Using QIIME to analyze 16S rRNA gene sequences from microbial communities

Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, Colorado, USA.
Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.] 12/2011; Chapter 10:Unit 10.7.. DOI: 10.1002/0471250953.bi1007s36
Source: PubMed


QIIME (canonically pronounced "chime") is a software application that performs microbial community analysis. It is an acronym for Quantitative Insights Into Microbial Ecology, and has been used to analyze and interpret nucleic acid sequence data from fungal, viral, bacterial, and archaeal communities. The following protocols describe how to install QIIME on a single computer and use it to analyze microbial 16S sequence data from nine distinct microbial communities.

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Available from: William A Walters, Nov 03, 2014
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    • "quencing was performed , follow - ing an additional amplification step using the 27F - 519R primer pair for the 16S rRNA amplicon sequences on an Illumina MiSeq ( at Molecular Research LP ; Shallowater , TX , USA ) following the manufacturer ' s guidelines . 16S rRNA gene sequences were analyzed using the QIIME pipeline ( Caporaso et al . , 2010 ; Kuczynski et al . , 2011 ) . De novo Operational Taxonomic Units ( OTUs ) were defined at 97% sequence identity using UCLUST ( Edgar , 2010 ) and taxonomy was assigned to the Greengenes database ( version"
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    • "The identity for each OTU was determined using the Greengenes database (, version 13_5) with the RDP or BLAST classifier ( using QI- IME python scripts at the default 97% and 99% identity levels (Kuczynski and others 2011), as described later. For beta diversity , UniFrac distances were determined between all pairs of samples (Lozupone and others 2006). "
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    • "QIIME calculates both weighted and unweighted UniFrac. Principal coordinate analysis (PCoA) and unweighted pair group method with arithmetic mean (UPGMA) clustering were conducted by unweighted and weighted UniFrac based on the protocol published previously (Kuczynski et al. 2012). Canonical correspondence analysis (CCA) was performed to measure chemical properties that have the most significant influence on microbial communities. "
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