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.



Questions and comments shall be addressed to lxia at usc dot edu.


  1. 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