DF2
Multiplexed reads were assigned to biological samples and processed using the DADA2 package in R, which performs amplicon error modeling and correction without constructing OTUs. This method has been shown to detect more true variants and produce fewer spurious sequences than other approaches. The DADA2 pipeline transforms raw fastq files into high-resolution, chimera-free, sample-inferred sequences.Initial processing involved trimming PCR primer sequences and removing low-quality bases (Q<30) from the 3 ends of reads, while maintaining sufficient overlap for accurate merging of paired-end reads. DADA2 applies a parametric error model, which is learned from the data through an iterative process of error rate estimation and sample composition inference until convergence. This begins with a conservative assumption that only the most abundant sequences are correct. Subsequently, the denoising step identifies unique sequences and their abundances, calculating consensus quality scores from the input reads.Unlike traditional OTU-based methods, DADA2 resolves closely related sequence variants and performs paired-end read merging after denoising, improving accuracy by leveraging the empirical relationship between quality scores and error rates. As most substitution errors are removed prior to merging, exact overlap without mismatches is required. Chimeric sequences are then identified and removed.Following initial data processing, taxonomic classification of the ASVs (Amplicon Sequence Variants) was performed using DADA2s implementation of the naive Bayesian classifier, with the SILVA reference database. The resulting taxonomic assignments and abundance data were imported into the phyloseq package in R for downstream ecological and phylogenetic analyses. Phyloseq integrates several R tools such as VEGAN, ade4, ape, and Picante, and utilizes the ggplot2 system to generate high-quality visualizations.