ena-STUDY-Cardiff University-09-08-2019-11:50:04:497-25
Influenza Classification from Short Reads with VAPOR Facilitates Robust Mapping Pipelines and Zoonotic Strain Detection for Routine Surveillance Applications
Influenza viruses are associated with a significant global public health burden. Whole-genome sequencing (WGS) has begun to emerge as a useful tool in surveillance. However, due to the diversity and mutability of the influenza genome, and noise in short-read data, bioinformatics processing can present challenges.We developed a graph-based classifier of influenza WGS datasets: VAPOR. In real data benchmarking using 257 WGS read sets with corresponding de novo assemblies, VAPOR provided classifications for all samples with a mean of >99.8% identity to assembled contigs. This resulted in an increase in the number of mapped reads by 6.8% on average, up to a maximum of 13.3%.VAPOR is available at https://github.com/connor-lab/vapor.
Influenza viruses are associated with a significant global public health burden. Whole-genome sequencing (WGS) has begun to emerge as a useful tool in surveillance. However, due to the diversity and mutability of the influenza genome, and noise in short-read data, bioinformatics processing can present challenges.We developed a graph-based classifier of influenza WGS datasets: VAPOR. In real data benchmarking using 257 WGS read sets with corresponding de novo assemblies, VAPOR provided classifications for all samples with a mean of >99.8% identity to assembled contigs. This resulted in an increase in the number of mapped reads by 6.8% on average, up to a maximum of 13.3%.VAPOR is available at https://github.com/connor-lab/vapor.