Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5002919 | IFAC-PapersOnLine | 2016 | 6 Pages |
Abstract
In this paper, we propose a new method for inferring the metabolic potential of microbial ecosystems based on gene frequencies generated from shotgun metagenomic data. Our approach is based on Non-Negative Matrix Factorization with constraints accounting for prior biological knowledge of bacterial metabolism. The problem is solved using efficient accelerated projected gradient methods. The approach is illustrated on a toy model and on real data on fiber metabolism by the gut microbiota in humans. We show how this approach leads to the inference of biologically relevant gene clusters.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Sébastien Raguideau, Béatrice Laroche, Marion Leclerc, Sandra Plancade,