ArticleBioinformatics (Oxford, England)2020
Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of genomic characteristics for antiviral influenza defective interfering particle prioritization.NAR genomics and bioinformatics · 2025Pooled it
- NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.BMC bioinformatics · 2026Article
- Enterovirus D68 Subgenotype B3 Circulation in Children with Acute Respiratory Illness in the State of Alagoas, Brazil.Viruses · 2025Article
- Comprehensive molecular epidemiology of influenza viruses in Brazil: insights from a nationwide analysis.Virus evolution · 2025Article
- Influenza A virus shedding and reinfection during the post-weaning period in swine: longitudinal study of two nurseries.Frontiers in veterinary science · 2024Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Prospecting Human Milk Oligosaccharides as a Defense Against Viral Infections.ACS infectious diseases · 2021Review
- Evaluation of methods for detecting human reads in microbial sequencing datasets.Microbial genomics · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
motivationInfluenza 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.
resultsStandard 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.