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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network
Journal article   Open access

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

Zhengqiao Zhao, Stephen Woloszynek, Felix Agbavor, Joshua Chang Mell, Bahrad A Sokhansanj and Gail L Rosen
PLoS computational biology, v 17(9), pp e1009345-e1009345
Sep 2021
DOI: https://doi.org/10.1371/journal.pcbi.1009345
PMID: 34550967
url
https://doi.org/10.1371/journal.pcbi.1009345View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Algorithms Computational Biology Databases, Genetic Deep Learning Gastrointestinal Microbiome - genetics Host Microbial Interactions - genetics Humans Inflammatory Bowel Diseases - microbiology Microbiota - genetics Natural Language Processing Neural Networks, Computer Phenotype Prevotella - classification Prevotella - genetics Prevotella - isolation & purification Proof of Concept Study RNA, Ribosomal, 16S - classification RNA, Ribosomal, 16S - genetics
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 microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

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