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SUMMARY:Evolutionary Conservation of RNA structure
LOCATION:Bio 136
TZID:America/Denver
DTSTART:20230207T160000
UID:2026-04-21-10-51-41@natsci.colostate.edu
DTSTAMP:20260421T105141
Description:Many functional RNAs depend on particular 3D structures. A long
  tradition of computational methods aims to infer RNA secondary structure 
 (that is\, the collection of base pairs) from sequence. However\, essentia
 lly any sequence can be folded into a plausible structure. To distinguish 
 RNA sequences that have biologically relevant structures from those that d
 o not\, additional evidence is needed. One powerful source of evidence is 
 evolutionary conservation of RNA sequence and structure\, which induces pa
 irwise covariations that can be observed in RNA multiple sequence alignmen
 ts.\n\nKnowing when an RNA sequence includes a conserved RNA structure is 
 not trivial and depends on clues left behind by conservation\, covariation
  and variation.\n\nI will present three recent advances: (1) a statistical
  covariation test to identify significant covariation over background cova
 riation due to phylogeny\; including a power of covariation calculation to
  identify negative pairs with power (variation) but insignificant covariat
 ion unlikely to form RNA base pairs\; (2) a cascading folding algorithm th
 at combines all positive and negative evolutionary information into comple
 x structures including all types of pseudoknots and triplets. (3) An enhan
 ced covariation statistical test at helix-level resultion that increases s
 ensitivity in the detection of evolutionaryly conserved RNA structure with
 out sacrificing specificity.\n\nThe efficacy of these advances has been te
 sted by predicting to great accuracy the structures of the human noncoding
  RNAs MALAT1 and telomerase RNA\, and by inferring that the data currently
  available do not support a conserved structure for the non-coding RNAs HO
 TAIR and XIST.\n\nI will present further directions to expand and apply th
 ese methods for the systematic identification of novel vertebrate structur
 al RNAs relevant to human biology\, and to create novel algorithms to inco
 rporate the prediction of RNA motifs using deep learning methods. 4:00 pm
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