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MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.

Zellers RG, Drewell RA, Dresch JM - BMC Bioinformatics (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Harvey Mudd College, 301 Platt Boulevard, Claremont CA, 91711, USA. rzellers@hmc.edu.

ABSTRACT

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.

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Related in: MedlinePlus

Complete Flowchart of the MARZ algorithm. The flowchart illustrates the MARZ algorithm during implementation. The figure should be read from top to bottom, as the three boxes at the top (orange) illustrate the three inputs to the MARZ algorithm and the large box at the bottom (green) illustrates the values the MARZ algorithm outputs.
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Fig1: Complete Flowchart of the MARZ algorithm. The flowchart illustrates the MARZ algorithm during implementation. The figure should be read from top to bottom, as the three boxes at the top (orange) illustrate the three inputs to the MARZ algorithm and the large box at the bottom (green) illustrates the values the MARZ algorithm outputs.

Mentions: The complete MARZ algorithm is illustrated in the flow chart shown in Figure 1. What follows is a detailed description of each individual component of the algorithm.Figure 1


MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.

Zellers RG, Drewell RA, Dresch JM - BMC Bioinformatics (2015)

Complete Flowchart of the MARZ algorithm. The flowchart illustrates the MARZ algorithm during implementation. The figure should be read from top to bottom, as the three boxes at the top (orange) illustrate the three inputs to the MARZ algorithm and the large box at the bottom (green) illustrates the values the MARZ algorithm outputs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4384306&req=5

Fig1: Complete Flowchart of the MARZ algorithm. The flowchart illustrates the MARZ algorithm during implementation. The figure should be read from top to bottom, as the three boxes at the top (orange) illustrate the three inputs to the MARZ algorithm and the large box at the bottom (green) illustrates the values the MARZ algorithm outputs.
Mentions: The complete MARZ algorithm is illustrated in the flow chart shown in Figure 1. What follows is a detailed description of each individual component of the algorithm.Figure 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Harvey Mudd College, 301 Platt Boulevard, Claremont CA, 91711, USA. rzellers@hmc.edu.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus