed
    1. Sunagawa S. et al. . Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods 10, 1196-+ (2013). - PubMed

Publication types