| Literature DB >> 32343490 |
Mehrbod Estaki1, Lingjing Jiang2, Nicholas A Bokulich3,4, Daniel McDonald1, Antonio González1, Tomasz Kosciolek1,5, Cameron Martino6,7, Qiyun Zhu1, Amanda Birmingham8, Yoshiki Vázquez-Baeza7,9, Matthew R Dillon3, Evan Bolyen3, J Gregory Caporaso3,4, Rob Knight1,7,10,11.
Abstract
QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses-e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org.Entities:
Keywords: QIIME 2; Qiita; bioinformatics; metagenomics; microbiome
Mesh:
Year: 2020 PMID: 32343490 PMCID: PMC9285460 DOI: 10.1002/cpbi.100
Source DB: PubMed Journal: Curr Protoc Bioinformatics ISSN: 1934-3396