Computational protocol: The Effect of Dietary Supplementation with Spent Cider Yeast on the Swine Distal Gut Microbiome

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Protocol publication

[…] The sequences from faecal DNA samples of 16 animals (8 control and 8 treatment) at two time points (day 0 and day 21) were processed and analyzed to determine differences at all taxonomic and community levels using PANGEA (Pipeline for Analysis of Next GEneration Amplicons) . Since the data for one animal at day 0 is not available there are 15 animals in day 0, 8 animals for the control and 8 animals for the treatment group at day 21. In PANGEA small sequences (<100) are discarded, poor quality (phred quality score <20) ends are trimmed, 16S rRNA gene sequences are separated by representative barcode, and the closest cultured relative member of each sequence is identified using MEGABLAST against a modified bacterial RDP-II database prepared using Taxcollector downloaded on Nov 2010 . The significant differences of taxa (Phylum, Class, Order, Family, Genus and Species) between control and treatment animals are determined using a modified χ2-test which includes a false discovery rate (fdr) determination to get a P-value for the null hypothesis. The unclassified sequences were clustered with a sequence identity threshold at 0.8 similarities to Domain/Phylum, 0.9 to Class/Order/Family, 0.95 to Genus and 0.99 to the Species level. In order to quantitatively estimate the microbial diversity, the reads were normalized to the number of reads in the sample which had the smallest number of reads. Qualitative analysis is performed with the unnormalized reads. In order to evaluate the similarities and differences in the diversity of microbial communities between the groups, FastUnifrac was performed using the default options in QIIME using the QIIME virtual machine 1.1.0 . A beta diversity distance matrix is computed by Qiime which is then used to build the unweighted pair group method with arithmetic mean (UPGMA) tree. This tree is visualised using FigTree . To validate the UPGMA results, Jackknifing analysis is also performed and the results presented. For UPGMA clustering, a constant random number of sequences are selected from each animal to generate an UPGMA tree which is compared to the UPGMA tree built from all the animals using 100 permutations to generate the tree nodes. Jackknifing is performed with 700 sequences randomly selected from the animals using 100 permutations to get the Jackkniffe support values. The same database (Taxcollector) used in PANGEA is also used for all the Qiime analysis. Sample richness is calculated for all animals in day 0, control animals in day 21 and treatment animals in day 21. In order to provide a better understanding on how dietary cider yeast affects the diversities of microbial populations in each pig gut microbiome, sample richness analysis was also performed on each individual animal at day 0 and day 21. This was performed as follows: the sequences from each animal were aligned using MUSCLE and the aligned sequences were used to generate a distance matrix using the dnadist subroutine in the PHYLIP package . This distance matrix is then read using MOTHUR and the statistical quantities are calculated. The changes in the percentage of relative abundance values were calculated from the number of occurrences in the control group (Nc21) and the number of occurrences in the treatment group (Nt21) using the following equation . Sequence reads obtained from this study are available from the Sequence Read Archive (SRA) (, under study accession number SRP028111. […]

Pipeline specifications

Software tools PANGEA, BLASTN, QIIME, FigTree, MUSCLE, PHYLIP, mothur
Databases SRA
Applications Phylogenetics, Nucleotide sequence alignment
Organisms Saccharomyces cerevisiae, Sus scrofa, Escherichia coli
Diseases Animal Diseases