LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

Abstract

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing methods struggle with these goals because they rely on sequencebased multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used ncRNA gene finder RNAz by a factor of 3 from a median deviation of 47 to 13 nt. Post-processing RNAz predictions, LocARNA-P's STAR score allows much stronger discrimination between true-and false-positive predictions than RNAz's own evaluation. The improved accuracy, in this scenario increased from AUC 0.71 to AUC 0.87, significantly reduces the cost of successive analysis steps. The ready-to-use software tool LocARNA-P produces structure-based multiple RNA alignments with associated columnwise STARs and predicts ncRNA boundaries. We provide additional results, a web server for LocARNA/LocARNA-P, and the software package, including documentation and a pipeline for refining screens for structural ncRNA, at http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/.

An assessment of bacterial small RNA target prediction programs

“…Yet, computational methods have recently benefited from new algorithms and the introduction of RNA accessibility and conservation information that improved their performances. [6][7][8] Here, we evaluate computational target prediction methods using a collection of trusted, experimentally validated sRNA/ mRNA pairs in E. coli. We designed a benchmark system so that wet lab scientists can easily interpret it for practical purposes.…”

Elaborate uORF/IRES features control expression and localization of human glycyl-tRNA synthetase

“…47 This structure was re-evaluated and curated with the last version of the graphical tool Assemble. 48 …”

Large RNA Secondary Structure Conservation Annotation using Secondary Structure-Based MSA

“…Annotation on the level of individual bases enables to visually express the conservation as shown in the experimental section. Another possibility, apart from adjusting a whole structure conservation approach, is to utilize some of the methods for prediction of consensus secondary structure, such as LocaRNA [6] or Infernal [7], and transfer that information back to the structures from which the consensus structure was built, but it is again not obvious how to do that. Both of the approaches are tedious and therefore we introduce here a fast, straightforward method and its freely available implementation which enables conservation annotation of a set of RNA sequences with conservancy levels based on the available secondary structure information.…”