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Viral Quasispecies Assembly via Maximal Clique Enumeration

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 8394)

Abstract

Genetic variability of virus populations within individual hosts is a key determinant of pathogenesis, virulence, and treatment outcome. It is of clinical importance to identify and quantify the intra-host ensemble of viral haplotypes, called viral quasispecies. Ultra-deep next-generation sequencing (NGS) of mixed samples is currently the only efficient way to probe genetic diversity of virus populations in greater detail. Major challenges with this bulk sequencing approach are (i) to distinguish genetic diversity from sequencing errors, (ii) to assemble an unknown number of different, unknown, haplotype sequences over a genomic region larger than the average read length, (iii) to estimate their frequency distribution, and (iv) to detect structural variants, such as large insertions and deletions (indels) that are due to erroneous replication or alternative splicing. Even though NGS is currently introduced in clinical diagnostics, the de-facto standard procedure to assess the quasispecies structure is still single-nucleotide variant (SNV) calling. Viral phenotypes cannot be predicted solely from individual SNVs, as epistatic interactions are abundant in RNA viruses. Therefore, reconstruction of long-range viral haplotypes has the potential to be adopted, as data is already available.

Keywords

  • Sequencing Error
  • Viral Phenotype
  • Average Read Length
  • Phred Score
  • Haplotype Sequence

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2014 Springer International Publishing Switzerland

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Töpfer, A., Marschall, T., Bull, R.A., Luciani, F., Schönhuth, A., Beerenwinkel, N. (2014). Viral Quasispecies Assembly via Maximal Clique Enumeration. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-05269-4_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05268-7

  • Online ISBN: 978-3-319-05269-4

  • eBook Packages: Computer ScienceComputer Science (R0)