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Gregory B. Gloor

Researcher at University of Western Ontario

Publications -  152
Citations -  14109

Gregory B. Gloor is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Gene & Microbiome. The author has an hindex of 47, co-authored 144 publications receiving 11064 citations. Previous affiliations of Gregory B. Gloor include Lawson Health Research Institute & University of Wisconsin-Madison.

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Microbiome Datasets Are Compositional: And This Is Not Optional.

TL;DR: The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis.
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A new genomic blueprint of the human gut microbiota

TL;DR: The known species repertoire of the collective human gut microbiota is substantially expanded with the discovery of 1,952 uncultured bacterial species that greatly improve classification of understudied African and South American samples.
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Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

TL;DR: Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.
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Stool substitute transplant therapy for the eradication of Clostridium difficile infection: 'RePOOPulating' the gut.

TL;DR: This proof-of-principle study demonstrates that a stool substitute mixture comprising a multi-species community of bacteria is capable of curing antibiotic-resistant C. difficile colitis.
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Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction

TL;DR: A rapid, simple and general method based on information theory that accurately estimates the level of background mutual information for each pair of positions in a given protein family, and correctly identifies substantially more coevolving positions in protein families than any existing method.