Optimization of denoising and filtering parameters of DADA2 for QIIME2 amplicon metagenomics data analysis

Abstract

BackgroundHigh-throughput sequencing generates vast data, often containing low-quality bases, chimeras, and artifacts that can mislead taxonomic classification and diversity assessments. DADA2 enhances taxonomic resolution by excluding low-quality bases and optimizing ASV inference. Proper truncation reduces computational load while maintaining key hypervariable regions for accurate classification. In this study, we examine the effect of various truncation lengths during the DADA2 analysis in ensuring statistical robustness and improving the reliability of microbial community profiling in ecological and environmental studies. ResultsTruncation of read length improves the quality reads recovery rate, and preserves microbial diversity in the V4 hypervariable region of Illumina paired-end reads. ConclusionIncorporating the best truncation length strategy optimizes the reads recovery and preserves the richness and evenness of microbial communities.