title: Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing.
abstract: BACKGROUND
  Next-generation sequencing (NGS) offers a unique opportunity for high-throughput genomics and has potential to replace Sanger sequencing in many fields, including de-novo sequencing, re-sequencing, meta-genomics, and characterisation of infectious pathogens, such as viral quasispecies. Although methodologies and software for whole genome assembly and genome variation analysis have been developed and refined for NGS data, reconstructing a viral quasispecies using NGS data remains a challenge. This application would be useful for analysing intra-host evolutionary pathways in relation to immune responses and antiretroviral therapy exposures. Here we introduce a set of formulae for the combinatorial analysis of a quasispecies, given a NGS re-sequencing experiment and an algorithm for quasispecies reconstruction. We require that sequenced fragments are aligned against a reference genome, and that the reference genome is partitioned into a set of sliding windows (amplicons). The reconstruction algorithm is based on combinations of multinomial distributions and is designed to minimise the reconstruction of false variants, called in-silico recombinants.
  
  
  RESULTS
  The reconstruction algorithm was applied to error-free simulated data and reconstructed a high percentage of true variants, even at a low genetic diversity, where the chance to obtain in-silico recombinants is high. Results on empirical NGS data from patients infected with hepatitis B virus, confirmed its ability to characterise different viral variants from distinct patients.
  
  
  CONCLUSIONS
  The combinatorial analysis provided a description of the difficulty to reconstruct a quasispecies, given a determined amplicon partition and a measure of population diversity. The reconstruction algorithm showed good performance both considering simulated data and real data, even in presence of sequencing errors.
authors.affiliation: Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it
authors.forename: Mattia C F
authors.initials: MC
authors.lastname: Prosperi
authors.forename: Luciano
authors.initials: L
authors.lastname: Prosperi
authors.forename: Alessandro
authors.initials: A
authors.lastname: Bruselles
authors.forename: Isabella
authors.initials: I
authors.lastname: Abbate
authors.forename: Gabriella
authors.initials: G
authors.lastname: Rozera
authors.forename: Donatella
authors.initials: D
authors.lastname: Vincenti
authors.forename: Maria Carmela
authors.initials: MC
authors.lastname: Solmone
authors.forename: Maria Rosaria
authors.initials: MR
authors.lastname: Capobianchi
authors.forename: Giovanni
authors.initials: G
authors.lastname: Ulivi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D014644:Genetic Variation; D016679:Genome, Viral / Q000235:genetics; D023281:Genomics / Q000379:methods*; D006515:Hepatitis B virus / Q000145:classification / Q000235:genetics*; D006801:Humans; D010802:Phylogeny; D017422:Sequence Analysis, DNA; D012984:Software