Modeling and automation of sequencing-based characterization of RNA structure

Sharon Aviran, Cole Trapnell, Julius B. Lucks, Stefanie A. Mortimer, Shujun Luo, Gary P. Schroth, Jennifer A. Doudna*, Adam P. Arkin, Lior Pachter

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Sequence census methods reduce molecular measurements such as transcript abundance and protein-nucleic acid interactions to counting problems via DNA sequencing. We focus on a novel assay utilizing this approach, called selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), that can be used to characterize RNA secondary and tertiary structure. We describe a fully automated data analysis pipeline for SHAPE-Seq analysis that includes read processing, mapping, and structural inference based on a model of the experiment. Our methods rely on the solution of a series of convex optimization problems for which we develop efficient and effective numerical algorithms. Our results can be easily extended to other chemical probes of RNA structure, and also generalized to modeling polymerase drop-off in other sequence census-based experiments.

Original languageEnglish (US)
Pages (from-to)11069-11074
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number27
StatePublished - Jul 5 2011


  • Chemical mapping
  • Next generation sequencing
  • RNA folding
  • RNA sequencing
  • Signal processing

ASJC Scopus subject areas

  • General


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