A Deep Embedded Clustering Algorithm for the Binning of Metagenomic Sequences

Bao, Huynh Quang; Vinh, Le Van; Hoài, Trần Văn

doi:10.1109/access.2022.3176954

Cited by 8 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To extract features, methods like CNN can be used for taxonomic classification[ 106 109 110 ]. Autoencoders are used by MetaDEC [ 111 ], which groups reads together by creating a graph where the nodes are reads, linked if they exhibit significant overlap in their substrings. Subsequently, clusters are extracted from this graph.…”

Section: Resultsmentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy,

Prifti,

Belda

et al. 2024

Microbial Genomics

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy,

Prifti,

Belda

et al. 2024

Microbial Genomics

View full text Add to dashboard Cite

show abstract

“…VAMB showed improvements in the reconstruction of near-complete strains over multiple datasets, with a performance increase of 29% to 98% on simulated datasets and 45% on real data compared to other state-of-the-art metagenomic binning methods, such as MetaBAT2, MaxBin2, and Canopy. Other recent approaches further explore deep learning algorithms to learn contig embeddings [15], [16].…”

Section: Prior Workmentioning

confidence: 99%

BinChill: A Metagenomic Binning Ensemble Method

Bak,

Jensen,

Trudslev

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The goal of metagenomic binning is to reconstruct genomes from a mixture of DNA sequences into genomic bins, which can be considered a clustering task. Multiple methods have been proposed for this task, such as distance-based metrics, machine learning, and ensemble approaches. We propose BinChill, a metagenomic ensemble method, based on the generic co-occurrence ensembler method, ACE. BinChill incorporates domain information in the form of Single-Copy Genes (SCG) with a co-occurrence strategy. This strategy combines multiple clustering partitions according to how often two items co-occur in the same cluster. BinChill was able to reconstruct more or equally as many high-and medium quality while having an equal or faster runtime than other metagenomics-specific methods on a smaller simulated dataset. On larger datasets, both simulated and real-world, BinChill outperformed other methods in reconstructing high-quality bins, at the cost of an increased processing time when compared to generic ensemble clustering algorithms. This is due to the domain-specific steps that our method implements. Our results show that the strengths of multiple partitions can be combined to generate a partition of higher quality.INDEX TERMS Genomics, clustering algorithms, bioinformatics, partitioning algorithms.

show abstract

“…MeShClust v3.0 [18] uses the mean shift algorithm and alignment-free identity scores for the sake of not paying precious time due to the costly global alignment algorithm. MetaDEC [19] applies a deep unsupervised learning approach to cluster metagenomic DNA strands. MM-seqs2 [20] uses a graph-based approach, in which every noisy copy is a vertex, and two noisy copies are connected if they satisfy a particular similarity criteria.…”

Section: Related Workmentioning

confidence: 99%

GradHC: Highly Reliable Gradual Hash-based Clustering for DNA Storage Systems

Shabat,

Hadad,

Boruchovsky

et al. 2023

Preprint

View full text Add to dashboard Cite

As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results.

show abstract

A Deep Embedded Clustering Algorithm for the Binning of Metagenomic Sequences

Cited by 8 publications

References 24 publications

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

BinChill: A Metagenomic Binning Ensemble Method

GradHC: Highly Reliable Gradual Hash-based Clustering for DNA Storage Systems

Contact Info

Product

Resources

About