Identification of recent cases of hepatitis C virus infection using physical-chemical properties of hypervariable region 1 and a radial basis function neural network classifier.

Journal: BMC genomics
PMID: 29244000

Abstract

BACKGROUND: Identification of acute or recent hepatitis C virus (HCV) infections is important for detecting outbreaks and devising timely public health interventions for interruption of transmission. Epidemiological investigations and chemistry-based laboratory tests are 2 main approaches that are available for identification of acute HCV infection. However, owing to complexity, both approaches are not efficient. Here, we describe a new sequence alignment-free method to discriminate between recent (R) and chronic (C) HCV infection using next-generation sequencing (NGS) data derived from the HCV hypervariable region 1 (HVR1).

Authors

  • James Lara
    Division of Viral Hepatitis, National Center for HIV, Hepatitis, TB and STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA. xzl5@cdc.gov.
  • Mahder Teka
    Division of Viral Hepatitis, National Center for HIV, Hepatitis, TB and STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.
  • Yury Khudyakov
    Division of Viral Hepatitis, National Center for HIV, Hepatitis, TB and STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.

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