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 3.5 is available at http://bioinfo.single-cell.cn/parallel-meta.html.

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

    • a. Support of ITS sequences;
    • b. Support of OTU table format for PM-pipeline (-T);
    • c. Support of OTU table format for PM-rand-rare (-T);
    • d. Added PM-predict-func-contribute to calculate the contribution of OTUs to functional profiles;
    • e. IO format and performance optimization;
    • f. Warning and error information in error.log;
    • g. Removed PM-split-table and PM-split-matrix;
    • h. Changed some binary file names for easy understanding;

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

    3.5 (Feb 11, 2019) Release Note

    X86-64 (bin package) X86-64 (src package)
    Mac (bin package) Mac (src package)
  • Tutorial and Demo dataset
  • 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 and Sample Output (by 3.5)

  • 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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