Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz, Jakub M; Seidel, Anja; Renard, Bernhard Y.

doi:10.1101/2020.01.29.925354

Cited by 27 publications

(67 citation statements)

References 81 publications

(116 reference statements)

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“…While pathogenicity prediction methods using whole genomes or protein sets as input also exist, this work focuses on read-based classification to offer real-time predictions and avoid delays necessitated by assembly pipelines. However, read-based methods have been shown to perform well and also for full genomes and assembled contigs, achieving similar or better performance than alignment-based approaches (Deneke et al, 2017;Bartoszewicz et al, 2020Bartoszewicz et al, , 2021.…”

Section: Read-based Detection Of Novel Pathogensmentioning

confidence: 99%

Deep learning-based real-time detection of novel pathogens during sequencing

Bartoszewicz

Genske

Renard

2021

Preprint

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MotivationNovel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state-of-the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens, but require relatively long input sequences and processed data from a finished sequencing run.ResultsWe present DeePaC-Live, a Python package for real-time pathogenic potential prediction directly from incomplete sequencing reads. We train deep neural networks to classify Illumina and Nanopore reads and integrate our models with HiLive2, a real-time Illumina mapper. DeePaC-Live outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we increase the true positive rate 80-fold compared to the live-mapping approach. The first 250bp of Nanopore reads, corresponding to 0.5s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. Our approach could also be used for screening synthetic sequences against biosecurity threats.AvailabilityThe code is available at: https://gitlab.com/dacs-hpi/deepac-live and https://gitlab.com/dacs-hpi/deepac. The package can be installed with Bioconda, Docker or pip.ContactJakub.Bartoszewicz@hpi.de, Bernhard.Renard@hpi.deSupplementary informationSupplementary data are available online.

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Section: Read-based Detection Of Novel Pathogensmentioning

confidence: 99%

Deep learning-based real-time detection of novel pathogens during sequencing

Bartoszewicz

Genske

Renard

2021

Preprint

Self Cite

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“…For the development of effective drugs, the systems demonstrated in [ 39 ] trained GAs and GANs, [ 41 ] used reinforcement learning techniques, and [ 43 ] applied LSTM networks. The human-infecting virus can be identified using deep learning-based architectures utilizing its next-generation sequence shown in [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…It is difficult to detect human-infecting viruses using bioinformatics systems. Bartoszewicz et al [ 45 ] proposed an approach to predict whether a virus can infect the human-body directly utilizing next-generation sequence. The system showed that CNN and LSTM-based architecture outperformed the other machine learning algorithms and generalized to taxonomic units with a half error rate from those that are presented in the training phase.…”

Section: Deep Learning Applications For Covid-19mentioning

confidence: 99%

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

2020

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During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.

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“…Shrikumar et al proposed an idea of reversecomplement network for genomic data, which can simultaneously read double-stranded DNA strands [10]. Based on the network structure, Jakub et al studied the detection method of the novel pathogenic DNA viruses [11], and proposed an interpretable learning method for detecting novel human viruses from genome sequencing data [12].…”

Section: B Feature Extractionmentioning

confidence: 99%

Pathogenic virus detection method based on multi-model fusion

Zhao

Wang

2020

2020 International Conference on Computer, Information and Telecommunication Systems (CITS)

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Identifying viruses is crucial for pandemics. Detecting pathogenic viruses is difficult and usually needs to spend lots of time. Here, we propose a multi-model fusion method for viruses pathogenic detection(MMFPV). We use CatBoost, Reverse-complement Convolutional Neural Networks, and, Reverse-complement Long Short-Term Memory(RC-LSTM) as base models, then, using stacking to combine these models. This method directly detects pathogenic viruses from next-generation sequencing data without virus databases. The result shows that this novel detection method has good capabilities.

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Interpretable detection of novel human viruses from genome sequencing data

Cited by 27 publications

References 81 publications

Deep learning-based real-time detection of novel pathogens during sequencing

Deep learning-based real-time detection of novel pathogens during sequencing

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

Pathogenic virus detection method based on multi-model fusion

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