Computational protocol: The relationships between faecal egg counts and gut microbial composition in UK Thoroughbreds infected by cyathostomins

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Protocol publication

[…] Following trimming of primer sequences using Cutadapt (, raw paired-end Illumina reads were joined using the Quantitative Insights Into Microbial Ecology (QIIME) software suite (version 1.9.0) () and quality filtered using the ‘usearch_qf’ script with default settings. Then, high-quality sequences were clustered into Operational Taxonomic Units (OTUs) on the basis of similarity to bacterial sequences available in the Greengenes database (v13.8;; 97% sequence similarity cut-off) using the UCLUST software; sequences that could not be matched to references in the Greengenes database were clustered de novo based on pair-wise sequence identity (97% sequence identity cut-off) (cf. ). Singleton OTUs and OTUs assigned to sequences obtained from no-DNA control samples were subtracted from individual datasets prior to downstream analysis. For normalisation, a subsampled OTU table was generated by random sampling (without replacement) of the input OTU table using an implementation of the Mersenne twister algorithm ( Cumulative-sum scaling (CSS) and log2 transformation were applied to account for the non-normal distribution of taxonomic counts data. Statistical analyses were conducted on the Calypso platform (; samples were clustered using supervised Canonical Correspondence Analysis (CCA) including FEC (Chigh:Clow) and time-point (D0, D2 and D14 p.t.) as explanatory variables. Differences in bacterial alpha diversity (Shannon diversity) between groups were evaluated using a paired t-test or ANOVA (depending on the number of groups for comparison). Beta diversity of microbial communities was calculated using weighted UniFrac distances and, based on the matrices, differences in beta diversity between groups were calculated using Permutational Analysis of Multivariate Dispersions (PERMDISP) through the ‘betadisper’ function (). Differences in the relative abundances of individual microbial species between groups were assessed using the Linear discriminant analysis Effect Size (LEfSe) workflow (), by assigning FEC/timepoint ‘groupings’ as the comparison class. All statistical analyses were repeated on a sub-group of horses with FEC ≥200 e.p.g. (n = 8) and 0 e.p.g. (n = 7), hereafter referred to as ‘C200’ and ‘C0’, respectively. P < 0.05 was considered statistically significant. […]

Pipeline specifications

Software tools cutadapt, QIIME, USEARCH, UCLUST, Numpy, LEfSe
Databases Greengenes