Parallel-META 3

  • Introduction
  • Parallel-META is a comprehensive and full-automatic computational toolkit for rapid data mining among metagenomic datasets, with advanced features including sequence profiling and OTU picking, rRNA copy number calibration, functional prediction, diversity statistics, bio-marker selection, interaction network construction, vector-graph-based visualization and parallel computing. Both metagenomic shotgun sequences and 16S rRNA amplicon sequences are accepted.

    Parallel-META is open source, and it is implemented using C++&R. Based on parallel algorithms, Parallel-META can achieve a very high speed compare to traditional metagenomic analysis pipelines. The executive binary is built as an integrated package for rapid installation and easy access under Linux X86, X86-64 and Mac OS X. Both binary and source code packages are available.

  • Download
  • Now version 3.4.1 is released (Mar 2, 2017) with some significant updates such as the pair-wised Alpha & Beta diversity analysis and supports to numerical meta-data. We strongly recommend all users to install/update the latest version.

    3.4.1 (Mar 2, 2017) Release Note

    X86-64 (bin package) X86-64 (src package)
    X86 (bin package) X86 (src package)
    Mac (bin package) Mac (src package)
  • Tutorial and Sample datasets
  • Here we provide a Tutorial with 20 human oral microbial community samples sequenced by 454 FLX in 2 different healthy conditions (“B” for healthy baseline, “I” for natural gingivitis) produced by Huang, et al., 2014. See the tutorial and"Readme" in the dataset package for details.

    Tutorial of Parallel-META 3

    Sample Dataset

     

  • Publications
  • 1. Jing., et al., Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities, Scientific Reports, 2017.

    2. Su X., Pan W., et al.,Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization, PLoS ONE, 2014.

    3. Su X., et al. Parallel-META: Efficient Metagenomic Data Analysis Based on High-Performance Computation, BMC Systems Biology, 2012.

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