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10.1093/bib/bbab269 http://scihub22266oqcxt.onion/10.1093/bib/bbab269 34297793!ä!34297793 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2021 ; 22 (6): ä Nephropedia Template TP {{tp|p=34297793|t=2021. Deep learning-based real-time detection of novel pathogens during sequencing |pdf=|usr=}}{{34297793}} gab.com Text Deep learning-based real-time detection of novel pathogens during sequencing JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34297793 #FOAvip Novel 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. Incomplete sequences contain less information, leading to a trade-off between sequencing time and detection accuracy. Using a workflow for real-time pathogenic potential prediction, we investigate which subsequences already allow accurate inference. We train deep neural networks to classify Illumina and Nanopore reads and integrate the models with HiLive2, a real-time Illumina mapper. This approach 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 observe an 80-fold sensitivity increase compared to real-time mapping. The first 250 bp of Nanopore reads, corresponding to 0.5 s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. The approach could also be used for screening synthetic sequences against biosecurity threats. Twit Text FOAVip Deep learning-based real-time detection of novel pathogens during sequencing ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34297793 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34297793 #FOAvip English Wikipedia <ref>{{Cite pmid|34297793}}</ref> Deep learning-based real-time detection of novel pathogens during sequencing #MMPMID34297793 Bartoszewicz JM; Genske U; Renard BY Brief Bioinform 2021[Nov]; 22 (6): ä PMID34297793show ga Novel 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. Incomplete sequences contain less information, leading to a trade-off between sequencing time and detection accuracy. Using a workflow for real-time pathogenic potential prediction, we investigate which subsequences already allow accurate inference. We train deep neural networks to classify Illumina and Nanopore reads and integrate the models with HiLive2, a real-time Illumina mapper. This approach 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 observe an 80-fold sensitivity increase compared to real-time mapping. The first 250 bp of Nanopore reads, corresponding to 0.5 s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. The approach could also be used for screening synthetic sequences against biosecurity threats. |COVID-19/*genetics/virology[MESH] |Deep Learning[MESH] |High-Throughput Nucleotide Sequencing[MESH] |Humans[MESH] |Nanopores[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2/genetics/*isolation & purification/pathogenicity[MESH]Deep learning-based real-time detection of novel pathogens during sequ(epmc).pdf DeepDyve Pubget Overpricing