CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning

Shang, Jiayu; Sun, Yanni

doi:10.1101/2020.03.26.009001

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…There are two major methods for the embedding layer: one-hot embedding and skip-gram embedding ( Mikolov et al, 2013 ). As shown in our previous work of using CNN for classifying RNA viruses ( Shang and Sun, 2020 ), the skip-gram-based embedding can improve CNN’s learning ability. Thus, in this work, we implemented a skip-gram embedding layer that can map proximate k-mers into highly similar vectors.…”

Section: Methodsmentioning

confidence: 77%

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Bacteriophage classification for assembled contigs using graph convolutional network

2021

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Motivation Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data. Results In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools. Availability and implementation The source code of PhaGCN is available via: https://github.com/KennthShang/PhaGCN.

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Section: Methodsmentioning

confidence: 77%

“…There are a number of learning-based tools for microbe classification such as the Naïve Bayes classifier ( Wang et al, 2007 ) and CNN ( Shang and Sun, 2020 ). They use either manually derived or automatically learned features to predict taxonomic labels for bacteria or RNA viruses.…”

Section: Introductionmentioning

confidence: 99%

Bacteriophage classification for assembled contigs using graph convolutional network

2021

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“…Homology based methods require higher computational resources and might produce unreliable alignment for novel viral species (Bazinet and Cummings, 2012). Supervised machine learning methods have been widely used to classify metagenomic reads against known viral and bacterial genomes (Ounit et al , 2015; Ounit and Lonardi, 2016; Shang and Sun, 2020). Machine learning techniques have also been used to assign taxonomic labels of viruses from genome sequence in CASTOR and ML-DSP (Remita et al , 2017; Randhawa et al , 2020, 2019).…”

Section: Introductionmentioning

confidence: 99%

VirusTaxo: Taxonomic classification of virus genome using multi-class hierarchical classification by k-mer enrichment

Shuvo

et al. 2021

Preprint

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Taxonomic classification of viruses is a multi-class hierarchical classification problem, as taxonomic ranks (e.g., order, family and genus) of viruses are hierarchically structured and have multiple classes in each rank. Classification of biological sequences which are hierarchically structured with multiple classes is challenging. Here we developed a machine learning architecture, VirusTaxo, using a multi-class hierarchical classification by k-mer enrichment. VirusTaxo classifies DNA and RNA viruses to their taxonomic ranks using genome sequence. To assign taxonomic ranks, VirusTaxo extracts k-mers from genome sequence and creates bag-of-k-mers for each class in a rank. VirusTaxo uses a top-down hierarchical classification approach and accurately assigns the order, family and genus of a virus from the genome sequence. The average accuracies of VirusTaxo for DNA viruses are 99% (order), 98% (family) and 95% (genus) and for RNA viruses 97% (order), 96% (family) and 82% (genus). VirusTaxo can be used to detect taxonomy of novel viruses using full length genome or contig sequences.AvailabilityOnline version of VirusTaxo is available at https://omics-lab.com/virustaxo/.

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Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

Coutinho

Câmara

Barbosa

et al. 2021

Preprint

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Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

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CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning

Cited by 4 publications

References 28 publications

Bacteriophage classification for assembled contigs using graph convolutional network

Bacteriophage classification for assembled contigs using graph convolutional network

VirusTaxo: Taxonomic classification of virus genome using multi-class hierarchical classification by k-mer enrichment

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

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