IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v515y2014i7525d10.1038_nature13802.html
   My bibliography  Save this article

Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells

Author

Listed:
  • Thomas M. Carlile

    (Massachusetts Institute of Technology)

  • Maria F. Rojas-Duran

    (Massachusetts Institute of Technology)

  • Boris Zinshteyn

    (Massachusetts Institute of Technology)

  • Hakyung Shin

    (Massachusetts Institute of Technology)

  • Kristen M. Bartoli

    (Massachusetts Institute of Technology)

  • Wendy V. Gilbert

    (Massachusetts Institute of Technology)

Abstract

The modification of uridine to pseudouridine is widespread in transfer and ribosomal RNAs but not observed so far in a coding RNA; here a new technique is used to detect this modification on a genome-wide scale, leading to the identification of pseudouridylation in messenger RNAs as well as almost 100 new sites in non-coding RNAs.

Suggested Citation

  • Thomas M. Carlile & Maria F. Rojas-Duran & Boris Zinshteyn & Hakyung Shin & Kristen M. Bartoli & Wendy V. Gilbert, 2014. "Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells," Nature, Nature, vol. 515(7525), pages 143-146, November.
  • Handle: RePEc:nat:nature:v:515:y:2014:i:7525:d:10.1038_nature13802
    DOI: 10.1038/nature13802
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature13802
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/nature13802?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abu Zahid Bin Aziz & Md Al Mehedi Hasan & Jungpil Shin, 2021. "Identification of RNA pseudouridine sites using deep learning approaches," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-13, February.
    2. Elzbieta Kierzek & Xiaoju Zhang & Richard M. Watson & Scott D. Kennedy & Marta Szabat & Ryszard Kierzek & David H. Mathews, 2022. "Secondary structure prediction for RNA sequences including N6-methyladenosine," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:515:y:2014:i:7525:d:10.1038_nature13802. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.