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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2005: Research in Computational Molecular Biology pp 133–151Cite as

A High-Throughput Approach for Associating microRNAs with Their Activity Conditions

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3500))

Abstract

Plant microRNAs (miRNAs) are short RNA sequences that bind to target mRNAs and change their expression levels by influencing their stabilities and marking them for cleavage. We present a high throughput approach for associating between microRNAs and conditions in which they act, using novel statistical and algorithmic measures. Our new prototype tool, miRNAXpress, computes a (binary) matrix T denoting the potential targets of microRNAs. Then, using T and an additional predefined matrix X indicating expression of genes under various conditions, it produces a new matrix that predicts associations between microRNAs and the conditions in which they act.

The computational intensive part of miRNAXpress is the calculation of T. We provide a hybridization search algorithm which given a query microRNA, a text mRNA, and a predefined energy cutoff threshold, finds and reports all targets (putative binding sites) of the query in the text with binding energy below the predefined threshold. In order to speed it up, we utilize the sparsity of the search space without sacrificing the optimality of the results. Consequently, the time complexity of the search algorithm is almost linear in the size of a sparse set of locations where base-pairs are stacked at a height of three or more.

We employed our tool to conduct a study, using the plant Arabidopsis thaliana as our model organism. By applying miRNAXpress to 98 microRNAs and 380 conditions, some biologically interesting and statistically strong relations were discovered.

Further details, including figures and pseudo-code, can be found at: http://www.cs.technion.ac.il/~michalz/LinearRNA.ps

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Technion – Israel Institute of Technology, Haifa, 32000, Israel

    Chaya Ben-Zaken Zilberstein, Michal Ziv-Ukelson, Ron Y. Pinter & Zohar Yakhini

  2. Agilent Technologies, Tel Aviv, Israel

    Zohar Yakhini

Authors
  1. Chaya Ben-Zaken Zilberstein
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  2. Michal Ziv-Ukelson
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  3. Ron Y. Pinter
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  4. Zohar Yakhini
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Editor information

Editors and Affiliations

  1. Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Satoru Miyano

  2. Broad Institute of MIT and Harvard, 320 Charles Street, 02141-2023, Cambridge, MA, USA

    Jill Mesirov

  3. Computational Genomics Laboratory, Department of Bioengineering, Boston University, 44 Cummington St., 02215, Boston, MA, USA

    Simon Kasif

  4. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  5. University of California, San Diego, USA

    Pavel A. Pevzner

  6. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

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© 2005 Springer-Verlag Berlin Heidelberg

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Zilberstein, C.BZ., Ziv-Ukelson, M., Pinter, R.Y., Yakhini, Z. (2005). A High-Throughput Approach for Associating microRNAs with Their Activity Conditions. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2005. Lecture Notes in Computer Science(), vol 3500. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11415770_11

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  • DOI: https://doi.org/10.1007/11415770_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25866-7

  • Online ISBN: 978-3-540-31950-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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