Effect of trimming conditions for DADA2 on oral microbiome study in QIIME2 platform
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초록
Accurate identification of microbes facilitates the prediction, prevention, and treatment of human diseases. To increase the accuracy of microbiome data analysis, a long region of the 16S rRNA is commonly sequenced via paired-end sequencing. In paired-end sequencing, a sufficient length of overlapping region is required for effective joining of the reads, and high-quality sequencing reads are needed at the overlapping region. Trimming sequences at the reads distal to a point where sequencing quality drops below a specific threshold enhance the joining process. In this study, we examined the effect of trimming conditions on the number of reads that remained after quality control and chimera removal in the Illumina paired-end reads of the V3–V4 hypervariable region. We also examined the alpha diversity and taxa assigned by each trimming condition. Optimum quality trimming increased the number of good reads and assigned more number of operational taxonomy units. The pre-analysis trimming step has a great influence on further microbiome analysis, and optimized trimming conditions should be applied for Divisive Amplicon Denoising Algorithm 2 analysis in QIIME2 platform.
Abstract
Accurate identification of microbes facilitates the prediction, prevention, and treatment of human diseases. To increase the accuracy of microbiome data analysis, a long region of the 16S rRNA is commonly sequenced via paired-end sequencing. In paired-end sequencing, a sufficient length of overlapping region is required for effective joining of the reads, and high-quality sequencing reads are needed at the overlapping region. Trimming sequences at the reads distal to a point where sequencing quality drops below a specific threshold enhance the joining process. In this study, we examined the effect of trimming conditions on the number of reads that remained after quality control and chimera removal in the Illumina paired-end reads of the V3–V4 hypervariable region. We also examined the alpha diversity and taxa assigned by each trimming condition. Optimum quality trimming increased the number of good reads and assigned more number of operational taxonomy units. The pre-analysis trimming step has a great influence on further microbiome analysis, and optimized trimming conditions should be applied for Divisive Amplicon Denoising Algorithm 2 analysis in QIIME2 platform.
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