| Literature DB >> 25173705 |
Lauren C Chong1, Marco A Albuquerque1, Nicholas J Harding1, Cristian Caloian1, Michelle Chan-Seng-Yue1, Richard de Borja1, Michael Fraser2, Robert E Denroche1, Timothy A Beck1, Theodorus van der Kwast2, Robert G Bristow3, John D McPherson4, Paul C Boutros5.
Abstract
As high-throughput sequencing continues to increase in speed and throughput, routine clinical and industrial application draws closer. These 'production' settings will require enhanced quality monitoring and quality control to optimize output and reduce costs. We developed SeqControl, a framework for predicting sequencing quality and coverage using a set of 15 metrics describing overall coverage, coverage distribution, basewise coverage and basewise quality. Using whole-genome sequences of 27 prostate cancers and 26 normal references, we derived multivariate models that predict sequencing quality and depth. SeqControl robustly predicted how much sequencing was required to reach a given coverage depth (area under the curve (AUC) = 0.993), accurately classified clinically relevant formalin-fixed, paraffin-embedded samples, and made predictions from as little as one-eighth of a sequencing lane (AUC = 0.967). These techniques can be immediately incorporated into existing sequencing pipelines to monitor data quality in real time. SeqControl is available at http://labs.oicr.on.ca/Boutros-lab/software/SeqControl/.Entities:
Mesh:
Year: 2014 PMID: 25173705 DOI: 10.1038/nmeth.3094
Source DB: PubMed Journal: Nat Methods ISSN: 1548-7091 Impact factor: 28.547