Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus

Galan, Wojciech; Bąk, Maciej; Jakubowska, Małgorzata

doi:10.1038/s41598-019-39847-2

Cited by 33 publications

(24 citation statements)

References 58 publications

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“…Two recent studies employ k-mer based, k-NN classifiers (16) and deep learning (17) to predict host range for a small set of three wellstudied species directly from viral sequences. While those approaches are limited to those particular species and do not scale to viral host-range prediction in general, the Host Taxon Predictor (HTP) (18) uses logistic regression and support vector machines to predict if a novel virus infects bacteria, plants, vertebrates or arthropods. Yet, the authors argue that it is not possible to use HTP in a read-based manner; it requires long sequences of at least 3,000 nucleotides.…”

Section: Current Tools For Host Range Predictionmentioning

confidence: 99%

Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz

Seidel

Renard

2020

Preprint

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AbstractViruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect virulence-related genes in novel agents, for example the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020.

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Section: Current Tools For Host Range Predictionmentioning

confidence: 99%

Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz

Seidel

Renard

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Two recent studies employ k -mer based, k -NN classifiers ( 16 ), and deep learning ( 17 ) to predict host range for a small set of three well-studied species directly from viral sequences. While those approaches are limited to those particular species and do not scale to viral host-range prediction in general, the Host Taxon Predictor (HTP) ( 18 ) uses logistic regression and supports vector machines to predict if a novel virus infects bacteria, plants, vertebrates or arthropods. Yet, the authors argue that it is not possible to use HTP in a read-based manner; it requires long sequences of at least 3000 nucleotides.…”

Section: Introductionmentioning

confidence: 99%

Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz

Seidel

Renard

2021

NAR Genomics and Bioinformatics

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Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

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“…However, DeePhage cannot identify these sequences before distinguishing the lifestyle of each contig. Fortunately, the related tool that helps to distinguish prokaryotic and eukaryotic viruses has been developed recently [48] and we are also considering constructing a preprocessing module for DeePhage to filter out the eukaryotic viruses so that DeePhage can generate more reliable results for the downstream analysis.…”

Section: Discussionmentioning

confidence: 99%

Distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach

Fang

Tan

et al. 2020

Preprint

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BackgroundProkaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for their role in interactions with bacterial hosts and regulations of microbial communities. However there is no experimental or computational approach to classify sequences of these two in culture-independent metavirome effectively, we present a new computational method DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment.FindingsDeePhage utilizes a “one-hot” encoding form to have an overall and detailed representation of DNA sequences. Sequence signatures are detected via a deep learning algorithm, namely a convolutional neural network to extract valuable local features. DeePhage makes better performance than the most related method PHACTS. The accuracy of DeePhage on five-fold validation reach as high as 88%, nearly 30% higher than PHACTS. Evaluation on real metavirome shows DeePhage annotated 54.4% of reliable contigs while PHACTS annotated 44.5%. While running on the same machine, DeePhage reduces computational time than PHACTS by 810 times. Besides, we proposed a new strategy to explore phage transformations in the microbial community by direct detection of the temperate viral fragments from metagenome and metavirome. The detectable transformation of temperate phages provided us a new insight into the potential treatment for human disease.ConclusionsDeePhage is the first tool that can rapidly and efficiently identify two kinds of phage fragments especially for metagenomics analysis with satisfactory performance. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.

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Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus

Cited by 33 publications

References 58 publications

Interpretable detection of novel human viruses from genome sequencing data

Interpretable detection of novel human viruses from genome sequencing data

Interpretable detection of novel human viruses from genome sequencing data

Distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach

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