tetano
Editor, Senior Moderator
Bioinformatics. 2019 Nov 6. pii: btz814. doi: 10.1093/bioinformatics/btz814. [Epub ahead of print] [h=1]Influenza Classification from Short Reads with VAPOR Facilitates Robust Mapping Pipelines and Zoonotic Strain Detection for Routine Surveillance Applications.[/h]
Southgate JA[SUP]1[/SUP], Bull MJ[SUP]1,[/SUP][SUP]2[/SUP], Brown CM[SUP]1[/SUP], Watkins J[SUP]2[/SUP], Corden S[SUP]2[/SUP], Southgate B[SUP]3[/SUP], Moore C[SUP]2[/SUP], Connor TR[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h] 1 Organisms and Environment Division, School of Biosciences, Cardiff University, United Kingdom. 2 Public Health Wales, University Hospital of Wales, Cardiff, United Kingdom. 3 MRC Centre for Regenerative Medicine, University of Edinburgh, United Kingdom.
[h=3]Abstract[/h] [h=4]MOTIVATION:[/h] 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.
[h=4]RESULTS:[/h] Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole genome sequencing (WGS) 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.
[h=4]AVAILABILITY:[/h] VAPOR is available at https://github.com/connor-lab/vapor.
[h=4]SUPPLEMENTARY INFORMATION:[/h] Supplementary data are available at Bioinformatics online.
? The Author(s) 2019. Published by Oxford University Press.
PMID: 31693070 DOI: 10.1093/bioinformatics/btz814
Southgate JA[SUP]1[/SUP], Bull MJ[SUP]1,[/SUP][SUP]2[/SUP], Brown CM[SUP]1[/SUP], Watkins J[SUP]2[/SUP], Corden S[SUP]2[/SUP], Southgate B[SUP]3[/SUP], Moore C[SUP]2[/SUP], Connor TR[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h] 1 Organisms and Environment Division, School of Biosciences, Cardiff University, United Kingdom. 2 Public Health Wales, University Hospital of Wales, Cardiff, United Kingdom. 3 MRC Centre for Regenerative Medicine, University of Edinburgh, United Kingdom.
[h=3]Abstract[/h] [h=4]MOTIVATION:[/h] 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.
[h=4]RESULTS:[/h] Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole genome sequencing (WGS) 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.
[h=4]AVAILABILITY:[/h] VAPOR is available at https://github.com/connor-lab/vapor.
[h=4]SUPPLEMENTARY INFORMATION:[/h] Supplementary data are available at Bioinformatics online.
? The Author(s) 2019. Published by Oxford University Press.
PMID: 31693070 DOI: 10.1093/bioinformatics/btz814