Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99457
Title: Finding transcription factor binding motifs for coregulated genes by combining sequence overrepresentation with cross-species conservation
Authors: Jia, Hui.
Li, Jinming.
Keywords: DRNTU::Science::Biological sciences
Issue Date: 2012
Source: Jia, H., & Li, J. (2012). Finding Transcription Factor Binding Motifs for Coregulated Genes by Combining Sequence Overrepresentation with Cross-Species Conservation. Journal of Probability and Statistics, 2012,1-18.
Series/Report no.: Journal of probability and statistics
Abstract: Novel computational methods for finding transcription factor binding motifs have long been sought due to tedious work of experimentally identifying them. However, the current prevailing methods yield a large number of false positive predictions due to the short, variable nature of transcriptional factor binding sites (TFBSs). We proposed here a method that combines sequence overrepresentation and cross-species sequence conservation to detect TFBSs in upstream regions of a given set of coregulated genes. We applied the method to 35 S. cerevisiae transcriptional factors with known DNA binding motifs (with the support of orthologous sequences from genomes of S. mikatae, S. bayanus, and S. paradoxus), and the proposed method outperformed the single-genome-based motif finding methods MEME and AlignACE as well as the multiple-genome-based methods PHYME and Footprinter for the majority of these transcriptional factors. Compared with the prevailing motif finding software, our method has some advantages in finding transcriptional factor binding motifs for potential coregulated genes if the gene upstream sequences of multiple closely related species are available. Although we used yeast genomes to assess our method in this study, it might also be applied to other organisms if suitable related species are available and the upstream sequences of coregulated genes can be obtained for the multiple closely related species.
URI: https://hdl.handle.net/10356/99457
http://hdl.handle.net/10220/17264
DOI: http://dx.doi.org/10.1155/2012/830575
Rights: © 2012 The Authors. This paper was published in Journal of Probability and Statistics and is made available as an electronic reprint (preprint) with permission of the authors. The paper can be found at the following official DOI: [http://dx.doi.org/10.1155/2012/830575]. 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:SBS Journal Articles

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