Evaluating the Effect of QIIME Balanced Default Parameters on Metataxonomic Analysis Workflows With a Mock Community

Abstract

Metataxonomic analysis represents a fast and cost-effective approach for acquiring informative insight into the composition of the microbiome of samples with variable diversity, such as wine samples. Nevertheless, it comprises a vast amount of laboratory procedures and bioinformatic frameworks each one associated with an inherent variability of protocols and algorithms, respectively. As a solution to the bioinformatic maze, QIIME bioinformatic framework has incorporated benchmarked, and balanced parameters as default parameters. In the current study, metataxonomic analysis of two types of mock community standards with the same microbial composition has been performed for evaluating the effectivess of QIIME balanced default parameters on a variety of aspects related to different laboratory and bioinformatic workflows. These aspects concern NGS platforms, PCR protocols, bioinformatic pipelines, and taxonomic classification algorithms. Several qualitative performance expectations have been the outcome of the analysis, rendering the mock community a useful evaluation tool.

High-Throughput Sequencing Approach to Analyze the Effect of Aging Time and Barrel Usage on the Microbial Community Composition of Red Wines

“…The processing of the raw amplicon sequences has been performed using Quantitative Insights into Microbial Ecology (QIIME versions 1.9.1 and 2018.2) implementing the Illumina OTU pipeline steps previously described (Kioroglou et al, 2019) with a Phred33 quality filtering threshold of <20, 99% similarity threshold during OTU clustering, and BLAST+ as taxonomic classification algorithm (Camacho et al, 2009). After quality filtering and taxonomic classification, exclusion of sequences matching chloroplast or mitochondria was performed.…”

Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing

“…At present, commercial mock communities are available from the American Type Culture Collection (ATCC) ( www.atcc.org ), BEI resource ( www.beiresources.org ) and Zymo Research ( www.zymoresearch.com ). Notably, as in this and previous studies, the use of in-house developed mock communities is also encouraged because they can more accurately reflect the variability of interesting or important bacteria than the commercially available communities [13] , [38] , [72] .…”

“…16Ss is used to identify and classify microbes by selectively amplifying and sequencing the hypervariable regions of the 16S rRNA gene. As 16Ss is high throughput (ten to hundreds of microbiotas in a single sequencing run) [11] , is cost effective [12] and has increasingly accessible bioinformatics tools [13] , it has become a widely deployed method for profiling complex microbial communities [14] , [15] . SMs sequences the genomes of all the microbes isolated from the entire microbial community.…”

The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients

“…has been continuously stressed in various metagenomic studies, emphasizing the importance of using proper control communities for correctly characterizing the investigated microbiome (Brooks et al, 2015;Fouhy et al, 2016;Kioroglou et al, 2019). The use of in house bacterial mock communities, such as we have done in our study, is often encouraged because it can reflect the variability of interesting or relevant taxa more accurately than commercially available communities (Han et al, 2020).…”