Viral Genome Deep Classifier

Fabijańska, Anna; Grabowski, Szymon

doi:10.1109/access.2019.2923687

Cited by 28 publications

(80 citation statements)

References 31 publications

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“…Deep learning applications in the bioinformatics, genomics and computational biology mostly concentrate in (i) genome sequencing and analysis [ 15 , 32 , 51 , 72 ] (ii) classification of DNA [ 33 , 56 ], chromatin [ 70 ], polyadenylation [ 26 ], and (iii) protein structure prediction [ 20 , 62 , 68 , 72 ]. Viral genome deep classifier [ 23 ], a CNN model have been proposed for classifying viral sequences into subtypes. Long Short-Term Memory (LSTM) [ 58 ] networks have excelled in the field of NLP in recent years, especially when modelling short sentences with hundreds of words [ 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable deep neural networks for novel viral genome prediction

Dasari

Bhukya

2021

Appl Intell

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Viral infection causes a wide variety of human diseases including cancer and COVID-19. Viruses invade host cells and associate with host molecules, potentially disrupting the normal function of hosts that leads to fatal diseases. Novel viral genome prediction is crucial for understanding the complex viral diseases like AIDS and Ebola. While most existing computational techniques classify viral genomes, the efficiency of the classification depends solely on the structural features extracted. The state-of-the-art DNN models achieved excellent performance by automatic extraction of classification features, but the degree of model explainability is relatively poor. During model training for viral prediction, proposed CNN, CNN-LSTM based methods (EdeepVPP, EdeepVPP-hybrid) automatically extracts features. EdeepVPP also performs model interpretability in order to extract the most important patterns that cause viral genomes through learned filters. It is an interpretable CNN model that extracts vital biologically relevant patterns (features) from feature maps of viral sequences. The EdeepVPP-hybrid predictor outperforms all the existing methods by achieving 0.992 mean AUC-ROC and 0.990 AUC-PR on 19 human metagenomic contig experiment datasets using 10-fold cross-validation. We evaluate the ability of CNN filters to detect patterns across high average activation values. To further asses the robustness of EdeepVPP model, we perform leave-one-experiment-out cross-validation. It can work as a recommendation system to further analyze the raw sequences labeled as ‘unknown’ by alignment-based methods. We show that our interpretable model can extract patterns that are considered to be the most important features for predicting virus sequences through learned filters.

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Section: Literature Reviewmentioning

confidence: 99%

Explainable deep neural networks for novel viral genome prediction

Dasari

Bhukya

2021

Appl Intell

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“…The increasing success of machine learning, and in particular deep learning, techniques is partly due to the introduction of suitable numerical representations for DNA sequences and the ability of the methods to find patterns in these representations (see [22, 24], respectively [20]). Other classification tasks in genomics such as taxonomic classification [25], and the identification of viral sequences among human samples from raw metagenomic segments [26, 27] have also been explored from the deep learning perspective.…”

Section: Introductionmentioning

confidence: 99%

DeLUCS: Deep Learning for Unsupervised Clustering of DNA Sequences

Alipour

Hill

et al. 2021

Preprint

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We present a novel Deep Learning method for the Unsupervised Classification of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Chaos Game Representations (CGRs) of primary DNA sequences, and generates “mimic” sequence CGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster label for each sequence. DeLUCS is able to cluster large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into families; three viral genome and gene datasets, averaging 1,300 sequences each, into virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means and Gaussian Mixture Models) for unlabelled data, by as much as 48%. DeLUCS is highly effective, it is able to classify datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence classification for previously unclassifiable datasets.

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“…Lastly, the tertiary analysis provides the genome interpretation, which can be performed for many algorithms and techniques [8][9][10]. The deep learning techniques have been successful used for the tertiary analysis in many viral classification problems associated with the diagnosis of viral infections, metagenomics, pharmacogenomics, and others [11][12][13][14][15]. Figure 2 shows the steps of the tertiary analysis using DL, that are the mapping and processing stages.…”

mentioning

confidence: 99%

“…The mapping stage receives the DNA sequence information, that can be the reads, contigs, or the whole genome sequence, and maps this data into a feature space. Various mapping strategies have been present in the works from the state of the art, such as one-hot encoding [13,[16][17][18], number representation [11,12], digital signal processing [19], and other strategies, including multiple mapping strategies applied sequentially [20,21]. The processing stage consists of the utilization of a DNN to perform classification, prediction, and other assumptions about the genome information.…”

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confidence: 99%

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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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Viral Genome Deep Classifier

Cited by 28 publications

References 31 publications

Explainable deep neural networks for novel viral genome prediction

Explainable deep neural networks for novel viral genome prediction

DeLUCS: Deep Learning for Unsupervised Clustering of DNA Sequences

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

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