This Article OPEN ACCESS ARTICLE Full Text Full Text (PDF) Supplemental Research Data All Versions of this Article: gr.6468307v1 17/8/1195    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Wang, C. Articles by Shafer, R. W. Search for Related Content PubMed PubMed Citation Articles by Wang, C. Articles by Shafer, R. W. Social Bookmarking                   What's this?

Methods

Characterization of mutation spectra with ultra-deep pyrosequencing: Application to HIV-1 drug resistance

¹ Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, California 94305, USA; ² Stanford Genome Technology Center, Stanford University, Stanford, California 94305, USA

The detection of mutant spectra within a population of microorganisms is critical for the management of drug-resistant infections. We performed ultra-deep pyrosequencing to detect minor sequence variants in HIV-1 protease and reverse transcriptase (RT) genes from clinical plasma samples. We estimated empirical error rates from four HIV-1 plasmid clones and used them to develop a statistical approach to distinguish authentic minor variants from sequencing errors in eight clinical samples. Ultra-deep pyrosequencing detected an average of 58 variants per sample compared with an average of eight variants per sample detected by conventional direct-PCR dideoxynucleotide sequencing. In the clinical sample with the largest number of minor sequence variants, all 60 variants present in 3% of genomes and 20 of 35 variants present in <3% of genomes were confirmed by limiting dilution sequencing. With appropriate analysis, ultra-deep pyrosequencing is a promising method for characterizing genetic diversity and detecting minor yet clinically relevant variants in biological samples with complex genetic populations.

⁴ Corresponding author.

E-mail rshafer{at}stanford.edu; fax (650) 725-2088.

[Supplemental material is available online at www.genome.org. The raw data from this study are available online at http://dbpartners.stanford.edu/454/pub.]

Article published online before print. Article and publication date are online at http://www.genome.org/cgi/doi/10.1101/gr.6468307

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?