NLP-MeTaxa: A Natural Language Processing Approach for Metagenomic Taxonomic Binning Based on Deep Learning

Matougui, Brahim; Boukelia, Abdelbasset; Belhadef, Hacene; Galiez, Clovis; Batouche, Mohamed

doi:10.2174/1574893616666210621101150

Cited by 6 publications

(4 citation statements)

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“…Given the analogy between NLP and DNA analyses, it is not surprising to see adaptations of word embedding algorithms to DNA sequence data. The word2vec method [ 122 ] has been adapted to generate k-mer and sequence embeddings by both NLP-MeTaxa [ 123 ] and FastDNA [ 124 ]. FastDNA was reused within the Metagenome2Vec method [ 125 ] to combine word embeddings with taxonomy and create a metagenome embedding.…”

Section: Resultsmentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy,

Prifti,

Belda

et al. 2024

Microbial Genomics

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The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome’s key role in our health.

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Section: Resultsmentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy,

Prifti,

Belda

et al. 2024

Microbial Genomics

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show abstract

“…Given the analogy between NLP and DNA analyses, it is not surprising to see adaptations of word embedding algorithms to DNA sequence data. The word2vec method ( [97]) has been adapted to generate k-mer and sequence embeddings by both NLP-MeTaxa ( [98]) and FastDNA. ( [99]).…”

Section: Methods Inspired By Natural Language Processingmentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy

Prifti

Zucker

2023

Preprint

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The ever decreasing cost of sequencing and the multiplication of potential applications for the study of metagenomes have led to an unprecedented increase in the volume of data generated. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome has been shown to play an important role in human health, providing critical information for patient diagnosis and prognosis. However, the analysis of metagenomic data remains challenging for many reasons, including reference catalogs, sparsity and compositionality of the data, to name a few. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. In fact, DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification, and disease prediction. Beyond the generation of predictive models, a key aspect of such methods remains their interpretability. In this article, we provide a systematic review of deep learning approaches in metagenomics, whether based on convolutional networks, autoencoders, or attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the key role the microbiome plays in our health.

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“…NLP-based methods appear to be better suited for metagenomics since nucleotide sequences can be processed as words and are less amenable to representation as images ( 16 ). For example, NLP-based methods that use filters to slide off DNA “sentence” matrices have been applied for genomic binning ( 17 ), viral sequence identification, and phenotypic classification for cancer reads ( 18 ). DL applications for taxonomic classification have been explored to some degree for NLP ( 19 ) and CNN architectures ( 20 ) but have not yet been widely applied ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

Investigation of machine learning algorithms for taxonomic classification of marine metagenomes

Park,

Lim,

Cosme

et al. 2023

Microbiol Spectr

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Microbial communities play key roles in ocean ecosystems through regulation of biogeochemical processes such as carbon and nutrient cycling, food web dynamics, and gut microbiomes of invertebrates, fish, reptiles, and mammals. Assessments of marine microbial diversity are therefore critical to understanding spatiotemporal variations in microbial community structure and function in ocean ecosystems. With recent advances in DNA shotgun sequencing for metagenome samples and computational analysis, it is now possible to access the taxonomic and genomic content of ocean microbial communities to study their structural patterns, diversity, and functional potential. However, existing taxonomic classification tools depend upon manually curated phylogenetic trees, which can create inaccuracies in metagenomes from less well-characterized communities, such as from ocean water. Herein, we explore the utility of deep learning tools—DeepMicrobes and a novel Residual Network architecture—that leverage natural language processing and convolutional neural network architectures to map input sequence data (k-mers) to output labels (taxonomic groups) without reliance on a curated taxonomic tree. We trained both models using metagenomic reads simulated from marine microbial genomes in the MarRef database. The performance of both models (accuracy, precision, and percent microbe predicted) was compared with the standard taxonomic classification tool Kraken2 using 10 complex metagenomic data sets simulated from MarRef. Our results demonstrate that time, compute power, and microbial genomic diversity still pose challenges for machine learning (ML). Moreover, our results suggest that high genome coverage and rectification of class imbalance are prerequisites for a well-trained model, and therefore should be a major consideration in future ML work. IMPORTANCE Taxonomic profiling of microbial communities is essential to model microbial interactions and inform habitat conservation. This work develops approaches in constructing training/testing data sets from publicly available marine metagenomes and evaluates the performance of machine learning (ML) approaches in read-based taxonomic classification of marine metagenomes. Predictions from two models are used to test accuracy in metagenomic classification and to guide improvements in ML approaches. Our study provides insights on the methods, results, and challenges of deep learning on marine microbial metagenomic data sets. Future machine learning approaches can be improved by rectifying genome coverage and class imbalance in the training data sets, developing alternative models, and increasing the accessibility of computational resources for model training and refinement.

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NLP-MeTaxa: A Natural Language Processing Approach for Metagenomic Taxonomic Binning Based on Deep Learning

Cited by 6 publications

References 0 publications

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

Investigation of machine learning algorithms for taxonomic classification of marine metagenomes

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