Marginalised stack denoising autoencoders for metagenomic data binning

Kouchaki, Samaneh; Tirunagari, Santosh; Tapinos, Avraam; Robertson, David L.

doi:10.1109/cibcb.2017.8058552

Cited by 3 publications

References 18 publications

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A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns

Kouchaki

Tapinos

Robertson

2019

Sci Rep

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Algorithms in bioinformatics use textual representations of genetic information, sequences of the characters A, T, G and C represented computationally as strings or sub-strings. Signal and related image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. Here we introduce a method, multi-resolution local binary patterns (MLBP) adapted from image processing to extract local ‘texture’ changes from nucleotide sequence data. We apply this feature space to the alignment-free binning of metagenomic data. The effectiveness of MLBP is demonstrated using both simulated and real human gut microbial communities. Sequence reads or contigs can be represented as vectors and their ‘texture’ compared efficiently using machine learning algorithms to perform dimensionality reduction to capture eigengenome information and perform clustering (here using randomized singular value decomposition and BH-tSNE). The intuition behind our method is the MLBP feature vectors permit sequence comparisons without the need for explicit pairwise matching. We demonstrate this approach outperforms existing methods based on k-mer frequencies. The signal processing method, MLBP, thus offers a viable alternative feature space to textual representations of sequence data. The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP.

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A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns

Kouchaki

Tapinos

Robertson

2019

Sci Rep

Self Cite

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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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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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Marginalised stack denoising autoencoders for metagenomic data binning

Cited by 3 publications

References 18 publications

A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns

A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

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