ArticleBMC bioinformatics2015
UrQt: an efficient software for the Unsupervised Quality trimming of NGS data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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32 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Expansions of adaptive-like NK cells with a tissue-resident phenotype in human lung and blood.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A Large-Scale and Serverless Computational Approach for Improving Quality of NGS Data Supporting Big Multi-Omics Data Analyses.Frontiers in genetics · 2021Article
- Viral infection impacts transposable element transcript amounts inProceedings of the National Academy of Sciences of the United States of America · 2020Article
- piRNA and Transposon Dynamics in Drosophila: A Female Story.Genome biology and evolution · 2020Article
- Transposable elements contribute to the genomic response to insecticides inPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundQuality 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.
resultsOur 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 one user-friendly parameter to define the minimal quality threshold (phred score) to consider a nucleotide to be informative, which is independent of both the experiment and the quality of the data. This procedure is implemented in C++ in an efficient and parallelized software with a low memory footprint. We tested the performances of UrQt compared to the best-known trimming programs, on seven RNA and DNA sequencing experiments and demonstrated its optimality in the resulting tradeoff between the number of trimmed nucleotides and the quality objective.
conclusionsBy finding the best segmentation to delimit a segment of good quality nucleotides, UrQt greatly increases the number of reads and of nucleotides that can be retained for a given quality objective. UrQt source files, binary executables for different operating systems and documentation are freely available (under the GPLv3) at the following address: https://lbbe.univ-lyon1.fr/-UrQt-.html .
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