Using Deep Learning for the Taxonomic Classification of Microbial Sequences

Abstract

Microbes are common creatures and play a crucial role in our world. Thus, the understanding of microbial communities brings benefits to human lives. Because the material samples of microbes contain sequences belonging to different organisms, an important task in analyzing processes is to classify the sequences into groups of different species or closely related organisms, called metagenomic classification. Many classification approaches were proposed to analyze the metagenomic data. However, due to the complexity of microbial samples, the accuracy performance of those methods still remains a challenge. This study applies an effective deep learning framework for the classification of microbial sequences. The proposed architecture combines a sequence embedding layer with other layers of a bidirectional Long Short-Term Memory, Seft-attention, and Dropout mechanisms for feature learning. Experimental results demonstrate the strength of the proposed method on datasets of real metagenomes.