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Updated: Jun 18, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
Published on: September 16, 2019
RNAz 2.0: improved noncoding RNA detection
Andreas R Gruber1, Sven Findeiß, Stefan Washietl
1Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany.
Abstract:
RNAz is a widely used software package for de novo detection of structured noncoding RNAs in comparative genomics data. Four years of experience have not only demonstrated the applicability of the approach, but also helped us to identify limitations of the current implementation. RNAz 2.0 provides significant improvements in two respects: (1) The accuracy is increased by the systematic use of dinucleotide models. (2) Technical limitations of the previous version, such as the inability to handle alignments with more than six sequences, are overcome by increased training data and the usage of an entropy measure to represent sequence similarities. RNAz 2.0 shows a significantly lower false discovery rate on a dinucleotide background model than the previous version. Separate models for structural alignments provide an additional way to increase the predictive power. RNAz is open source software and can be obtained free of charge at: http://www.tbi.univie.ac.at/~wash/RNAz/
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