Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications.

ABSTRACT:

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 m

SUBMITTER: Southgate JA 

PROVIDER: S-EPMC7703727 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature