Meta-analyses of studies of the human microbiota

Our body habitat-associated microbial communities are of intense research interest because of their influence on human health. Because many studies of the microbiota are based on the same bacterial 16S ribosomal RNA (rRNA) gene target, they can, in principle, be compared to determine the relative importance of different disease/physiologic/developmental states. However, differences in experimental protocols used may produce variation that outweighs biological differences. By comparing 16S rRNA gene sequences generated from diverse studies of the human microbiota using the QIIME database, we found that variation in composition of the microbiota across different body sites was consistently larger than technical variability across studies. However, samples from different studies of the Western adult fecal microbiota generally clustered by study, and the 16S rRNA target region, DNA extraction technique, and sequencing platform produced systematic biases in observed diversity that could obscure biologically meaningful compositional differences. In contrast, systematic compositional differences in the fecal microbiota that occurred with age and between Western and more agrarian cultures were great enough to outweigh technical variation. Furthermore, individuals with ileal Crohn's disease and in their third trimester of pregnancy often resembled infants from different studies more than controls from the same study, indicating parallel compositional attributes of these distinct developmental/physiological/disease states. Together, these results show that cross-study comparisons of human microbiota are valuable when the studied parameter has a large effect size, but studies of more subtle effects on the human microbiota require carefully selected control populations and standardized protocols

The Grizzly, November 2, 1984

Limerick, Part 3, the Controversy: No Simple Solution in Sight • Students Harassed in Two Incidents • Majority of Students Choose Reagan • Editorial: Some Illuminating Self-abuse en Route to an Endorsement • Letter to the Editor • Election \u2784 • 3000 Alumni Return for Homecoming • News of Yesteryear: Coeds to Hold Dormitory Dawn Patrols • UC Student Attends London\u27s Richmond College • Faculty Symposium Here Tomorrow • proTheatre Presents A Thurber Carnival • Shorts: E.T. Forum; PMA Offers Free Admission; H & PE Offers New Course • Career Planning and Placement Offers Services • Debaters Shine • Bears Upset National Power • Soccer Team to Visit China • Diaphragms Stop Delta Pi in Football • Soccer Wins Two, Record at 14-3 • Swimmers Look Strong • Magic Show Tonight • O\u27Chi\u27s Fiftiethhttps://digitalcommons.ursinus.edu/grizzlynews/1126/thumbnail.jp

MIxS-BE : a MIxS extension defining a minimum information standard for sequence data from the built environment

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in ISME Journal 8 (2014): 1-3, doi:10.1038/ismej.2013.176.The need for metadata standards for microbe sampling in the built environment.We would like to thank the Alfred P Sloan Foundation grant FP047325-01-PR for support for this project

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18

Evaluation of light collection from highly scattering media using wavelength-shifting fibers

Opaque scintillators are designed to have a short scattering length such that scintillation photons are probabilistically confined to a small region of space about their origin. The benefit of this feature is that information on the interaction event topology can be recorded with greater fidelity than in traditional highly transparent media with sensors at large distances from the light production region. Opaque scintillator detectors rely on wavelength-shifting fibers to extract the scintillation light; however, the efficiency of light collection has not yet been directly measured in experiment. We measured the efficiency of light collection as a function of the optical parameters of an opaque liquid and the distance from the origin of the light to the fiber. We use the experimental data to validate a Monte Carlo model of light transport and collection and discuss a simple diffusion model that reproduces the results of Monte Carlo simulation with high fidelity. This combination of validated models has the potential for use in predictions of performance in various designs of future opaque scintillator detectors such as LiquidO

TopiaryExplorer: visualizing large phylogenetic trees with environmental metadata.

MotivationMicrobial community profiling is a highly active area of research, but tools that facilitate visualization of phylogenetic trees and associated environmental data have not kept up with the increasing quantity of data generated in these studies.ResultsTopiaryExplorer supports the visualization of very large phylogenetic trees, including features such as the automated coloring of branches by environmental data, manipulation of trees and incorporation of per-tip metadata (e.g. taxonomic labels).Availabilityhttp://[email protected]

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

Abstract Background We present the Biological Observation Matrix (BIOM, pronounced “biome”) format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the “ome-ome”) grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. Findings The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. Conclusions The BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.</p

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome.

BackgroundWe present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses.FindingsThe BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages.ConclusionsThe BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome.

BackgroundWe present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses.FindingsThe BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages.ConclusionsThe BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium