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10.1093/nar/gkw1094 http://scihub22266oqcxt.onion/10.1093/nar/gkw1094 C5389523!5389523!27899588     free     free     free       Nucleic+Acids+Res 2017 ; 45 (5): e35 Nephropedia Template TP {{tp|p=27899588|t=2017. A high-throughput approach to profile RNA structure |pdf=|usr=}}{{27899588}} gab.com Text A high-throughput approach to profile RNA structure JO: Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=27899588 #FOAvip 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%. Twit Text FOAVip A high-throughput approach to profile RNA structure ????Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=27899588 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27899588 #FOAvip English Wikipedia <ref>{{Cite pmid|27899588}}</ref> A high-throughput approach to profile RNA structure #MMPMID27899588 Delli Ponti R; Marti S; Armaos A; Tartaglia G Nucleic Acids Res 2017[Mar]; 45 (5): e35 PMID27899588show ga 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%. äA high-throughput approach to profile RNA structure (epmc).pdf DeepDyve Pubget Overpricing