Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.

ABSTRACT: Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbia

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC8496832 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature