Background#R##N#Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth (“deep sequencing”), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset.
['Koen Van der Borght', 'Kim Thys', 'Yves Wetzels', 'Lieven Clement', 'Bie Verbist', 'Joke Reumers', 'Herman van Vlijmen', 'Jeroen Aerssens']
QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles