Detecting poliovirus sequences in the sequence read archive database using bioinformatics tools

One approach to find possible poliovirus sources that have not been detected by the Global Polio Eradication Initiative's (GPEI) surveillance systems is to scan reads in the Sequence Read Archive (SRA) for potential poliovirus sequences. In the post-eradication era, the identification of poliovirus sequences in the SRA database could signal a potential biosafety risk which may set back the enormous achievements of the GPEI. To advance the use of the SRA database for detecting poliovirus, we hypothesized that bioinformatics alignment tools like Bowtie2, BLASTn, Magic-BLAST, MegaBLAST, STAT, and ElasticBLAST could distinguish between non-poliovirus and poliovirus reads from samples represented in the SRA database. Short poliovirus sequencing reads were simulated using poliovirus Sabin strain genomes. Simulation was also done for sequences other than poliovirus (referred here as "non-poliovirus reads"). Simulated reads were aligned to reference poliovirus genomes using different alignment tools to benchmark the accuracy and computing time of each tool. Parameters were also established to identify previously unknown poliovirus reads using percent identity and alignment length from BLASTn results. Bowtie2 was the most accurate and efficient tool, correctly identifying all simulated poliovirus reads. The STAT tool detected 99.6% of known control poliovirus accessions using the enterovirus query but only 77.6% using the poliovirus query, demonstrating strong but incomplete detection capability. This study demonstrates the feasibility of screening the SRA for poliovirus sequences as a tool to strengthen poliovirus containment and mitigate post-eradication risks, which can serve as an additional safety net for the GPEI.