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.read more
Citations
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Journal ArticleDOI
SCNIC: Sparse correlation network investigation for compositional data
TL;DR: SCNIC as mentioned in this paper is an open-source software that can generate correlation networks and detect and summarize modules of highly correlated features, which can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD).
Book ChapterDOI
Host Phenotype Prediction from Differentially Abundant Microbes Using RoDEO
TL;DR: High-dimensional metagenomic sequencing is increasingly used in human and animal health, food safety, and environmental studies, where the phenotype of the host organism may not be obvious to detect and the ability to predict it becomes a powerful analytic tool.
Journal ArticleDOI
Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects
Pamela Vernocchi,Maria Vittoria Ristori,Silvia Guerrera,Valerio Guarrasi,Federica Conte,Alessandra Russo,Elisabetta Lupi,Sami Albitar-Nehme,Simone Gardini,Paola Paci,Gianluca Ianiro,Stefano Vicari,Antonio Gasbarrini,Lorenza Putignani +13 more
TL;DR: In this patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients’ age.
It takes guts to learn: machine learning techniques for disease detection from the gut microbiome.
TL;DR: A review of existing ML methods for disease classification from microbiome data can be found in this article, where the authors highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work.
Journal ArticleDOI
Alterations of the murine gut microbiome in allergic airway disease are independent of surfactant protein D
Kenneth Klingenberg Barfod,Michael Roggenbuck,Suzan Al-Shuweli,Dalia Fakih,Dalia Fakih,Søren J. Sørensen,Grith Lykke Sørensen +6 more
TL;DR: The results show that the composition of the microbiota is not influenced by the SP-D deficient genotype under naïve or OVA induced airway disease, however, OVA sensitization and pulmonary challenge did alter the gut microbiota, supporting a bidirectional lung-gut crosstalk.
References
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QIIME allows analysis of high-throughput community sequencing data.
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Journal ArticleDOI
Metagenomic biomarker discovery and explanation
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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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J. Gregory Caporaso,Justin Kuczynski,Jesse Stombaugh,Kyle Bittinger,Frederic D. Bushman,Elizabeth K. Costello,Noah Fierer,Antonio Gonzalez Peña,Julia K. Goodrich,Jeffrey I. Gordon,Gavin A. Huttley,Scott T. Kelley,Dan Knights,Jeremy E. Koenig,Ruth E. Ley,Catherine A. Lozupone,Daniel McDonald,Brian D. Muegge,Meg Pirrung,Jens Reeder,Joel Sevinsky,Peter J. Turnbaugh,William A. Walters,Jeremy Widmann,Tanya Yatsunenko,Jesse R. Zaneveld,Rob Knight,Rob Knight +27 more