Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants

Alexander Artyomenko, Nicholas C. Wu, Serghei Mangul, Eleazar Eskin, Ren Sun, Alex Zelikovsky*

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

As a result of a high rate of mutations and recombination events, an RNA-virus exists as a heterogeneous "swarm" of mutant variants. The long read length offered by single-molecule sequencing technologies allows each mutant variant to be sequenced in a single pass. However, high error rate limits the ability to reconstruct heterogeneous viral population composed of rare, related mutant variants. In this article, we present two single-nucleotide variants (2SNV), a method able to tolerate the high error rate of the single-molecule protocol and reconstruct mutant variants. 2SNV uses linkage between single-nucleotide variations to efficiently distinguish them from read errors. To benchmark the sensitivity of 2SNV, we performed a single-molecule sequencing experiment on a sample containing a titrated level of known viral mutant variants. Our method is able to accurately reconstruct clone with frequency of 0.2% and distinguish clones that differed in only two nucleotides distantly located on the genome. 2SNV outperforms existing methods for full-length viral mutant reconstruction.

Original languageEnglish (US)
Pages (from-to)558-570
Number of pages13
JournalJournal of Computational Biology
Volume24
Issue number6
DOIs
StatePublished - Jun 2017

Keywords

  • RNA viral variants
  • SMRT reads
  • Single-nucleotide variation

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants'. Together they form a unique fingerprint.

  • Cite this