Evaluating the effect of QIIME balanced default parameters on metataxonomic analysis workflows with a mock community

Identifier:  imarina:5874111

Handle: https://hdl.handle.net/20.500.11797/imarina5874111

Authors:  Kioroglou, D; Mas, A; Portillo, MD

Abstract:
Copyright © 2019 Kioroglou, Mas and Portillo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 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.