Modeling And Analysis Of Metagenome Of Microbial Community

Metagenomic sequencing and Metatranscriptomic sequencing are critical to dis-cover and understand the dynamic changes of the microbial community.At present,there are two types of analysis tools for Metagenome data and Metatranscriptome data in microbial communities:the first is the analysis tool based on sequence alignment;the second is the alignment-free analysis tool.For alignment-based method,we improve alignment method based on the ampli-fication of 16S rRNA.We propose the analysis process sequence to combine the 16S r-RNA database with the whole genome database to assemble full-length 16S rRNA from whole genome data and compare it to the 16S rRNA database.We introduce a com-putational framework RBRA to assemble full-length 16S rRNA from whole genome data.Our study apply the improved alignment-based method to 27 blood samples from 10 cases of leukemia patients.The experimental results show that the improved com-munity analysis method based on sequence alignment can not only cover the microbes recognized in the traditional method based on sequence alignment,but also find the microorganisms that can not be recognized in the traditional method,Which provides a broader perspective for the analysis of microbial communities.For alignment-free method,In this paper,the FOMC model is replaced by another data-driven variable length Markov model(VLMC)to model the background sequence in the Metadata accordingly.For the improved alignment-free method,several β di-versity clustering methods based on VLMC are applied to the comparison of Bacterial RNA sequences and several sets of Metatranscriptomic data.The experimental results show that the modified VLMC has a significant effect on the modeling of the microbial community background sequence relative to the original FOMC.This article also apply the method of microbial community analysis to fecal bacte-ria transplantation data of human,to explore the overall differences between microbial community and to identify the difference between microbial community characteristics.