IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0013876.html
   My bibliography  Save this article

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Author

Listed:
  • Adrian Schröder
  • Johannes Eichner
  • Jochen Supper
  • Jonas Eichner
  • Dierk Wanke
  • Carsten Henneges
  • Andreas Zell

Abstract

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.

Suggested Citation

  • Adrian Schröder & Johannes Eichner & Jochen Supper & Jonas Eichner & Dierk Wanke & Carsten Henneges & Andreas Zell, 2010. "Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-15, November.
  • Handle: RePEc:plo:pone00:0013876
    DOI: 10.1371/journal.pone.0013876
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0013876
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0013876&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0013876?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
    ---><---

    References listed on IDEAS

    as
    1. Barbara E Engelhardt & Michael I Jordan & Kathryn E Muratore & Steven E Brenner, 2005. "Protein Molecular Function Prediction by Bayesian Phylogenomics," PLOS Computational Biology, Public Library of Science, vol. 1(5), pages 1-1, October.
    2. Shaun Mahony & Philip E Auron & Panayiotis V Benos, 2007. "DNA Familial Binding Profiles Made Easy: Comparison of Various Motif Alignment and Clustering Strategies," PLOS Computational Biology, Public Library of Science, vol. 3(3), pages 1-14, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew K Miller & Cristin G Print & Poul M F Nielsen & Edmund J Crampin, 2010. "A Bayesian Search for Transcriptional Motifs," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-7, November.
    2. Nils Weinhold & Oliver Sander & Francisco S Domingues & Thomas Lengauer & Ingolf Sommer, 2008. "Local Function Conservation in Sequence and Structure Space," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-13, July.
    3. Jianzhu Ma & Sheng Wang & Zhiyong Wang & Jinbo Xu, 2014. "MRFalign: Protein Homology Detection through Alignment of Markov Random Fields," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-12, March.
    4. David L Corcoran & Kusum V Pandit & Ben Gordon & Arindam Bhattacharjee & Naftali Kaminski & Panayiotis V Benos, 2009. "Features of Mammalian microRNA Promoters Emerge from Polymerase II Chromatin Immunoprecipitation Data," PLOS ONE, Public Library of Science, vol. 4(4), pages 1-10, April.
    5. Shaoqiang Zhang & Yong Chen, 2016. "CLIMP: Clustering Motifs via Maximal Cliques with Parallel Computing Design," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
    6. David K Crockett & Stephen R Piccolo & Perry G Ridge & Rebecca L Margraf & Elaine Lyon & Marc S Williams & Joyce A Mitchell, 2011. "Predicting Phenotypic Severity of Uncertain Gene Variants in the RET Proto-Oncogene," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-7, March.
    7. Duncan P Brown & Nandini Krishnamurthy & Kimmen Sjölander, 2007. "Automated Protein Subfamily Identification and Classification," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-13, August.

    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:plo:pone00:0013876. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.