• DocumentCode
    191043
  • Title

    VGA: A method for viral quasispecies assembly from ultra-deep sequencing data

  • Author

    Mangul, Serghei ; Wu, Nicholas C. ; Mancuso, Nicholas ; Zelikovsky, Alexander ; Ren Sun ; Eskin, Eleazar

  • Author_Institution
    Comput. Sci. Dept., Univ. of California, Los Angeles, Los Angeles, CA, USA
  • fYear
    2014
  • fDate
    2-4 June 2014
  • Firstpage
    1
  • Lastpage
    1
  • Abstract
    We present VGA, an accurate method for viral quasispecies assembly from ultra-deep sequencing data. The proposed method consists of a high-fidelity sequencing protocol and an accurate method for viral quasispecies assembly, referred to as Viral Genome Assembler (VGA). The proposed protocol is able to eliminate sequencing errors by using individual barcodes attached to the sequencing fragments. Results on both synthetic and real datasets show that our method able to accurately assemble HIV viral quasispecies and detect rare quasispecies previously undetectable due to sequencing errors. VGA outperforms state-of-the-art methods for the viral assembly. Furthermore, our method is the first viral assembly method which scales to millions of sequencing reads. Our tool VGA is freely available at http://genetics.cs.ucla.edu/vga/
  • Keywords
    diseases; genomics; medical computing; microorganisms; HIV viral quasispecies; VGA; Viral Genome Assembler; high-fidelity sequencing protocol; individual barcodes; rare quasispecies; real datasets; sequencing errors; sequencing fragments; synthetic datasets; ultradeep sequencing data; viral assembly method; viral quasispecies assembly; Assembly; Computer science; Human immunodeficiency virus; Protocols; Sequential analysis; Sociology; Statistics; NGS; error-correction protocol; viral assembly; viral quasispecies;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Advances in Bio and Medical Sciences (ICCABS), 2014 IEEE 4th International Conference on
  • Conference_Location
    Miami, FL
  • Print_ISBN
    978-1-4799-5786-6
  • Type

    conf

  • DOI
    10.1109/ICCABS.2014.6863932
  • Filename
    6863932