ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering.

ABSTRACT:

Background

Deep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to

SUBMITTER: Verbist B 

PROVIDER: S-EPMC4369097 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature