Journal
ANNUAL REVIEW OF BIOPHYSICS, VOL 43
Volume 43, Issue -, Pages 433-456Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev-biophys-051013-022950
Keywords
noncoding RNA; lncRNA; probing; SHAPE; statistical inference
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Funding
- Howard Hughes Medical Institute Funding Source: Medline
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Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.
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