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

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 convolutio...

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Autores principales:	Zhengqiao Zhao, Stephen Woloszynek, Felix Agbavor, Joshua Chang Mell, Bahrad A Sokhansanj, Gail L Rosen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f942b69fd3b3444fabf60265b9f20db6
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spelling	oai:doaj.org-article:f942b69fd3b3444fabf60265b9f20db62021-12-02T19:57:45ZLearning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.1553-734X1553-735810.1371/journal.pcbi.1009345https://doaj.org/article/f942b69fd3b3444fabf60265b9f20db62021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009345https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Recurrent 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).Zhengqiao ZhaoStephen WoloszynekFelix AgbavorJoshua Chang MellBahrad A SokhansanjGail L RosenPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009345 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Zhengqiao Zhao Stephen Woloszynek Felix Agbavor Joshua Chang Mell Bahrad A Sokhansanj Gail L Rosen Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
description	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).
format	article
author	Zhengqiao Zhao Stephen Woloszynek Felix Agbavor Joshua Chang Mell Bahrad A Sokhansanj Gail L Rosen
author_facet	Zhengqiao Zhao Stephen Woloszynek Felix Agbavor Joshua Chang Mell Bahrad A Sokhansanj Gail L Rosen
author_sort	Zhengqiao Zhao
title	Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
title_short	Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
title_full	Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
title_fullStr	Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
title_full_unstemmed	Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.
title_sort	learning, visualizing and exploring 16s rrna structure using an attention-based deep neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f942b69fd3b3444fabf60265b9f20db6
work_keys_str_mv	AT zhengqiaozhao learningvisualizingandexploring16srrnastructureusinganattentionbaseddeepneuralnetwork AT stephenwoloszynek learningvisualizingandexploring16srrnastructureusinganattentionbaseddeepneuralnetwork AT felixagbavor learningvisualizingandexploring16srrnastructureusinganattentionbaseddeepneuralnetwork AT joshuachangmell learningvisualizingandexploring16srrnastructureusinganattentionbaseddeepneuralnetwork AT bahradasokhansanj learningvisualizingandexploring16srrnastructureusinganattentionbaseddeepneuralnetwork AT gaillrosen learningvisualizingandexploring16srrnastructureusinganattentionbaseddeepneuralnetwork
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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.

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