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Updated: Jan 4, 2026
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain
Joel A Southgate1, Matthew J Bull1,2, Clare M Brown1
1Organisms and Environment Division, School of Biosciences, Cardiff University, Cardiff CF10 3AX, UK.
Motivation:
Influenza viruses represent a global public health burden due to annual epidemics and pandemic potential. Due to a rapidly evolving RNA genome, inter-species transmission, intra-host variation, and noise in short-read data, reads can be lost during mapping, and de novo assembly can be time consuming and result in misassembly. We assessed read loss during mapping and designed a graph-based classifier, VAPOR, for selecting mapping references, assembly validation and detection of strains of non-human origin.
Results:
Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole-genome sequencing samples with a mean of >99.8% identity to assemblies, and increased the proportion of mapped reads by up to 13.3% compared to standard references. VAPOR has the potential to improve the robustness of bioinformatics pipelines for surveillance and could be adapted to other RNA viruses.
Availability And Implementation:
VAPOR is available at https://github.com/connor-lab/vapor.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
