Genome sequence analyses of the 2014 Ebola Virus (EBOV) isolates revealed a potential problem with the diagnostic assays currently in use; i.e., drifting genomic profiles of the virus may affect the sensitivity or even produce false-negative results. We evaluated signature erosion in ebolavirus molecular assays using an in silico approach and found frequent potential false-negative and false-positive results. We further empirically evaluated many EBOV assays, under real time PCR conditions using EBOV Kikwit (1995) and Makona (2014) RNA templates. These results revealed differences in performance between assays but were comparable between the old and new EBOV templates. Using a whole genome approach and a novel algorithm, termed BioVelocity, we identified new signatures that are unique to each of EBOV, Sudan virus (SUDV), and Reston virus (RESTV). Interestingly, many of the current assay signatures do not fall within these regions, indicating a potential drawback in the past assay design strategies. The new signatures identified in this study may be evaluated with real-time reverse transcription PCR (rRT-PCR) assay development and validation. In addition, we discuss regulatory implications and timely availability to impact a rapidly evolving outbreak using existing but perhaps less than optimal assays versus redesign these assays for addressing genomic changes.

Background: Emerging and reemerging infectious diseases such as the novel Coronavirus disease, COVID-19 and Ebola pose a significant threat to global society and test the public health community’s preparedness to rapidly respond to an outbreak with effective diagnostics and therapeutics. Recent advances in next generation sequencing technologies enable rapid generation of pathogen genome sequence data, within 24 hours of obtaining a sample in some instances. With these data, one can quickly evaluate the effectiveness of existing diagnostics and therapeutics using in silico approaches. The propensity of some viruses to rapidly accumulate mutations can lead to the failure of molecular detection assays creating the need for redesigned or newly designed assays. Results: Here we describe a bioinformatics system named BioLaboro to identify signature regions in a given pathogen genome, design PCR assays targeting those regions, and then test the PCR assays in silico to determine their sensitivity and specificity. We demonstrate BioLaboro with two use cases: Bombali Ebolavirus (BOMV) and the novel Coronavirus 2019 (SARS-CoV-2). For the BOMV, we analyzed 30 currently available real-time reverse transcription-PCR assays against the three available complete genome sequences of BOMV. Only two met our in silico criteria for successful detection and neither had perfect matches to the primer/probe sequences. We designed five new primer sets against BOMV signatures and all had true positive hits to the three BOMV genomes and no false positive hits to any other sequence. Four assays are closely clustered in the nucleoprotein gene and one is located in the glycoprotein gene. Similarly, for the SARS-CoV-2, we designed five highly specific primer sets that hit all 145 whole genomes (available as of February 28, 2020) and none of the near neighbors. Conclusions: Here we applied BioLaboro in two real-world use cases to demonstrate its capability; 1) to identify signature regions, 2) to assess the efficacy of existing PCR assays to detect pathogens as they evolve over time, and 3) to design new assays with perfect in silico detection accuracy, all within hours, for further development and deployment. BioLaboro is designed with a user-friendly graphical user interface for biologists with limited bioinformatics experience.