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VPrimer: A method of designing and updating primer and probe with high variant coverage for RNA virus detection

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dc.contributor.authorJeon, Hajin-
dc.contributor.authorBae, Jeongmin-
dc.contributor.authorKim, Hyerin-
dc.contributor.authorKim, Min-Soo-
dc.date.accessioned2023-08-16T09:28:58Z-
dc.date.available2023-08-16T09:28:58Z-
dc.date.created2022-01-11-
dc.date.issued2023-01-
dc.identifier.issn1545-5963-
dc.identifier.urihttp://scholarworks.bwise.kr/kbri/handle/2023.sw.kbri/169-
dc.description.abstractFatal infectious diseases caused by RNA viruses, such as COVID-19, have emerged around the world. RT-PCR is widely employed for virus detection, and its accuracy depends on the primers and probes since RT-PCR can detect a virus only when the primers and probes bind to the target gene of the virus. Most of primer design methods are for a single host and so require a great deal of effort to design for RNA virus detection, including homology tests among the host and all the viruses for the host using BLAST-like tools. Furthermore, they do not consider variant sequences, which are very common in viruses. In this study, we describe VPrimer, a method of designing high-quality primer-probe sets for RNA viruses. VPrimer can find primer-probe sets that cover more than 95% of the variants of a target virus but do not cover any sequences of other viruses or the host. With VPrimer, we found 381,698,582 primer-probe sets for 3,104 RNA viruses. Multiplex PCR assays using the top 2 primer-probe sets suggested by VPrimer usually cover 100% of variants. To address the rapid changes in viral genomes, VPrimer finds the best and up-to-date primer-probe sets incrementally against the most recently reported variants.-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleVPrimer: A method of designing and updating primer and probe with high variant coverage for RNA virus detection-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Hyerin-
dc.identifier.doi10.1109/TCBB.2021.3138145-
dc.identifier.scopusid2-s2.0-85122108010-
dc.identifier.wosid001013148200039-
dc.identifier.bibliographicCitationIEEE/ACM Transactions on Computational Biology and Bioinformatics, v.20, no.1, pp.775 - 784-
dc.relation.isPartOfIEEE/ACM Transactions on Computational Biology and Bioinformatics-
dc.citation.titleIEEE/ACM Transactions on Computational Biology and Bioinformatics-
dc.citation.volume20-
dc.citation.number1-
dc.citation.startPage775-
dc.citation.endPage784-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusMULTIPLE SEQUENCE ALIGNMENT-
dc.subject.keywordPlusSENSITIVITY-
dc.subject.keywordPlusPCR-
dc.subject.keywordAuthorAlgorithm design and analysis-
dc.subject.keywordAuthorbioinformatics-
dc.subject.keywordAuthordatabase-
dc.subject.keywordAuthorgenomics-
dc.subject.keywordAuthormolecular biology-
dc.subject.keywordAuthorpolymerase chain reaction (PCR)-
dc.subject.keywordAuthorprimer design-
dc.subject.keywordAuthorprobe-
dc.subject.keywordAuthorserver-
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