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|Title:||A statistical thin-tail test of predicting regulatory regions in the Drosophila genome||Authors:||Shu, Jian Jun
|Keywords:||DRNTU::Science::Biological sciences||Issue Date:||2013||Source:||Shu, J. J., & Li, Y. (2013). A statistical thin-tail test of predicting regulatory regions in the Drosophila genome. Theoretical Biology and Medical Modelling, 10(1):11.||Series/Report no.:||Theoretical biology and medical modelling||Abstract:||The identification of transcription factor binding sites (TFBSs) and cis-regulatory modules (CRMs) is a crucial step in studying gene expression, but the computational method attempting to distinguish CRMs from NCNRs still remains a challenging problem due to the limited knowledge of specific interactions involved. Methods The statistical properties of cis-regulatory modules (CRMs) are explored by estimating the similar-word set distribution with overrepresentation (Z-score). It is observed that CRMs tend to have a thin-tail Z-score distribution. A new statistical thin-tail test with two thinness coefficients is proposed to distinguish CRMs from non-coding non-regulatory regions (NCNRs). Results As compared with the existing fluffy-tail test, the first thinness coefficient is designed to reduce computational time, making the novel thin-tail test very suitable for long sequences and large database analysis in the post-genome time and the second one to improve the separation accuracy between CRMs and NCNRs. These two thinness coefficients may serve as valuable filtering indexes to predict CRMs experimentally. Conclusions The novel thin-tail test provides an efficient and effective means for distinguishing CRMs from NCNRs based on the specific statistical properties of CRMs and can guide future experiments aimed at finding new CRMs in the post-genome time.||URI:||https://hdl.handle.net/10356/96947
|ISSN:||1742-4682||DOI:||http://dx.doi.org/10.1186/1742-4682-10-11||Rights:||© 2013 The Author(s); licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper was published in Theoretical Biology and Medical Modelling and is made available as an electronic reprint (preprint) with permission of The Author(s). The paper can be found at the following official DOI: [http://dx.doi.org/10.1186/1742-4682-10-11]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Journal Articles|
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