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Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing

Mattia CF Prosperi12*, Luciano Prosperi, Alessandro Bruselles3, Isabella Abbate3, Gabriella Rozera3, Donatella Vincenti3, Maria Carmela Solmone3, Maria Rosaria Capobianchi3 and Giovanni Ulivi4

  • * Corresponding author: Mattia CF Prosperi

  • † Equal contributors

Author Affiliations

1 Clinic of Infectious Diseases, Catholic University of the Sacred Heart, Rome, Italy

2 Department of Pathology, Immunology and Laboratory Medicine, Emerging Pathogens Institute, College of Medicine, University of Florida, Gainesville, Florida, USA

3 Department of Virology, National Institute for Infectious Diseases "L. Spallanzani", Rome, Italy

4 Department of Computer Science and Automation, faculty of Computer Science Engineering, University of Roma TRE, Rome, Italy

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BMC Bioinformatics 2011, 12:5  doi:10.1186/1471-2105-12-5

Published: 5 January 2011



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.


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.


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.