Literature DB >> 12495153

A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

Mei-Ling Ting Lee1, Martha L Bulyk, G A Whitmore, George M Church.   

Abstract

There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.

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Year:  2002        PMID: 12495153     DOI: 10.1111/j.0006-341x.2002.00981.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  18 in total

1.  Additivity in protein-DNA interactions: how good an approximation is it?

Authors:  Panayiotis V Benos; Martha L Bulyk; Gary D Stormo
Journal:  Nucleic Acids Res       Date:  2002-10-15       Impact factor: 16.971

2.  A motif co-occurrence approach for genome-wide prediction of transcription-factor-binding sites in Escherichia coli.

Authors:  Martha L Bulyk; Abigail M McGuire; Nobuhisa Masuda; George M Church
Journal:  Genome Res       Date:  2004-02       Impact factor: 9.043

3.  A non-parametric model for transcription factor binding sites.

Authors:  Oliver D King; Frederick P Roth
Journal:  Nucleic Acids Res       Date:  2003-10-01       Impact factor: 16.971

4.  Quantitative high-throughput analysis of transcription factor binding specificities.

Authors:  Jane Linnell; Richard Mott; Simon Field; Dominic P Kwiatkowski; Jiannis Ragoussis; Irina A Udalova
Journal:  Nucleic Acids Res       Date:  2004-02-27       Impact factor: 16.971

Review 5.  Determining the specificity of protein-DNA interactions.

Authors:  Gary D Stormo; Yue Zhao
Journal:  Nat Rev Genet       Date:  2010-09-28       Impact factor: 53.242

Review 6.  Protein binding microarrays for the characterization of DNA-protein interactions.

Authors:  Martha L Bulyk
Journal:  Adv Biochem Eng Biotechnol       Date:  2007       Impact factor: 2.635

7.  Maximally efficient modeling of DNA sequence motifs at all levels of complexity.

Authors:  Gary D Stormo
Journal:  Genetics       Date:  2011-02-07       Impact factor: 4.562

8.  Diversity and complexity in DNA recognition by transcription factors.

Authors:  Gwenael Badis; Michael F Berger; Anthony A Philippakis; Shaheynoor Talukder; Andrew R Gehrke; Savina A Jaeger; Esther T Chan; Genita Metzler; Anastasia Vedenko; Xiaoyu Chen; Hanna Kuznetsov; Chi-Fong Wang; David Coburn; Daniel E Newburger; Quaid Morris; Timothy R Hughes; Martha L Bulyk
Journal:  Science       Date:  2009-05-14       Impact factor: 47.728

9.  Inferring binding energies from selected binding sites.

Authors:  Yue Zhao; David Granas; Gary D Stormo
Journal:  PLoS Comput Biol       Date:  2009-12-04       Impact factor: 4.475

10.  Modeling the quantitative specificity of DNA-binding proteins from example binding sites.

Authors:  Dana S F Homsi; Vineet Gupta; Gary D Stormo
Journal:  PLoS One       Date:  2009-08-25       Impact factor: 3.240

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