Advertisement

Hepatitis C Virus Database and Bioinformatics Analysis Tools in the Virus Pathogen Resource (ViPR)

  • Yun Zhang
  • Christian Zmasek
  • Guangyu Sun
  • Christopher N. Larsen
  • Richard H. Scheuermann
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1911)

Abstract

The Virus Pathogen Resource (ViPR; www.viprbrc.org) is a US National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center providing bioinformatics support for major human viral pathogens. The hepatitis C virus (HCV) portal of ViPR facilitates basic research and development of diagnostics and therapeutics for HCV, by providing a comprehensive collection of HCV-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, and personal Workbench spaces for data storage and sharing. This chapter introduces the data and functionality provided by the ViPR HCV portal. It describes example workflows for (1) searching HCV genome and protein sequences, (2) conducting phylogenetic analysis, and (3) analyzing sequence variations using pattern search for amino acid substitutions in proteins, single nucleotide variation calculation, metadata-driven comparison, and sequence feature variant type analysis. All data and tools are freely available via the ViPR HCV portal at https://www.viprbrc.org/brc/home.spg?decorator=flavi_hcv.

Key words

Virus Pathogen Resource ViPR Database Annotation Genotype Mature peptides Comparative genomics Phylogenetics Antiviral Drug resistance 

Notes

Acknowledgment

This work was supported by the National Institutes of Health/National Institute for Allergy and Infectious Diseases [HHSN272201400028C].

References

  1. 1.
    WHO (2017) Hepatitis C. http://www.who.int/mediacentre/factsheets/fs164/en/. Accessed 11 June 2017
  2. 2.
    Pickett BE, Sadat EL, Zhang Y et al (2012) ViPR: an open bioinformatics database and analysis resource for virology research. Nucleic Acids Res 40:D593–D598CrossRefGoogle Scholar
  3. 3.
    ICTV Flaviviridae Study Group (2017) Updated alignment (FASTA) of HCV genotypes and subtypes 1.6.17.FST. https://talk.ictvonline.org/ictv_wikis/flaviviridae/w/sg_flavi/57/hcv-reference-sequence-alignments. Accessed 11 June 2017
  4. 4.
    Matsen FA, Kodner RB, Armbrust EV (2010) pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11:538CrossRefGoogle Scholar
  5. 5.
  6. 6.
    ViPR (2017) Protocol for virus mature peptide prediction. https://www.viprbrc.org/brcDocs/documents/VIPR_MAT_PEPTIDE_SOP.pdf. Accessed 11 June 2017
  7. 7.
    Pickett BE, Liu M, Sadat EL et al (2013) Metadata-driven comparative analysis tool for sequences (meta-CATS): an automated process for identifying significant sequence variations that correlate with virus attributes. Virology 447:45–51CrossRefGoogle Scholar
  8. 8.
    ViPR (2017) Protocol for meta-CATS analysis tool. https://www.viprbrc.org/brcDocs/documents/MGC_Protocol.pdf. Accessed 11 June 2017
  9. 9.
    Noronha JM, Liu M, Squires RB et al (2012) Influenza virus sequence feature variant type analysis: evidence of a role for NS1 in influenza virus host range restriction. J Virol 86:5857–5866CrossRefGoogle Scholar
  10. 10.
    Desper R, Gascuel O (2002) Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle. J Comput Biol 9:687–705CrossRefGoogle Scholar
  11. 11.
    Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313CrossRefGoogle Scholar
  12. 12.
    Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52:696–704CrossRefGoogle Scholar
  13. 13.
    AASLD-IDSA (2017) Monitoring patients who are starting Hepatitis C treatment, are on treatment, or have completed therapy. Recommendations for testing, managing, and treating hepatitis C. http://www.hcvguidelines.org/print/92. Accessed 11 June 2017
  14. 14.
    Nakamoto S, Kanda T, Wu S et al (2014) Hepatitis C virus NS5A inhibitors and drug resistance mutations. World J Gastroenterol 20:2902–2912CrossRefGoogle Scholar
  15. 15.
    Lawitz EJ, Dvory-Sobol H, Doehle BP et al (2016) Clinical resistance to velpatasvir (GS-5816), a novel pan-genotypic inhibitor of the hepatitis C virus NS5A protein. Antimicrob Agents Chemother 60:5368–5378CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yun Zhang
    • 1
  • Christian Zmasek
    • 1
  • Guangyu Sun
    • 4
  • Christopher N. Larsen
    • 4
  • Richard H. Scheuermann
    • 1
    • 2
    • 3
  1. 1.J. Craig Venter InstituteLa JollaUSA
  2. 2.Department of PathologyUniversity of CaliforniaSan DiegoUSA
  3. 3.Division of Vaccine DiscoveryLa Jolla Institute for Allergy and ImmunologyLa JollaUSA
  4. 4.Vecna TechnologiesGreenbeltUSA

Personalised recommendations