add amplicon sequencing data analysis assay and protocol

assays/AmpliconSequencingDataAnalysis/protocols/AmpliconSequencingDataAnalysisProtocol.md

+## Amplicon sequencing data analysis
+
+Sequencing reads were demultiplexed using Qiime2 (qiime demux emp_paired) (Bolyen, E. et al., 2019), and merged using Flash2 (Magoč, T. and Salzberg, S.L., 2011). Reads were denoised and dereplicated using Dada2 (Callahan, B.J. et al., 2016) and remaining individual reads were denoted as ASVs. Chimeras were removed using Qiime2 (vsearch uchime-denovo). Taxonomic classification was done via the Qiime feature classifier using the silva_138 and sequences classified as mitochondrial or chloroplast were removed from the dataset. Remaining ASVs were included in count tables. Bray-Curtis dissimilarities between samples were calculated normalized ASV tables and performed PCAs using the cmdscale function (*vegan* R package). To quantify the effects of genotype and root region we used a constrained PCoA using the *capscale* function (*vegan* R package). To quantify the contribution of different variables and their interactions to the variance in pairwise Bray-Curtis dissimilarities, we analysed the Bray-Curtis distance matrix between pairs of samples with 999 iterations of a permutation-based test (permutational multivariate analysis of variance (PERMANOVA), adonis function, vegan R package), and removed technical and batch effects, using the formula as follows: *Bray-curtis ∼ VariableX + Condition(Technical_replicates + Biological_replicates)*. We further inspected the effects of the variables using a constrained PCoA using the capscale function (vegan R package). Sequencing data from the SynCom experiment was pre-processed similarly as natural community 16S rRNA data. Quality-filtered merged paired-end reads were aligned to a reference set of sequences extracted from the whole-genome assemblies of the 60 strains included in the SynCom using Rbec79 (v1.0.0). A count table that was employed for downstream analyses of diversity was generated in R (v4.0.3) with the R package vegan (v2.5–6). Scripts for microbiota analysis and data visualization can be found at https://github.com/duranpa/sweet_collaboration
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