Identifying biologically relevant differences between metagenomic communities

By Donovan H. Parks, Robert G. Beiko. Bioinform., 2010

920 citations recorded by the source corpus.

Subfields: Ecology, Molecular Biology, Pollution

View Paper: https://www.semanticscholar.org/paper/022eaffc73e5c35701fa51462da1ab07b60e3bfa

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Citations in the corpus

Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences (2013, background): “Such functional categories also typically show large differences in relative abundance between similar communities (e.g. metal cation efflux 36 and nickel/ peptide transporters 19 ) and are enriched for lateral gene transfer 21, 37 .”
Common and distinguishing features of the bacterial and fungal communities in biological soil crusts and shrub root zone soils (2014, methodology): “, 2009) as implemented in the STAMP software package (Parks and Beiko, 2010).”
Computational meta'omics for microbial community studies (2013, methodology): “metric biomarker discovery (White et al, 2009; Parks and Beiko, 2010; Segata et al, 2011) and appropriately transformed regression models (Chen et al, 2012).”
A Phylogenomic View of Ecological Specialization in the Lachnospiraceae, a Family of Digestive Tract-Associated Bacteria (2014, methodology): “Final counts of reads designated as sporulationassociated were compared between habitats using STAMP version 2 (Parks and Beiko 2010) with a two-sided Welch’s t-test and Bonferroni multiple test correction.”
Tracking microbial colonization in fecal microbiota transplantation experiments via genome-resolved metagenomics (2017, methodology): “We then performed a Wilcoxon signed-rank test (STAMP) [53] with BenjaminiHochberg FDR (false discovery rate) correction for multiple tests on the total-sum normalized data to ascertain any significant differences in the functional potential between the groups and carried out canonical correspondence”
Biochar alters the soil microbiome and soil function: results of next‐generation amplicon sequencing across Europe (2017, methodology): “Finally, differential abundance testing of each taxonomic level (from phylum to genus) was carried out using STAMP (Parks & Beiko, 2010), using two-sided Whites nonparametric t-tests (White et al., 2009), with Benjamini–Hochberg FDR correction for multiple testing (Benjamini & Hochberg, 1995).”
Biological, physicochemical and plant health responses in lettuce and strawberry in soil or peat amended with biochar (2016, positive): “The STAMP analysis software was used to study individual differences in the bacterial groups (Parks and Beiko, 2010).”
STAMP: statistical analysis of taxonomic and functional profiles (2014, methodology): “The original release of STAMP (Parks and Beiko, 2010) was limited to comparing a single pair of taxonomic or functional profiles.”
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013, background): “Others have argued that characterizing biological data in this way is more informative than decisions based upon p-value thresholds because p-values encourage acceptance or rejection of a null hypothesis rather than an explicit assessment of the evidence[12,13].”
Metagenomic biomarker discovery and explanation (2011, methodology): “MEGAN, however, can only compare single pairs of metagenomes, as is also the case with STAMP [37], which does introduce a concept of ‘biological relevance’ in the form of confidence intervals.”