IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0174052.html
   My bibliography  Save this article

Discriminating between HuR and TTP binding sites using the k-spectrum kernel method

Author

Listed:
  • Shweta Bhandare
  • Debra S Goldberg
  • Robin Dowell

Abstract

Background: The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an interesting problem. Machine learning approaches, such as support vector machines (SVMs), may be useful in the identification of discriminative features. However, this method has yet to be applied to studies of RNA binding protein motifs. Results: Applying the k-spectrum kernel to a support vector machine (SVM), we first verified the published binding sites of both HuR and TTP. Additional feature engineering highlighted the U-rich binding preference of HuR and AU-rich binding preference for TTP. Domain adaptation along with multi-task learning was used to predict the common binding sites. Conclusion: The distinction between HuR and TTP binding appears to be subtle content features. HuR prefers strongly U-rich sequences whereas TTP prefers AU-rich as with increasing A content, the sequences are more likely to be bound only by TTP. Our model is consistent with competitive binding of the two proteins, particularly at intermediate AU-balanced sequences. This suggests that fine changes in the A/U balance within a untranslated region (UTR) can alter the binding and subsequent stability of the message. Both feature engineering and domain adaptation emphasized the extent to which these proteins recognize similar general sequence features. This work suggests that the k-spectrum kernel method could be useful when studying RNA binding proteins and domain adaptation techniques such as feature augmentation could be employed particularly when examining RBPs with similar binding preferences.

Suggested Citation

  • Shweta Bhandare & Debra S Goldberg & Robin Dowell, 2017. "Discriminating between HuR and TTP binding sites using the k-spectrum kernel method," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0174052
    DOI: 10.1371/journal.pone.0174052
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174052
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0174052&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0174052?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. Asa Ben-Hur & Cheng Soon Ong & Sören Sonnenburg & Bernhard Schölkopf & Gunnar Rätsch, 2008. "Support Vector Machines and Kernels for Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    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. Alaa Tharwat & Aboul Ella Hassanien, 2019. "Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 576-598, October.
    2. Emily S W Wong & Margaret C Hardy & David Wood & Timothy Bailey & Glenn F King, 2013. "SVM-Based Prediction of Propeptide Cleavage Sites in Spider Toxins Identifies Toxin Innovation in an Australian Tarantula," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
    3. Lior Shamir & John D Delaney & Nikita Orlov & D Mark Eckley & Ilya G Goldberg, 2010. "Pattern Recognition Software and Techniques for Biological Image Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-10, November.
    4. Kay H Brodersen & Thomas M Schofield & Alexander P Leff & Cheng Soon Ong & Ekaterina I Lomakina & Joachim M Buhmann & Klaas E Stephan, 2011. "Generative Embedding for Model-Based Classification of fMRI Data," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-19, June.
    5. Wei Shui & Yiyi Zhang & Xinggui Wang & Yuanmeng Liu & Qianfeng Wang & Fei Duan & Chaowei Wu & Wanyu Shui, 2022. "Does Tibetan Household Livelihood Capital Enhance Tourism Participation Sustainability? Evidence from China’s Jiaju Tibetan Village," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
    6. Marina M -C Vidovic & Nico Görnitz & Klaus-Robert Müller & Gunnar Rätsch & Marius Kloft, 2015. "SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-23, December.
    7. Emili Balaguer-Ballester & Christopher C Lapish & Jeremy K Seamans & Daniel Durstewitz, 2011. "Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-19, May.
    8. A Ivanenko & P Watkins & M A J van Gerven & K Hammerschmidt & B Englitz, 2020. "Classifying sex and strain from mouse ultrasonic vocalizations using deep learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    9. Yue Deng & Yanyu Zhao & Yebin Liu & Qionghai Dai, 2013. "Differences Help Recognition: A Probabilistic Interpretation," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-10, June.
    10. Charlotte Soneson & Sarah Gerster & Mauro Delorenzi, 2014. "Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
    11. Igor O Korolev & Laura L Symonds & Andrea C Bozoki & Alzheimer's Disease Neuroimaging Initiative, 2016. "Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-25, February.
    12. Stephen J Gilmore, 2018. "Automated decision support in melanocytic lesion management," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    13. Juan A G Ranea & Ian Morilla & Jon G Lees & Adam J Reid & Corin Yeats & Andrew B Clegg & Francisca Sanchez-Jimenez & Christine Orengo, 2010. "Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-14, September.
    14. S. Camelo & M. González-Lima & A. Quiroz, 2015. "Nearest neighbors methods for support vector machines," Annals of Operations Research, Springer, vol. 235(1), pages 85-101, December.
    15. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.

    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:pone00:0174052. 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.