Unknown

Dataset Information

0

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


ABSTRACT: 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 > or =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.

SUBMITTER: Wang C 

PROVIDER: S-EPMC1933516 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Wang Chunlin C   Mitsuya Yumi Y   Gharizadeh Baback B   Ronaghi Mostafa M   Shafer Robert W RW  

Genome research 20070628 8


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-d  ...[more]

Similar Datasets

| S-EPMC5249062 | biostudies-literature
| S-EPMC4710461 | biostudies-literature
| S-EPMC3344889 | biostudies-literature
| S-EPMC2822863 | biostudies-literature
| S-EPMC3143174 | biostudies-literature
| S-EPMC2898805 | biostudies-literature
| S-EPMC4466260 | biostudies-literature
| S-EPMC3740783 | biostudies-literature
| S-EPMC5749046 | biostudies-literature
| S-EPMC4653658 | biostudies-literature