Synthetic spike-in standards improve run-specific systematic error analysis for DNA and RNA sequencing.

ABSTRACT: While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a data set used to calculate association of SSEs with various features in the reads and sequence context. This data set is typically either from a part of the data set being "recalibrated" (Genome Analysis ToolK

SUBMITTER: Zook JM 

PROVIDER: S-EPMC3409179 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature