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Rainer Roehe

Researcher at Scotland's Rural College

Publications -  177
Citations -  5954

Rainer Roehe is an academic researcher from Scotland's Rural College. The author has contributed to research in topics: Beef cattle & Metagenomics. The author has an hindex of 37, co-authored 160 publications receiving 4851 citations. Previous affiliations of Rainer Roehe include Scottish Agricultural College & The Roslin Institute.

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Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review.

TL;DR: NIR has a considerable potential to predict simultaneously numerous meat quality criteria and to categorize meat into quality classes, considering the literature published mainly in the last decade.
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Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

TL;DR: This dataset substantially improves the coverage of rumen microbial genomes in the public databases and represents a valuable resource for biomass-degrading enzyme discovery and studies of the rumen microbiome.
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Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery

TL;DR: A genome-resolved metagenomics workflow that enabled assembly of bacterial and archaeal genomes that were at least 80% complete and predicted and annotated a large set of rumen proteins will enable a better understanding of the structure and functions of the rumen microbiota.
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The rumen microbial metagenome associated with high methane production in cattle

TL;DR: The abundance of archaeal genes in ruminal digesta correlated strongly with differing methane emissions from individual animals, a finding useful for genetic screening purposes.
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Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

TL;DR: The results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts.