Parallel-META

  • Introduction
  • Parallel-META is a 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.

     

  • Sample datasets
  • Here we provide human oral microbial community sampless equenced by 454 Titanium in three different healthy conditions (“B” for healthy baseline, “I” for natural gingivitis and “E” for experimental gingivitis) produced by Huang, et al., 2014.

    Dataset1, 150 samples in total, 3 healthy status;

    Dataset2, 20 samples in total, 2 healthy status.

     

  • Download
  • Now 3.3.1 Released (Aug 2, 2016)

    3.3.2 (Sep 28, 2016) X86-64 (bin package / src package)
    X86 (bin package / src package)
    Mac (bin package)
    3.3.1 (Aug 2, 2016) X86-64 (bin package / src package)
    X86 (bin package / src package)
    3.3 (Jun 3, 2016) X86-64 (bin package / src package)
    X86 (bin package / src package)
    3.2.1 (Apr 13, 2016) X86-64 (bin package / src package)
    X86 (bin package / src package)
    3.2.0 (Jan 14, 2016) X86-64 (bin package / src package)
    X86 (bin package / src package)
    3.1.0 (Dec 09, 2015) X86-64 (bin package / src package)
    X86 (bin package / src package)
    3.0.1 ( Nov 03, 2015) X86-64 (bin package / src package)
    X86 (bin package / src package)

     

  • Publications
  • 1. 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.

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

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