PATTERNA: Transcriptome-wide search for functional RNA elements via structural data signatures

Mirko Ledda, Sharon Aviran

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number28
JournalGenome Biology
Volume19
Issue number1
DOIs
StatePublished - Mar 1 2018

Keywords

  • Functional elements
  • GMM-HMM
  • Machine learning
  • PARS
  • Pattern recognition
  • RNA structure
  • RNA structure-function
  • SHAPE
  • Structure probing
  • Transcriptome-wide profiling

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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