PROJECT TITLE :

Infer Metagenomic Abundance and Reveal Homologous Genomes Based on the Structure of Taxonomy Tree

ABSTRACT:

Metagenomic research uses sequencing technologies to research the genetic biodiversity of microbiomes presented in varied ecosystems or animal tissues. The composition of a microbial community is very associated with the setting in which the organisms exist. As giant quantity of sequencing short reads of microorganism genomes obtained, accurately estimating the abundance of microorganisms among a metagenomic sample is changing into an increasing challenge in bioinformatics. During this paper, we tend to describe a hierarchical taxonomy tree-based mixture model (HTTMM) for estimating the abundance of taxon at intervals a microbial community by incorporating the structure of the taxonomy tree. In this model, genome-specific short reads and homologous short reads among genomes will be distinguished and represented by leaf and intermediate nodes in the taxonomy tree, respectively. We tend to adopt an expectation-maximization algorithm to resolve this model. Using simulated and real-world data, we tend to demonstrate that the proposed methodology is superior to each flat mixture model and lowest common ancestry-based strategies. Moreover, this model can reveal previously unaddressed homologous genomes.