Name
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.thi.univie.an.ati-wash/RNAz/ |
Year | Venue | Keywords |
|---|---|---|
2010 | Biocomputing-Pacific Symposium on Biocomputing | RNA structure,noncoding RNA,structure conservation,comparative genomics,gene prediction |
Field | DocType | ISSN |
Training set,False discovery rate,Biology,Comparative genomics,Software,Bioinformatics,Genetics,Open source software,Non-coding RNA | Conference | 2335-6936 |
Citations | PageRank | References |
19 | 0.96 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas R. Gruber | 1 | 196 | 8.45 |
| Sven Findeiß | 2 | 77 | 4.67 |
| Stefan Washietl | 3 | 292 | 19.82 |
| Ivo L. Hofacker | 4 | 1669 | 131.57 |
| Peter F. Stadler | 5 | 1839 | 152.96 |