Genome Relative Abundance using Mixture Models

A tool for shotgun metagenomics analysis


GRAMMy is a computational framework developed for Genome Relative Abundance using Mixture Model theory (GRAMMy) based estimation. Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We developed the Genome Relative Abundance using Mixture Model theory (GRAMMy) approach estimate genome relative abundance based on shotgun reads. GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets.


Figure 1.  The GRAMMy model. A schematic diagram of the finite mixture model underlies the GRAMMy framework for shotgun metagenomics.


  • Download released source code package here and install. Look into the README.txt file within the package (also viewable from for detailed installation information and others.
  • Development source code access at:
  • A test example with step by step explanation can be found in ‘test/’ within the package.


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D Ai, R Huang, J Wen, C Li, J Zhu, LC Xia. Integrated Metagenomic Data Analysis Demonstrates That A Loss Of Diversity In Oral Microbiota Is Associated With Periodontitis. BMC Genomics 18 (S1), 1041 (2017)

Li C. Xia, Jacob A. Cram, Ting Chen, Jed A. Fuhrman, Fengzhu Sun Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE 2011, 6(12):p.e27992