Embedding the de Bruijn graph, and applications to metagenomics

Menegaux, Romain; Vert, Jean-Philippe

doi:10.1101/2020.03.06.980979

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Reads falling in the same bucket share the same embedding. On the other hand, fastDNA has been enhanced with BRUME [ 127 ]. The idea here is that k-mers that are always present or absent together in the same reads should be considered as having the same importance in sequence 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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“…Prior work for using machine learning in taxonomic classification either relied on testing with simulated reads [14,15,30] or did not show advantages compared to conventional k-mer-matching and mapping-based approaches and real data [11,13]. MetageNN is, to the best of our knowledge, the first machine learning-based method that shows improvements relative to conventional tools with real long-read data, when assessing performance for genomes that are not in the database.…”

Section: Discussionmentioning

confidence: 99%

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes

Peres da Silva,

Suphavilai,

Nagarajan

2024

BMC Bioinformatics

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Background With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database. Results We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is > 7× faster than MetaMaps and GeNet and > 2× faster than MEGAN-LR and MMseqs2. Conclusion This proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.

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“…Prior work for using machine learning in taxonomic classification either relied on testing with simulated reads [15,31,14] or did not show advantages compared to conventional k-mer-matching and mapping-based approaches and real data [11,13]. MetageNN is, to the best of our knowledge, the first machine learning-based method that shows improvements relative to conventional tools with real long-read data, when assessing performance for genomes that are not in the database.…”

Section: Discussionmentioning

confidence: 99%

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes

da Silva,

Suphavilai,

Nagarajan

2023

Preprint

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BackgroundWith the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database.ResultsWe present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4thof the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is >7x faster than MetaMaps and GeNet and >2x faster than MEGAN-LR and MMseqs2.ConclusionThis proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.

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Embedding the de Bruijn graph, and applications to metagenomics

Cited by 6 publications

References 15 publications

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes

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