title: Viral quasispecies inference from 454 pyrosequencing.
abstract: BACKGROUND
  Many potentially life-threatening infectious viruses are highly mutable in nature. Characterizing the fittest variants within a quasispecies from infected patients is expected to allow unprecedented opportunities to investigate the relationship between quasispecies diversity and disease epidemiology. The advent of next-generation sequencing technologies has allowed the study of virus diversity with high-throughput sequencing, although these methods come with higher rates of errors which can artificially increase diversity.
  
  
  RESULTS
  Here we introduce a novel computational approach that incorporates base quality scores from next-generation sequencers for reconstructing viral genome sequences that simultaneously infers the number of variants within a quasispecies that are present. Comparisons on simulated and clinical data on dengue virus suggest that the novel approach provides a more accurate inference of the underlying number of variants within the quasispecies, which is vital for clinical efforts in mapping the within-host viral diversity. Sequence alignments generated by our approach are also found to exhibit lower rates of error.
  
  
  CONCLUSIONS
  The ability to infer the viral quasispecies colony that is present within a human host provides the potential for a more accurate classification of the viral phenotype. Understanding the genomics of viruses will be relevant not just to studying how to control or even eradicate these viral infectious diseases, but also in learning about the innate protection in the human host against the viruses.
authors.forename: Wan-Ting
authors.initials: WT
authors.lastname: Poh
authors.forename: Eryu
authors.initials: E
authors.lastname: Xia
authors.forename: Kwanrutai
authors.initials: K
authors.lastname: Chin-Inmanu
authors.forename: Lai-Ping
authors.initials: LP
authors.lastname: Wong
authors.forename: Anthony Youzhi
authors.initials: AY
authors.lastname: Cheng
authors.forename: Prida
authors.initials: P
authors.lastname: Malasit
authors.forename: Prapat
authors.initials: P
authors.lastname: Suriyaphol
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore. statyy@nus.edu.sg.
authors.forename: Yik-Ying
authors.initials: YY
authors.lastname: Teo
authors.forename: Rick Twee-Hee
authors.initials: RT
authors.lastname: Ong
mesh_terms: D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003715:Dengue / Q000235:genetics / Q000821:virology; D003716:Dengue Virus / Q000145:classification / Q000235:genetics*; D014644:Genetic Variation; D016679:Genome, Viral / Q000235:genetics*; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D010641:Phenotype; D011995:Recombination, Genetic; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*; D013045:Species Specificity