Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks

Abstract

Abstract Background To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0–20 for QIIME1 and 0–30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples. Results Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region. Conclusions Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2.

Keywords

• QIIME
• Quality trimming
• Paired-end reads
• Diversity analysis
• Optimization