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Over-represented sequences located on UTRs are potentially involved in regulatory functions

Listed author(s):
  • Kihoon Yoon

    (The University of Texas Health)

  • Daijin Ko

    (The University of Texas at San Antonio)

  • Carolina B. Livi

    (The University of Texas at San Antonio)

  • Nathan Trinklein

    (SwitchGear Genomics)

  • Mark Doderer

    (The University of Texas at San Antonio)

  • Stephen Kwek

    (The University of Texas at San Antonio)

  • Luiz O. F. Penalva

    (The University of Texas Health)

Registered author(s):

    Eukaryotic gene expression must be coordinated for the proper functioning of biological processes. This coordination can be achieved both at the transcriptional and post-transcriptional levels. In both cases, regulatory sequences placed at either promoter regions or on UTRs function as markers recognized by regulators that can then activate or repress different groups of genes according to necessity. While regulatory sequences involved in transcription are quite well documented, there is a lack of information on sequence elements involved in post-transcriptional regulation. We used a statistical over-representation method to identify novel regulatory elements located on UTRs. An exhaustive search approach was used to calculate the frequency of all possible n-mers (short nucleotide sequences) in 16,160 human genes of NCBI RefSeq sequences and to identify any peculiar usage of n-mers on UTRs. After a stringent filtering process, we identified circa 4,000 highly over-represented n-mers on UTRs. We provide evidence that these n-mers are potentially involved in regulatory functions. Identified n-mers overlap with previously identified binding sites for HuR and Tia1 and, AU-rich and GU-rich sequences. We determined also that over-represented n-mers are particularly enriched in a group of 159 genes directly involved in tumor formation. Finally, a method to cluster n-mer groups allowed the identification of putative gene networks.

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    Paper provided by College of Business, University of Texas at San Antonio in its series Working Papers with number 0053.

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    Length: 39 pages
    Date of creation:
    Handle: RePEc:tsa:wpaper:0053
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    1. Leiva, Ricardo, 2007. "Linear discrimination with equicorrelated training vectors," Journal of Multivariate Analysis, Elsevier, vol. 98(2), pages 384-409, February.
    2. Ritter, Gunter & Gallegos, MarĂ­a Teresa, 2002. "Bayesian Object Identification: Variants," Journal of Multivariate Analysis, Elsevier, vol. 81(2), pages 301-334, May.
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