Computational protocol: Meat, dairy and plant proteins alter bacterial composition of rat gut bacteria

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Protocol publication

[…] Raw fastq files were de-multiplexed and quality filtered using QIIME (version 1.17) with the following criteria: 1) the 250 bp reads were truncated at any site receiving an average quality score <20 over a 10 bp sliding window; 2) the truncated reads less than 50 bp were removed; 3) the specific barcodes were exactly matched; 4) the mismatching part with primers allowed was less than 2 bp; 5) reads containing ambiguous characters were removed; 6) only sequences that overlapped by more than 10 bp were assembled according to their overlap sequence; 7) reads that could not be assembled were discarded. Operational Taxonomic Units (OTU) were clustered with 97% similarity cutoff using UPARSE (version 7.1 and chimeric sequences were identified and removed using UCHIME. RDP classifier was used for taxonomical assignments of each sequence at 70% confidence level using 16S rRNA sequences from Silva release 119 ( Rarefaction analysis and alpha diversities were performed using Mothur (version v.1.30.1, Community richness was evaluated by Chao and ACE. Community diversity was evaluated by Shannon index and Simpson index. The Good’s coverage analysis was evaluated. Bray Curtis similarity clustering analysis was performed by R package (R 3.0.2, analysis was performed ( to find the highly-dimensional gut bacteria and characterize the differences between two or more biological conditions (or classes). The differences in features were identified at the OTU level. The six groups were categorized into three classes: red meat (beef and pork), white meat (chicken and fish) and non-meat (soybean and casein). The LEfSe analysis conditions were as follows: 1) alpha value for the factorial Kruskal-Wallis test among classes was less than 0.05; 2) alpha value for the pairwise Wilcoxon test among subclasses was less than 0.05; 3) the threshold on the logarithmic LDA score for discriminative features was less than 2.0; 4) multi-class analysis was set as all-against-all.Differentially abundant features of bacterial taxa at the OTU level were performed using Metastats (, which is a statistically strict method designed specifically to compare microbial communities on 16S rRNA abundance data.Differences in serum LBP level and relative abundance of bacteria among six groups were evaluated by one-way analysis of variance and Bartlett’s test, and means were compared by Duncan’s multiple comparison using SAS system (version 9.2), and p value less than 0.05 was declared significant. […]

Pipeline specifications

Software tools QIIME, UPARSE, UCHIME, RDP Classifier, mothur, LEfSe, Metastats
Applications Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Rattus norvegicus, Gallus gallus
Chemicals Heme