Dataset Information

Resolving microbial membership using Abundance and Variability In Taxonomy ('AVIT?).

ABSTRACT: Development of NGS has revolutionized the analysis in microbial ecology contributing to our deeper understanding of microbiota in health and disease. However, the quality, quantity and confidence of summarized taxonomic abundances are in need of further scrutiny due to sample dependent and independent effects. In this article we introduce 'AVIT (Abundance and Variability In Taxonomy), an unbiased method to enrich for assigned members of microbial communities. As opposed to using a priori thresholds, 'AVIT uses inherent abundance and variability of taxa in a dataset to determine the inclusion or rejection of each taxa for further downstream analysis. Using in-vitro and in-vivo studies, we benchmarked performance and parameterized 'AVIT to establish a framework for investigating the dynamic range of microbial community membership in clinically relevant scenarios.

SUBMITTER: Chakrabarti A

PROVIDER: S-EPMC4987704 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Resolving microbial membership using Abundance and Variability In Taxonomy ('AVIT ).

Chakrabarti Anirikh A Siddharth Jay J Lauber Christian L CL Membrez Mathieu M Betrisey Bertrand B Loyer Carole C Chou Chieh Jason CJ Pataky Zoltan Z Golay Alain A Parkinson Scott J SJ

Scientific reports 20160817

Development of NGS has revolutionized the analysis in microbial ecology contributing to our deeper understanding of microbiota in health and disease. However, the quality, quantity and confidence of summarized taxonomic abundances are in need of further scrutiny due to sample dependent and independent effects. In this article we introduce 'AVIT (Abundance and Variability In Taxonomy), an unbiased method to enrich for assigned members of microbial communities. As opposed to using a priori thresho ...[more]

PMID: 27530237

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Project description:Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.

Dataset Information

Resolving microbial membership using Abundance and Variability In Taxonomy ('AVIT?).

Publications

Resolving microbial membership using Abundance and Variability In Taxonomy ('AVIT ).

Similar Datasets

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