MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.
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A number of computational approaches have been developed to examine these interactions, including simple mononucleotide and dinucleotide position weight matrix models.Here we develop a novel, unbiased computational algorithm, MARZ, that systematically analyzes all possible gapped matrices across a fixed number of nucleotides.Our results indicate that in many cases gapped matrix models can outperform traditional models, but that the relative strength of the binding sites considered in the analysis can profoundly influence the predictive ability of specific models.
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PubMed Central - PubMed
Affiliation: Department of Computer Science, Harvey Mudd College, 301 Platt Boulevard, Claremont CA, 91711, USA. rzellers@hmc.edu.
ABSTRACT
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Background: A key challenge in understanding the molecular mechanisms that control gene regulation is the characterization of the specificity with which transcription factor proteins bind to specific DNA sequences. A number of computational approaches have been developed to examine these interactions, including simple mononucleotide and dinucleotide position weight matrix models. Results: Here we develop a novel, unbiased computational algorithm, MARZ, that systematically analyzes all possible gapped matrices across a fixed number of nucleotides. In addition, to evaluate the ability of these matrix models to predict in vivo binding sites, we utilize a new scoring system and, in combination with established scoring methods and statistical analysis, test the performance of 32 different gapped matrices on the well characterized HUNCHBACK transcription factor in Drosophila. Conclusions: Our results indicate that in many cases gapped matrix models can outperform traditional models, but that the relative strength of the binding sites considered in the analysis can profoundly influence the predictive ability of specific models. Related in: MedlinePlus |
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Mentions: First, convert the type ID to binary. Then convert the binary representation to a string of k’s and m’s representing the nucleotides considered in that particular model by replacing each zero with a k and each one with an m. Any leading k’s are omitted, as the leading nucleotide must be included, and an m is inserted on the right hand side, as the terminal nucleotide is always included. Table 1 lists all of the type IDs and Figure 2 gives a graphical illustration of the matrix construction and sequence interpretation for the mononucleotide model m and the more complex gapped n-mer model mkkkkm.Figure 2 |
View Article: PubMed Central - PubMed
Affiliation: Department of Computer Science, Harvey Mudd College, 301 Platt Boulevard, Claremont CA, 91711, USA. rzellers@hmc.edu.
Background: A key challenge in understanding the molecular mechanisms that control gene regulation is the characterization of the specificity with which transcription factor proteins bind to specific DNA sequences. A number of computational approaches have been developed to examine these interactions, including simple mononucleotide and dinucleotide position weight matrix models.
Results: Here we develop a novel, unbiased computational algorithm, MARZ, that systematically analyzes all possible gapped matrices across a fixed number of nucleotides. In addition, to evaluate the ability of these matrix models to predict in vivo binding sites, we utilize a new scoring system and, in combination with established scoring methods and statistical analysis, test the performance of 32 different gapped matrices on the well characterized HUNCHBACK transcription factor in Drosophila.
Conclusions: Our results indicate that in many cases gapped matrix models can outperform traditional models, but that the relative strength of the binding sites considered in the analysis can profoundly influence the predictive ability of specific models.