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Open AccessJournal ArticleDOI

Differential abundance analysis for microbial marker-gene surveys

TLDR
It is shown that metagenomeSeq outperforms the tools currently used in this field and relies on a novel normalization technique and a statistical model that accounts for undersampling in large-scale marker-gene studies.
Abstract
We introduce a methodology to assess differential abundance in sparse high-throughput microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling-a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field.

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Citations
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Journal ArticleDOI

Discovery of Predictors of Mycoplasma hyopneumoniae Vaccine Response Efficiency in Pigs: 16S rRNA Gene Fecal Microbiota Analysis.

TL;DR: The results showed variability in antibody response among individual pigs, whilst pre-vaccination operational taxonomic units primarily belonging to Prevotella, [Prevotella], Anaerovibrio, and Sutterella appeared to best-predict vaccine response.
Journal ArticleDOI

Roots Mediate the Effects of Snowpack Decline on Soil Bacteria, Fungi, and Nitrogen Cycling in a Northern Hardwood Forest.

TL;DR: It is suggested that a declining winter snowpack and its effect on plant roots each have direct effects on the diversity and abundance of soil bacteria and fungal communities that interact to determine rates of soil N cycling in northern hardwood forests.
Journal ArticleDOI

Effects of Exogenous Yeast and Bacteria on the Microbial Population Dynamics and Outcomes of Olive Fermentations.

TL;DR: The findings show the distinct effects of exogenous spoilage and starter microbes on indigenous communities in plant-based food fermentations that result in very different impacts on product quality.
Journal ArticleDOI

Swift evolutionary response of microbes to a rise in anthropogenic mercury in the Northern Hemisphere.

TL;DR: It is shown that even low levels of anthropogenic mercury affected the evolutionary trajectory of microbes in the Northern Hemisphere, and that microbial DNA encoding for detoxification determinants stored in environmental archives can be used to track historical pollutant toxicity.
Posted ContentDOI

MUSiCC: A marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.

TL;DR: This work introduces an alternative normalization paradigm, MUSiCC, which combines universal single-copy genes with machine learning methods to correct biases and to obtain a more accurate and biologically meaningful measure of gene abundances.
References
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Journal ArticleDOI

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI

Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

TL;DR: The RDP Classifier can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes, and the majority of the classification errors appear to be due to anomalies in the current taxonomies.
Journal ArticleDOI

Differential expression analysis for sequence count data.

Simon Anders, +1 more
- 27 Oct 2010 - 
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
Journal ArticleDOI

Metagenomic biomarker discovery and explanation

TL;DR: A new method for metagenomic biomarker discovery is described and validates by way of class comparison, tests of biological consistency and effect size estimation to address the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities.
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