title: A machine learning approach for viral genome classification.
abstract: BACKGROUND
  Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families.
  
  
  RESULTS
  Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments.
  
  
  CONCLUSIONS
  The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .
authors.affiliation: Laboratoire de bioinformatique, département d'informatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Pharmaqam Center, Université du Québec à Montréal (Québec), Montréal (Quebec), PO BOX 8888 Downtown Station, H3C 3P8, Canada.
authors.forename: Mohamed Amine
authors.initials: MA
authors.lastname: Remita
authors.affiliation: Laboratoire de bioinformatique, département d'informatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Pharmaqam Center, Université du Québec à Montréal (Québec), Montréal (Quebec), PO BOX 8888 Downtown Station, H3C 3P8, Canada.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Halioui
authors.affiliation: Laboratoire de bioinformatique, département d'informatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Pharmaqam Center, Université du Québec à Montréal (Québec), Montréal (Quebec), PO BOX 8888 Downtown Station, H3C 3P8, Canada.
authors.forename: Abou Abdallah
authors.initials: AA
authors.lastname: Malick Diouara
authors.affiliation: Laboratoire de bioinformatique, département d'informatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Pharmaqam Center, Université du Québec à Montréal (Québec), Montréal (Quebec), PO BOX 8888 Downtown Station, H3C 3P8, Canada.
authors.forename: Bruno
authors.initials: B
authors.lastname: Daigle
authors.affiliation: Laboratoire de bioinformatique, département d'informatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Pharmaqam Center, Université du Québec à Montréal (Québec), Montréal (Quebec), PO BOX 8888 Downtown Station, H3C 3P8, Canada.
authors.forename: Golrokh
authors.initials: G
authors.lastname: Kiani
authors.affiliation: Laboratoire de bioinformatique, département d'informatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada. diallo.abdoulaye@uqam.ca.|Pharmaqam Center, Université du Québec à Montréal (Québec), Montréal (Quebec), PO BOX 8888 Downtown Station, H3C 3P8, Canada. diallo.abdoulaye@uqam.ca.
authors.forename: Abdoulaye Baniré
authors.identifier: http://orcid.org/0000-0002-1168-9371
authors.initials: AB
authors.lastname: Diallo
mesh_terms: D002965:Classification; D003198:Computer Simulation; D016679:Genome, Viral; D023281:Genomics / Q000379:methods*; D015497:HIV-1 / Q000235:genetics; D006515:Hepatitis B virus / Q000235:genetics; D006801:Humans; D000069550:Machine Learning; D027383:Papillomaviridae / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods
keywords: Prediction; Sequence classification; Virus classification