Fabijanić, Maja, Vlahoviček, Kristian, Maja Fabijanić, Kristian Vlahoviček
Editors
Oliviero Carugo, Frank Eisenhaber
Abstract
Metagenomics projects use next-generation sequencing to unravel genetic potential in microbial communities from a wealth of environmental niches, including those associated with human body and relevant to human health. In order to understand large datasets collected in metagenomics surveys and interpret them in context of how a community metabolism as a whole adapts and interacts with the environment, it is necessary to extend beyond the conventional approaches of decomposing metagenomes into microbial species' constituents and performing analysis on separate components. By applying concepts of translational optimization through codon usage adaptation on entire metagenomic datasets, we demonstrate that a bias in codon usage present throughout the entire microbial community can be used as a powerful analytical tool to predict for community lifestyle-specific metabolism. Here we demonstrate this approach combined with machine learning, to classify human gut microbiome samples according to the pathological condition diagnosed in the human host.
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 December 2017.
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So far Altmetric has tracked 13,981 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 97% of its peers.
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