biostudies-literatureUnknown2Borgman JWe present a novel approach for rapidly identifying sequences that leverages the representational power of Deep Learning techniques and is applied to the analysis of microbiome data. The method involves the creation of a latent sequence space, training a convolutional neural network to rapidly identify sequences by mapping them into that space, and we leverage the novel encoded latent space for denoising to correct sequencing errors. Using mock bacterial communities of known composition, we show that this approach achieves single nucleotide resolution, generating results for sequence identification and abundance estimation that match the best available microbiome algorithms in terms of accuracy while vastly increasing the speed of accurate processing. We further show the ability of this apFrontiers in bioinformatics871256https://www.ebi.ac.uk/biostudies/studies/S-EPMC9580936biostudies-literatureDeep Learning Encoding for Rapid Sequence Identification on Microbiome Data.PMC9580936Carson JStark KBorgman JHauser LfalseDeep Learning Encoding for Rapid Sequence Identification on Microbiome Data.We present a novel approach for rapidly identifying sequences that leverages the representational power of Deep Learning techniques and is applied to the analysis of microbiome data. The method involves the creation of a latent sequence space, training a convolutional neural network to rapidly identify sequences by mapping them into that space, and we leverage the novel encoded latent space for denoising to correct sequencing errors. Using mock bacterial communities of known composition, we show that this approach achieves single nucleotide resolution, generating results for sequence identification and abundance estimation that match the best available microbiome algorithms in terms of accuracy while vastly increasing the speed of accurate processing. We further show the ability of this ap2022-01-01T00:00:00Z20222026-04-12T19:27:50.52Z2025-04-19T12:04:05.101ZS-EPMC95809363630431610.3389/fbinf.2022.871256