AUTHOR=Rampelli Simone , Fabbrini Marco , Candela Marco , Biagi Elena , Brigidi Patrizia , Turroni Silvia 

TITLE=G2S: A New Deep Learning Tool for Predicting Stool Microbiome Structure From Oral Microbiome Data

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.644516

DOI=10.3389/fgene.2021.644516

ISSN=1664-8021

ABSTRACT=Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. In the frame of meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of human microbiome. In this context, we developed G2S, a bioinformatic tool for the taxonomic prediction of the human stool microbiome directly from oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on data of the Human Microbiome Project, allowing to infer the stool microbiome at the family level more accurately than other available approaches. The tool can be used in retrospective studies, where fecal sampling was not carried out, and especially in the field of paleomicrobiology, as a unique opportunity to recover data related to ancient gut microbiome configurations. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects.