BMC Bioinformatics (Sep 2018)
Application of whole genome data for in silico evaluation of primers and probes routinely employed for the detection of viral species by RT-qPCR using dengue virus as a case study
Abstract
Abstract Background Viral infection by dengue virus is a major public health problem in tropical countries. Early diagnosis and detection are increasingly based on quantitative reverse transcriptase real-time polymerase chain reaction (RT-qPCR) directed against genomic regions conserved between different isolates. Genetic variation can however result in mismatches of primers and probes with their targeted nucleic acid regions. Whole genome sequencing allows to characterize and track such changes, which in turn enables to evaluate, optimize, and (re-)design novel and existing RT-qPCR methods. The immense amount of available sequence data renders this however a labour-intensive and complex task. Results We present a bioinformatics approach that enables in silico evaluation of primers and probes intended for routinely employed RT-qPCR methods. This approach is based on analysing large amounts of publically available whole genome data, by first employing BLASTN to mine the genomic regions targeted by the RT-qPCR method(s), and afterwards using BLASTN-SHORT to evaluate whether primers and probes will anneal based on a set of simple in silico criteria. Using dengue virus as a case study, we evaluated 18 published RT-qPCR methods using more than 3000 publically available genomes in the NCBI Virus Variation Resource, and provide a systematic overview of method performance based on in silico sensitivity and specificity. Conclusions We provide a comprehensive overview of dengue virus RT-qPCR method performance that will aid appropriate method selection allowing to take specific measures that aim to contain and prevent viral spread in afflicted regions. Notably, we find that primer-template mismatches at their 3′ end may represent a general issue for dengue virus RT-qPCR detection methods that merits more attention in their development process. Our approach is also available as a public tool, and demonstrates how utilizing genomic data can provide meaningful insights in an applied public health setting such as the detection of viral species in human diagnostics.
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