Raw sequence reads

In this project, microbial diversity analysis includes the following operations:1, the original sequencing sequence quality control, including low quality filtering, length filtering, to obtain high quality sequences;2. Perform clustering/de-noising on high-quality sequences, divide OTUs/ASVs (later uniformly called Feature), and obtain their species classification according to the sequence composition of Feature;3. Based on the Feature analysis results, the samples were analyzed at various taxonomic levels, and the community structure map, species clustering heat map, phylogenetic tree and taxonomic tree of each sample were obtained at taxonomic levels of phyla, class, order, family, genus and species;4. Species diversity within a single sample was studied through Alpha diversity analysis, Ace, Chao1, Shannon and Simpson indices of each sample were counted, and sample dilution curves and grade abundance curves were drawn;5. The difference in species diversity (community composition and structure) of different samples was compared by Beta diversity analysis. According to the distance matrix, sample hierarchical clustering (UPGMA) tree, NMDS analysis, sample clustering heat map, sample PCA, PCoA diagram (with grouping information), box plots based on various distances are obtained.6. At the level of species taxonomic composition, the significance analysis of inter-group differences was used to further measure the differences in species abundance composition among different samples (groups), and statistically different biomarkers were found among different groups.7. According to the composition distribution of species in each sample, the correlation network was constructed, and network analysis, correlation heat map, RDA/CCA analysis, and regression analysis of environmental factor ranking were carried out;8. According to the results of 16S rRNA or ITS gene sequencing, the gene function or phenotype of the sample was predicted and the functional gene or phenotype abundance was calculated through functional prediction analysis.