Dataset Information


Interpreting Viral Deep Sequencing Data with GLUE.

ABSTRACT: Using deep sequencing technologies such as Illumina's platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data.


PROVIDER: S-EPMC6520954 | BioStudies | 2019-01-01

REPOSITORIES: biostudies

Similar Datasets

1000-01-01 | S-EPMC3027120 | BioStudies
1000-01-01 | S-EPMC4920121 | BioStudies
2016-01-01 | S-EPMC4934512 | BioStudies
1000-01-01 | S-EPMC3534618 | BioStudies
2019-01-01 | S-EPMC7107797 | BioStudies
2017-01-01 | S-EPMC5735922 | BioStudies
1000-01-01 | S-EPMC3846741 | BioStudies
2014-01-01 | S-EPMC3958049 | BioStudies
2009-01-01 | S-EPMC2704438 | BioStudies
2014-01-01 | S-EPMC4147885 | BioStudies