A high-throughput approach to profile RNA structure
Abstract
Here we introduce the Computational Recognition of Secondary Structure (CROSS) method to calculate the structural profile of an RNA sequence (single- or double-stranded state) at single-nucleotide resolution and without sequence length restrictions. We trained CROSS using data from high-throughput experiments such as Selective 2΄-Hydroxyl Acylation analyzed by Primer Extension (SHAPE; Mouse and HIV transcriptomes) and Parallel Analysis of RNA Structure (PARS; Human and Yeast transcriptomes) as well as high-quality NMR/X-ray structures (PDB database). The algorithm uses primary structure information alone to predict experimental structural profiles with >80% accuracy, showing high performances on large RNAs such as Xist (17 900 nucleotides; Area Under the ROC Curve AUC of 0.75 on dimethyl sulfate (DMS) experiments). We integrated CROSS in thermodynamics-based methods to predict secondary structure and observed an increase in their predictive power by up to 30%. ; The research leading to these results has received funding from European Union Seventh Framework Programme [FP7/2007-2013]; European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]; AGAUR [2014 SGR 00685 to GGT]; Spanish Ministry of Economy and Competitiveness, European Research Development Fund ERDF, 'Centro de Excelencia Severo Ochoa 2013-2017' [SEV-2012-0208]. Funding for open access charge: European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]. The authors also thank the CRG fellowship to SM.
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