UrQt: an efficient software for the Unsupervised Quality trimming of NGS data.

ABSTRACT:

Background

Quality control is a necessary step of any Next Generation Sequencing analysis. Although customary, this step still requires manual interventions to empirically choose tuning parameters according to various quality statistics. Moreover, current quality control procedures that provide a "good quality" data set, are not optimal and discard many informative nucleotides. To address these drawbacks, we present a new quality control method, implemented in UrQt software, for Unsupervised Quality trimming of Next Generation Sequencing reads.

Results

Our trimming procedure relies on a well-defined probabilistic framework to detect the best segmentation between two segments of unreliable nucleotides, framing a segment of informative nucleotides. Our software only requires o

SUBMITTER: Modolo L 

PROVIDER: S-EPMC4450468 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature