Population sequencing using short reads: HIV as a case study

Vladimir Jojic, Tomer Hertz, Nebojsa Jojic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

Despite many drawbacks, traditional sequencing technologies have proven to be invaluable in modern medical research, even when the targeted genomes are highly variable. While it is often known in such cases that multiple slightly different sequences are present in the analyzed sample in concentrations that vary dramatically, the traditional techniques typically allow only the most dominant strain to be extracted from a single chromatogram. These limitations made some research directions rather difficult to pursue. For example, the analysis of HIV evolution (including the emergence of drug resistance) in a single patient is expected to benefit from a comprehensive catalog of the patient's HIV population. In this paper, we show how the new generation of sequencing technologies, based on high throughput of short reads, can be used to link site variants and reconstruct multiple full strains of the targeted gene, including those of low concentration in the sample. Our algorithm is based on a generative model of the sequencing process, and uses a tailored probabilistic inference and learning procedure to fit the model to the obtained reads.

Original languageEnglish
Title of host publicationPacific Symposium on Biocomputing 2008, PSB 2008
Pages114-125
Number of pages12
StatePublished - 1 Dec 2008
Externally publishedYes
Event13th Pacific Symposium on Biocomputing, PSB 2008 - Kohala Coast, HI, United States
Duration: 4 Jan 20088 Jan 2008

Publication series

NamePacific Symposium on Biocomputing 2008, PSB 2008

Conference

Conference13th Pacific Symposium on Biocomputing, PSB 2008
Country/TerritoryUnited States
CityKohala Coast, HI
Period4/01/088/01/08

Keywords

  • HIV
  • epitome
  • multiple strains
  • population
  • rare variants
  • sequence assembly
  • variant linkage

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Biomedical Engineering
  • General Medicine

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