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Full-Text Articles in Life Sciences
The Gut Mycobiome Of The Human Microbiome Project Healthy Cohort, Andrea K. Nash, Thomas A. Auchtung, Matthew C. Wong, Daniel P. Smith, Jonathan R. Gesell, Matthew C. Ross, Christopher J. Stewart, Ginger A. Metcalf, Donna M. Muzny, Richard A. Gibbs, Nadim J. Ajami, Joseph F. Petrosino
The Gut Mycobiome Of The Human Microbiome Project Healthy Cohort, Andrea K. Nash, Thomas A. Auchtung, Matthew C. Wong, Daniel P. Smith, Jonathan R. Gesell, Matthew C. Ross, Christopher J. Stewart, Ginger A. Metcalf, Donna M. Muzny, Richard A. Gibbs, Nadim J. Ajami, Joseph F. Petrosino
Department of Food Science and Technology: Faculty Publications
Background: Most studies describing the human gut microbiome in healthy and diseased states have emphasized the bacterial component, but the fungal microbiome (i.e., the mycobiome) is beginning to gain recognition as a fundamental part of our microbiome. To date, human gut mycobiome studies have primarily been disease centric or in small cohorts of healthy individuals. To contribute to existing knowledge of the human mycobiome, we investigated the gut mycobiome of the Human Microbiome Project (HMP) cohort by sequencing the Internal Transcribed Spacer 2 (ITS2) region as well as the 18S rRNA gene.
Results: Three hundred seventeen HMP stool samples were …
Negative Binomial Mixed Models For Analyzing Microbiome Count Data, Xinyan Zhang, Himel Mallick, Zaixiang Tang, Lei Zhang, Xiangqin Cui, Andrew K. Benson, Nengjun Yi
Negative Binomial Mixed Models For Analyzing Microbiome Count Data, Xinyan Zhang, Himel Mallick, Zaixiang Tang, Lei Zhang, Xiangqin Cui, Andrew K. Benson, Nengjun Yi
Department of Food Science and Technology: Faculty Publications
Background: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data.
Results: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and …