Viral Quasispecies Assembly via Maximal Clique Enumeration

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• 作者：Armin T?pfer (20) (21)
  Tobias Marschall (22)
  Rowena A. Bull (23)
  Fabio Luciani (23)
  Alexander Sch?nhuth (22)
  Niko Beerenwinkel (20)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8394
• 期：1
• 页码：309-310
• 全文大小：141 KB
• 作者单位：Armin T?pfer (20) (21)
  Tobias Marschall (22)
  Rowena A. Bull (23)
  Fabio Luciani (23)
  Alexander Sch?nhuth (22)
  Niko Beerenwinkel (20)
  
  20. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
  21. SIB Swiss Institute of Bioinformatics, Switzerland
  22. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
  23. Inflammation and Infection Research Centre, School of Medical Sciences, UNSW, Sydney, Australia
  
• ISSN：1611-3349

文摘

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.