Using QIIME to analyze 16S rRNA gene sequences from Microbial Communities

By J. Kuczynski, Jesse Stombaugh, William Walters, Antonio Gonzalez, J. Gregory Caporaso, Rob Knight. Current Protocols in Bioinformatics, 2011

1,159 citations recorded by the source corpus.

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. Curr. Protoc. Bioinform. 36:10.7.1‐10.7.20. © 2011 by John Wiley & Sons, Inc.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7517135a9c864f023e3965f92ae3f9372e5bda8e

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Citations in the corpus

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, methodology): “…9 papers related to the development of ML methods for microbiome longitudinal analysis that are mainly based on the reuse of ﬁve datasets (Caporaso et al., 2011; Gajer et al., 2012; David et al., 2014; La Rosa et al., 2014; DiGiulio et al., 2015) with Gajer et al. being reused in four of them.”
EMPeror: a tool for visualizing high-throughput microbial community data (2013, background): “The main features that EMPeror provides are: (1) easily change visibility features of data points in the plots based on metadata; (2) can be easily embedded into other tools, such as Evident [16] as a reusable visualization component; (3) scale to thousands of points with minimal load times (seconds”
Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study (2016, background): “0 [51, 52]) and the Greengenes database (database/13_8) [54] preclustered at 97 % identity.”
Exercise Alters Gut Microbiota Composition and Function in Lean and Obese Humans (2017, methodology): “0 (25) at an even sample depth of 14,038 sequences per sample, visualized using EMPeror (26), and analyzed by permutational multivariate analysis of variance (PERMANOVA).”
The Gut Microbiota and Inflammation: An Overview (2020, methodology): “Alpha diversity refers to the variation within a single sample, whilst beta diversity refers to that between samples [13].”
The amphibian microbiome: natural range of variation, pathogenic dysbiosis, and role in conservation (2017, background): “Sources Faith (1992), Moreira and López-Garcı́a (2011), Lozupone et al. (2011), Kuczynski et al. (2011), Morgan and Huttenhower (2012), Ursell et al. (2012), Swenson (2014a, b) Alpha diversity within-sample taxonomic diversity, acts as a summary statistic of a single population Beta diversity…”
Activity, abundance and community structure of anammox bacteria along depth profiles in three different paddy soils (2015, methodology): “The calculation of operational taxonomic units (OTUs) at the 97% identity and diversity indices of anammox bacteria 16S rRNA genes was carried out using QIIME software (Kuczynski et al., 2011).”