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[…] Sequencing reads were assigned to each sample based on unique barcodes, and truncated by cutting off the barcode and primer sequence. The original DNA fragments were merged into tags using FLASH (v1.2.7). Quality filtering of the raw tags was performed under specific filtering conditions to generate high-quality clean tags according to the QIIME (v1.7.0) quality controlled process. In order to generate effective tags, the chimeric sequences were removed from the clean tags using the UCHIME algorithm based on the reference database (Gold database). After selecting representative species for each operational taxonomic unit (OTU), each of the remaining sequences was assigned to an OTU when at least 97% threshold identity was obtained using UPARSE software (v7.0.1001). The taxonomy of each OTU representative sequence was assigned for further annotation using the Ribosomal Database Project (RDP) classifier (v2.2) based on the GreenGenes Database. Subsequently, the information pertaining to OTU abundance was normalized using a standard sequence number corresponding to the sample with the least number of sequences. Subsequent analyses were performed based on the normalized data. Alpha diversity analysis is usually used to study the complexity of species diversity in samples. In this study, we evaluated the alpha diversity of the samples using the Chao1 and Shannon indices,, which were calculated by QIIME (v1.7.0). Moreover, we evaluated the sample size and the sequencing depth using Good’s coverage index. Beta diversity analysis was used to evaluate the differences in microbial community composition in the samples. In this study, the weighted UniFrac distance matrixes were calculated by QIIME (v1.7.0) to evaluate the differences in the microbiotal community compositions of all the test samples from the three gut locations, and the results were visualized by PCoA and dendrograms using R software (v2.15.3) and FastTree (v1.9.0), respectively. Finally, in order to evaluate the differences in phylogenetic composition between the groups, multi-response permutation procedure (MRPP) analysis was performed.Linear discriminant analysis coupled with the effect size (LEfSe) algorithm was used to identify the OTUs that differed significantly among the three groups (ileum, cecum, and colon) based on the OTU relative abundance values. Briefly, the algorithm first used the non-parametric factorial Kruskal-Wallis (KW) sum-rank test to detect the taxa with significantly different abundances, followed by pairwise Wilcoxon tests to detect biological consistency between the two groups. Finally, an LDA score was used to estimate the effect size of each differentially abundant feature. The functional capacity of the gut microbiome was estimated by inferring metabolic functionality from the 16S rRNA gene sequencing data using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) v1.0 software. Z-scores were calculated to construct a heatmap to demonstrate the relative abundance of the pathways in each group with the formula z = (x − μ)/σ, where x is the relative abundance of the pathways in each group, μ is the mean value of the relative abundances of the pathways in all groups, and σ is the standard deviation of the relative abundances. The Wilcoxon Signed-Ranks test was also used to determine the significance of the OTU number, alpha diversity, gene pathways, and OTU relative abundance between the sample groups. […]

Pipeline specifications

Software tools QIIME, UCHIME, UPARSE, RDP Classifier, UniFrac, FastTree, LEfSe, PICRUSt
Databases Greengenes
Applications Phylogenetics, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Sus scrofa, Escherichia coli
Chemicals Fatty Acids