Parallel-META 3

  • Introduction
  • Parallel-META is a comprehensive and full-automatic computational toolkit for rapid data mining among metagenomic datasets. Both metagenomic shotgun sequences and 16S/18S/ITS rRNA amplicon sequences are accepted. Based on parallel algorithms and optimizations, Parallel-META 3 can achieve a very high speed compare to traditional microbiome analysis pipelines. Now the Parallel-META 3 version 3.6 is available at

  • Download
  • The following functions have been updated in the new version of Parallel-META 3.

    • Analysis of Microbiome taxonomy based on ASV (Amplicon Sequence Variant)
    • Add optional parameters, -v is used to choose ASV denoising, -c is used to choose chimera removal, -d is used to choose sequence alignment threshold
    • Add a new database, GreenGenes in 99% level

    See Release Note for more details. We strongly recommend all users to install/update the latest version.

    3.6 (Dec. 7, 2020) Release Note

    X86-64 (src package)
    Mac (src package)

    You can also get the lastest release from Parallel-META GitHub respository.

    • System requirement
    • Mac OS X needs to install the compiler that supports OpenMP by
      brew install gcc
    • Quick installation
    • Type the following commands in the directory that contains the package for quick installation:
      tar -xzvf parallel-meta-3.6-src.tar.gz #Extract the package
      cd parallel-meta#Go to the package directory
      source installation
    • The example dataset could be found at “example” folder. Check the “example/Readme” for details about the demo run.
  • Tutorial and Demo dataset
  • Here we provide a demo dataset with 20 environment microbiome samples in five different environment(feces, tongue, palm, soil, marine). See the tutorial and"Readme" in the dataset package for details.

    Tutorial of Parallel-META 3

    Sample Dataset and Sample Output (by 3.6)

  • 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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