Introducing SONS, a Tool for Operational Taxonomic Unit-Based Comparisons of Microbial Community Memberships and Structures

By Patrick D. Schloss, Jo Handelsman. Applied and Environmental Microbiology, 2006

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View Paper: https://www.semanticscholar.org/paper/05892bfbab646b5526af3563f29ea96f9b2c7040

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Citations in the corpus

Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. (2009, background): “…(or other taxonomic units) are likely to be in a sample (Chao 1984; Schloss and Handelsman 2005), for comparing which OTUs are shared among particular subsets of samples (Schloss and Handelsman 2006), or for building networks that relate species and samples to one another (Ley et al. 2008a).”
Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data (2012, methodology): “Several methods to analyze metagenomic data have been proposed based on exploratory cluster analysis, bootstrap or resampling methods, and application of univariate and non-parametric statistics to subsets of the data [4–12].”
Translocation of nitrogen and carbon integrates biotic crust and grass production in desert grassland (2008, methodology): “The fraction of Pleosporales OTUs common to root, soil and crust communities was determined by counting shared OTUs and calculating a similarity index using the sons program (Schloss & Handelsman 2006).”
Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples (2009, background): “DOTUR [9], Libshuff [10], # -libshuff [11], SONs [12], MEGAN [13], UniFrac [14], and TreeClimber [15] all focus on different aspects of such an analysis.”
Bacterial diversity in cucumber (Cucumis sativus) rhizosphere in response to salinity, soil pH, and boron (2010, methodology): “The computer program DOTUR (Schloss and Handelsman, 2005, 2006) was used to calculate species richness estimates and diversity indices from sequence data.”
Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies (2015, methodology): “It was created from the combination of pre-existing software, such as DOTUR, 134 SONS, 135 and Treeclimber, 136 but, due to the community support it has received, currently it incorporates many more algorithms, thus providing the user with a variety of choices.”
Diversity of rock varnish bacterial communities from Black Canyon, New Mexico (2010, methodology): “The similarity between each clone library was calculated using S‐Libshuff (http:// www.plantpath.wisc.edu/joh/s‐libshuff.html [Schloss et al., 2004; Singleton et al., 2001]) and SONS [Schloss and Handelsman, 2006].”
Deciphering Diversity Indices for a Better Understanding of Microbial Communities. (2017, background): “When microbiota are collected from a certain niche, there is a need to evaluate how well a sample reflects the true diversity of the specific niche, which is synonymous with species richness and relative abundance in time and space [14].”
Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis (2012, background): “The u index takes into consideration the number of bacterial species present and their relative abundances in the two communities being compared (Yue and Clayton 2005; Schloss and Handelsman 2006) with u = 1 indicating identical community structure and u = 0 indicating absolute dissimilarity.”
Advancing our understanding of the human microbiome using QIIME (2013, background): “The choice of metrics is critical in beta-diversity analysis as metrics differ substantially in their ability to detect clustering or gradient patterns among microbial communities on the same dataset (Arumugam et al., 2011; Ravel et al., 2012; Schloss & Handelsman, 2006).”
A diversity profile of the human skin microbiota. (2008, methodology): “Clustering and comparison of 16S libraries All calculations using the SONS package were performed using an OTU cutoff of 97% or 99% similarity, as indicated in the text (Schloss and Handelsman 2006).”