Introduction

Large-scale genomics and computational approaches have identified thousands of putative long non-coding RNAs (lncRNAs). It has been controversial, however, as to what fraction of these RNAs is truly non-coding. Here, we combine ribosome profiling with a machine-learning approach to validate lncRNAs during zebrafish development in a high throughput manner. We find that dozens of proposed lncRNAs are protein-coding contaminants and that many lncRNAs have ribosome profiles that resemble the 5' leaders of coding RNAs. Analysis of ribosome profiling data from embryonic stem cells reveals similar properties for mammalian lncRNAs. These results clarify the annotation of developmental lncRNAs and suggest a potential role for translation in lncRNA regulation. In addition, our computational pipeline and ribosome profiling data provide a powerful resource for the identification of translated open reading frames during zebrafish development.

Publications

  1. Ribosome profiling reveals resemblance between long non-coding RNAs and 5' leaders of coding RNAs.
    Cite this
    Chew GL, Pauli A, Rinn JL, Regev A, Schier AF, Valen E, 2013-07-01 - Development (Cambridge, England)

Credits

  1. Guo-Liang Chew
    Developer

    Department of Molecular and Cellular Biology, Harvard University, United States of America

  2. Andrea Pauli
    Developer

  3. John L Rinn
    Developer

  4. Aviv Regev
    Developer

  5. Alexander F Schier
    Developer

  6. Eivind Valen
    Investigator

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Summary
AccessionBT001167
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByEivind Valen