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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures
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DOI：10.1186/s13059-018-1399-z.png)
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摘要
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Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions.
Keyword:
RNA structure
Structure probing
Machine learning
Pattern recognition
Functional elements
Transcriptome-wide profiling
SHAPE
PARS
RNA structure-function
GMM-HMM
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PROCEEDINGS OF THE IEEEIF25.9
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