A hybrid model for robust detection of transcription factor binding sites

Bioinformatics
Sumedha Gunewardena, Zhaolei Zhang

Abstract

The short and degenerate nature of transcription factor (TF) binding sites contributes towards a low signal to noise ratio making it very difficult to separate them from their background. In order to tackle this problem one needs to look at ways of capturing the underlying biophysical properties that best discriminates TF binding sites from their background DNA. One such discriminatory property lies in the observed compositional differences in the nucleotide levels of TF binding sites and background DNA which are a result of processes such as purifying selection and selective preferences of TF binding sites for particular nucleotides or a combination of nucleotides over others. In this article, we present a hybrid model, referred to as a MonoDi-nucleotide model for robustly detecting TF binding sites. It incorporates both mono- and dinucleotide statistics to optimally partition the base positions of an aligned set of TF binding sites (motif) into a non-redundant sequence of mono and/or dinucleotide segments that maximizes the odds ratio of the binding sites relative to their background DNA. We tested the MonoDi-nucleotide model on the benchmark dataset compiled by Tompa et al. (2005) for assessing computational tools that predi...Continue Reading

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Jun 10, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Sumedha GunewardenaZhaolei Zhang

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Citations

Aug 10, 2010·Nucleic Acids Research·Julia Yue CuiCurtis D Klaassen
Jul 3, 2013·Nucleic Acids Research·Ka-Chun WongZhaolei Zhang
Nov 26, 2009·Genome Biology·Deniz KuralJeffrey H Chuang
Mar 26, 2013·PLoS Computational Biology·Jan GrauJens Boch
Jul 10, 2009·Yi chuan = Hereditas·Lin HouMing-Hua Deng
Sep 9, 2011·The British Journal of Sociology·Lee M MillerAlessandro Bonanno

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