Projects per year
PCR diagnostics are often the first line of laboratory diagnostics and are regularly designed to either differentiate between or detect all pathogen variants of a family, genus or species. The ideal PCR test detects all variants of the target pathogen, including newly discovered and emerging variants, while closely related pathogens and their variants should not be detected. This is challenging as pathogens show a high degree of genetic variation due to genetic drift, adaptation and evolution. Therefore, frequent re-evaluation of PCR diagnostics is needed to monitor its usefulness. Validation of PCR diagnostics recognizes three stages, in silico, in vitro and in vivo validation. In vitro and in vivo testing are usually costly, labour intensive and imply a risk of handling dangerous pathogens. In silico validation reduces this burden. In silico validation checks primers and probes by comparing their sequences with available nucleotide sequences. In recent years the amount of available sequences has dramatically increased by high throughput and deep sequencing projects. This makes in silico validation more informative, but also more computing intensive. To facilitate validation of PCR tests, a software tool named PCRv was developed. PCRv consists of a user friendly graphical user interface and coordinates the use of the software programs ClustalW and SSEARCH in order to perform in silico validation of PCR tests of different formats. Use of internal control sequences makes the analysis compliant to laboratory quality control systems. Finally, PCRv generates a validation report that includes an overview as well as a list of detailed results. In-house developed, published and OIE-recommended PCR tests were easily (re-) evaluated by use of PCRv. To demonstrate the power of PCRv, in silico validation of several PCR tests are shown and discussed.
van Weezep, E., Kooi, B., & van Rijn, P. A. (2019). PCR diagnostics: In silico validation by an automated tool using freely available software programs. Journal of Virological Methods, 270, 106-112. https://doi.org/10.1016/j.jviromet.2019.05.002