Computational protocol: Next generation sequencing for gut microbiome characterization in rainbow trout (Oncorhynchus mykiss) fed animal by-product meals as an alternative to fishmeal protein sources

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Protocol publication

[…] The sequencing raw data were processed by the QIIME pipeline [] using the “closed reference” out picking strategy. Raw reads quality has been checked using FastQC v0.11.2 [], and R1 and R2 paired reads were joined using QIIME with the “SeqPrep” join method. The quality control was performed by QIIME, setting the phred_quality_threshold to 19 (Phred ≥ Q20). Reads were collected into OTUs (with identity ≥ 97%) using QIIME closed reference otu picking strategy against reference QIIME formatted Greengenes v.13.8 database (http://greengenes.lbl.gov). The taxonomical classification was performed down to genus level. OTUs assigned to the phylum Cyanobacteria (class Chloroplast), were considered potential plant contaminants and removed from the downstream analysis. Reads of mitochondrial or eukaryotic origin were also excluded. Singletons (OTUs with only one read associated) were excluded using the “filter_otus_from_otu_table.py" QIIME script.Alpha and beta diversity statistics have been performed using QIIME scripts ‘alpha_rarefection.py’ and ‘jackknifed_beta_diversity_.py’, respectively. In the calculation of alpha diversity metrics, the normalization was performed using the "rarefaction" QIIME process with standard parameters setting the “max_rare_depth” (upper limit of rarefaction depths) to lowest sample size. Alpha diversity metrics were calculated using ‘observed species’, ‘Chao1 index’ (species richness estimator), ‘Shannon’s diversity index’ and ‘Good’s coverage’. An alpha-rarefaction plot was created for each metric. The alpha diversity values at the same rarefaction level (at the lowest sample size) were calculated.Beta diversity metrics is an estimation of between-sample diversity of microbial profile and it was calculated by QIIME ‘jackknifed_beta_diversity_.py’ script. This script performed a jackknife iterative resampling method to normalize data, using a subsampling at 75% of the lowest sample size. We used both weighted (presence/absence/abundance matrix) and unweighted (presence/absence matrix) UniFrac distances [,]. The distance matrices were graphically visualized by three-dimensional PCoA representations. [...] Normality and homoscedasticity of all data were checked by Shapiro–Wilk’s and Levene’s test, respectively, using STATISTICA v.7 (StatSoft, Inc). One-way analysis of variance (ANOVA) was performed on growth performance, feed conversion and α-diversity data. Statistical significance was set at P-value < 0.05, and Fisher's Least Significant Difference (LSD) test was applied for multiple comparisons, when the overall ANOVA resulted significant.The number of reads across samples was normalized by sample size and the relative abundance (%) of each taxon was calculated. Only those taxa with an overall abundance of more than 1% (up to family level) and 0.5% at genus level were considered for statistical analysis.Statistical analysis of intestinal microbial profiles was performed using the Statistical Analysis of Metagenomics Profiles (STAMP) program (http://kiwi.cs.dal.ca/Software/STAMP), retaining unclassified reads []. P-values were calculated by ANOVA followed by Tukey-Kramer post-hoc test and correction of multiple testing was done using Benjamini–Hochberg False Discovery Rate (FDR) method [].Differences in the beta diversity of bacterial communities were verified using the non-parametric Permutational Multivariate Analysis of Variance (PERMANOVA) and adonis tests with 999 permutations. Both tests were available with QIIME script ‘compare_categories.py’. A “by diet” pairwise significance test was also performed. For each pairwise contrast a filtered distance matrix containing only the samples to be compared was created using the “filter_distance_matrix.py” QIIME script, then a PERMANOVA significance test on each pairwise filtered matrix was performed using the “compare_categories.py” QIIME script. […]

Pipeline specifications

Software tools QIIME, FastQC, SeqPrep, UniFrac, Statistica, STAMP
Applications Miscellaneous, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Oncorhynchus mykiss, Tetraodon lineatus, Danio rerio, Firmicutes, Bacteroidetes
Chemicals Amino Acids