GutMicroNet: an interactive platform for gut microbiome interaction exploration

The human gut microbiome data has been proven to be a powerful tool to understand the human body in both health and disease conditions. However, understanding their complex interactions and impact on the human body remains a challenging task. Unravelling the species-level interactions could allow us to study the causality of the microbiome. Moreover, it could lead us to better under-stand the underlying mechanisms of complex diseases and, subsequently, the discovery of new therapeutic targets. Given these challenges and benefits, it has become evident that a freely accessible and centralized platform for presenting gut microbiome interaction is essential to untangle the complexity and open multiple new paths and opportunities in disease- and drug-related research. Here, we present GutMicroNet, an interactive visualization platform of human gut microbiome inter-action networks. We generated 45 gut microbiome co-abundance networks from various geographical origins, gender, and diseases based on the data presented in the Human Gut Microbiome Atlas. This interactive platform includes more than 1900 gut microbiome species and allows users to query multiple species at the same time based on their interests and adjust it based on the statistical properties. Moreover, users can download publication-ready figures or network information for further analysis. The platform can be accessed freely on https://gutmicro.net without any login requirements or limitations, including access to the full networks data.

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Correction: Methods for Improving Human Gut Microbiome Data by Reducing Variability through Sample Processing and Storage of Stool

PLoS ONE ◽

10.1371/journal.pone.0139529 ◽

2015 ◽

Vol 10 (9) ◽

pp. e0139529 ◽

Cited By ~ 1

Author(s):

Keyword(s):

Gut Microbiome ◽

Sample Processing ◽

Human Gut ◽

Human Gut Microbiome ◽

Processing And Storage ◽

Microbiome Data ◽

And Storage

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Methods for Improving Human Gut Microbiome Data by Reducing Variability through Sample Processing and Storage of Stool

PLoS ONE ◽

10.1371/journal.pone.0134802 ◽

2015 ◽

Vol 10 (8) ◽

pp. e0134802 ◽

Cited By ~ 121

Author(s):

Monika A. Gorzelak ◽

Sandeep K. Gill ◽

Nishat Tasnim ◽

Zahra Ahmadi-Vand ◽

Michael Jay ◽

...

Keyword(s):

Gut Microbiome ◽

Sample Processing ◽

Human Gut ◽

Human Gut Microbiome ◽

Processing And Storage ◽

Microbiome Data ◽

And Storage

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Decoding the Language of Microbiomes: Leveraging Patterns in 16S Public Data using Word-Embedding Techniques and Applications in Inflammatory Bowel Disease

10.1101/748152 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christine A. Tataru ◽

Maude M. David

Keyword(s):

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Gut Microbiome ◽

Metabolic Pathways ◽

Community Level ◽

Human Gut ◽

Human Gut Microbiome ◽

Inflammatory Bowel ◽

Microbiome Data ◽

Positive Results

AbstractMicrobiomes are complex ecological systems that play crucial roles in understanding natural phenomena from human disease to climate change. Especially in human gut microbiome studies, where collecting clinical samples can be arduous, the number of taxa considered in any one study often exceeds the number of samples ten to one hundred-fold. This discrepancy decreases the power of studies to identify meaningful differences between samples, increases the likelihood of false positive results, and subsequently limits reproducibility. Despite the vast collections of microbiome data already available, biome-specific patterns of microbial structure are not currently leveraged to inform studies. Instead, most microbiome survey studies focus on differential abundance testing per taxa in pursuit of specific biomarkers for a given phenotype. This methodology assumes differences in individual species, genera, or families can be used to distinguish between microbial communities and ignores community-level response. In this paper, we propose to leverage public microbiome databases to shift the analysis paradigm from a focus on taxonomic counts to a focus on comprehensive properties that more completely characterize microbial community members’ function and environmental relationships. We learn these properties by applying an embedding algorithm to quantify taxa co-occurrence patterns in over 18,000 samples from the American Gut Project (AGP) microbiome crowdsourcing effort. The resulting set of embeddings transforms human gut microbiome data from thousands of taxa counts to a latent variable landscape of only one hundred “properties”, or contextual relationships. We then compare the predictive power of models trained using properties, normalized taxonomic count data, and another commonly used dimensionality reduction method, Principal Component Analysis in categorizing samples from individuals with inflammatory bowel disease (IBD) and healthy controls. We show that predictive models trained using property data are the most accurate, robust, and generalizable, and that property-based models can be trained on one dataset and deployed on another with positive results. Furthermore, we find that these properties can be interpreted in the context of current knowledge; properties correlate significantly with known metabolic pathways, and distances between taxa in “property space” roughly correlate with their phylogenetic distances. Using these properties, we are able to extract known and new bacterial metabolic pathways associated with inflammatory bowel disease across two completely independent studies.More broadly, this paper explores a reframing of the microbiome analysis mindset, from taxonomic counts to comprehensive community-level properties. By providing a set of pre-trained embeddings, we allow any V4 16S amplicon study to leverage and apply the publicly informed properties presented to increase the statistical power, reproducibility, and generalizability of analysis.

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