New Metabolic Modeling Tool Allows Microbiome Researchers to Map Community Ecology to Ecosystem Function
at the Institute for Systems Biology
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New Modeling Tool Allows Microbiome Researchers to Map Community Ecology to Ecosystem Function
A promising new open-source metabolic modeling tool provides microbiome researchers a path forward in predicting ecosystem function from community structure. News of the software package, called MICOM, was developed in part by researchers in ISB’s Gibbons Lab, and its uses were published in the journal mSystems.
Using Blood to Predict Gut Microbiome Diversity
Predicting the alpha diversity of an individual’s gut microbiome is possible by examining metabolites in the blood. The robust relationship between host metabolome and gut microbiome diversity opens the door for a fast, cheap and reliable blood test to identify individuals with low gut diversity.
All About the Human Microbiome
The human microbiome is a relatively new area of research, and there are numerous questions surrounding it. What is the human microbiome? Can we change it? Does it make us sick? Keep us well? ISB Assistant Professor and microbiome researcher Dr. Sean Gibbons answers these questions — and many more.
ISB's Dr. Sean Gibbons on the importance of the human microbiome
“This new organ that we’re coming to recognize as the microbiome is part and parcel to the functionality of the whole system, and if it breaks down, if it starts to fall apart, we start to get sick,” said Dr. Sean Gibbons, ISB’s newest faculty member, in a WGBH Forum Network presentation.
Dr. Sean Gibbons joins ISB faculty as WRF Distinguished Investigator
Dr. Sean Gibbons has joined ISB as our newest faculty member. Gibbons’ new position brings a number of changes, including relocating to the Pacific Northwest from the Northeast. Read on for a Q&A with Gibbons that sheds light on his research career to date, areas of study and even a hidden talent.
Correcting Batch Effects in Microbiome Data
Batch Effects in 16S Datasets Complicate Cross-Study Comparisons High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in…