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. Within the meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of the human microbiome. In this context, we developed G2S, a bioinformatic tool for taxonomic prediction of the human fecal microbiome directly from the oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on paired oral and fecal samples from populations across the globe, which allows inferring 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 performed, 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.
Highlights
Deep learning is increasingly being used to make inference on large and complex data
Our approach may be relevant for predicting the gut microbiome configuration when fecal data are not available, and suitable for human archeological records, where coprolites and fecal sediments are rare compared to dental calculi and other human remains
G2S is designed to predict the structure of the human stool microbiome from oral microbiome data
Summary
Deep learning is increasingly being used to make inference on large and complex data. G2S: Deep Learning for Microbiomes artificial intelligence, with applications such as facial recognition, speech recognition and self-driving vehicles. The same deep learning approaches are beginning to be applied to genetics, agriculture and medicine (Alipanahi et al, 2015; Leung et al, 2016; Ching et al, 2018; Demirci et al, 2018; Wainberg et al, 2018; Webb, 2018; Le, 2019; Le and Huynh, 2019; Le et al, 2019; Quang and Xie, 2019). Deep learning is still unexplored in the field of microbial metagenomics, with only a few approaches suitable for microbiome data (Geman et al, 2016; Reiman et al, 2017; Galkin et al, 2020), and a huge untapped potential yet unexplored
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