DescriptionUrQt : Unsupervised Quality trimming for NGS data Quality check (QC) is a necessary step of every Next Generation Sequencing (NGS) analyses. While customary, this preprocessing of the data still requires manual interventions in order to empirically choose tuning parameters according to different quality statistics. These choices require a strong experience in preprocessing NGS data to be made and can represent an obstacle for the reproducibility of an experiment. Moreover, if manual QC should provide data set of good quality, these procedures can potentially remove large number of nucleotides of good quality. To overcome these common drawbacks of QC, we present a new method for the unsupervised quality trimming of NGS data implemented in the UrQt software (Unsupervised read Quality trimming). In this software, the trimming procedure relies on a well-­defined statistical framework to detect the best segmentation possible between a segment of nucleotides of good quality and a segment of nucleotides of unreliable quality at the head and tail of each read from an NGS experiment. The unsupervised aspect of the proposed method removes the need for manual expertise while its maximum likelihood aspect aims to minimize the number of nucleotides of good quality removed. By getting rid of manual intervention manual intervention for data preprocessing we also ensure its high reproducibility.
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