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Regression based predictor for p53 transactivation.

Gowrisankar S, Jegga AG - BMC Bioinformatics (2009)

Bottom Line: The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs).Experimentally validated known p53-REs along with their transactivation capabilities are used for training.Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel in-silico regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. sgowrisankar@partners.org

ABSTRACT

Background: The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs). Traditional profile scores for p53 based on position weight matrices (PWM) are only a weak indicator of binding affinity because the level of binding also depends on various other factors such as interaction between the nucleotides and, in case of p53-REs, the extent of the spacer between the dimers.

Results: In the current study we introduce a novel in-silico predictor for p53-RE transactivation capability based on a combination of multidimensional scaling and multinomial logistic regression. Experimentally validated known p53-REs along with their transactivation capabilities are used for training. Through cross-validation studies we show that our method outperforms other existing methods. To demonstrate the utility of this method we (a) rank putative p53-REs of target genes and target microRNAs based on the predicted transactivation capability and (b) study the implication of polymorphisms overlapping p53-RE on its transactivation capability.

Conclusion: Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel in-silico regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.

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Graph showing the variance of the model captured with respect to the number of input dimensions (Eigen values). At 50 dimensions, 90% of the variance or complexity of the model is captured.
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Figure 1: Graph showing the variance of the model captured with respect to the number of input dimensions (Eigen values). At 50 dimensions, 90% of the variance or complexity of the model is captured.

Mentions: We used multidimensional scaling [23] to project these 263 sequences onto a multidimensional Euclidean space such that the distance between any two sequences was approximately equal to their dissimilarity. We were able to transform these sequences into a 116-dimensional subspace. Though 90% of the variance in the data could be captured by just 50 dimensions, we decided to retain all the 116 dimensions for accuracy and also because these dimensions would be automatically obtained for a novel p53-RE. It is therefore reasonable to conclude that 50 dimensions capture the complex nucleotide interactions that are ignored by earlier additive models. Figure 1 shows the percentage of variance captured as a function of number of dimensions (see methods for calculating variance from number of dimensions). In addition to the Euclidean space dimensions, we also obtained the spacer associated with each 20-mer p53-RE in the training set. On the whole, we used 116 (Dimensions) + 1 (spacer) = 117 features as input to the regression analysis.


Regression based predictor for p53 transactivation.

Gowrisankar S, Jegga AG - BMC Bioinformatics (2009)

Graph showing the variance of the model captured with respect to the number of input dimensions (Eigen values). At 50 dimensions, 90% of the variance or complexity of the model is captured.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2719629&req=5

Figure 1: Graph showing the variance of the model captured with respect to the number of input dimensions (Eigen values). At 50 dimensions, 90% of the variance or complexity of the model is captured.
Mentions: We used multidimensional scaling [23] to project these 263 sequences onto a multidimensional Euclidean space such that the distance between any two sequences was approximately equal to their dissimilarity. We were able to transform these sequences into a 116-dimensional subspace. Though 90% of the variance in the data could be captured by just 50 dimensions, we decided to retain all the 116 dimensions for accuracy and also because these dimensions would be automatically obtained for a novel p53-RE. It is therefore reasonable to conclude that 50 dimensions capture the complex nucleotide interactions that are ignored by earlier additive models. Figure 1 shows the percentage of variance captured as a function of number of dimensions (see methods for calculating variance from number of dimensions). In addition to the Euclidean space dimensions, we also obtained the spacer associated with each 20-mer p53-RE in the training set. On the whole, we used 116 (Dimensions) + 1 (spacer) = 117 features as input to the regression analysis.

Bottom Line: The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs).Experimentally validated known p53-REs along with their transactivation capabilities are used for training.Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel in-silico regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. sgowrisankar@partners.org

ABSTRACT

Background: The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs). Traditional profile scores for p53 based on position weight matrices (PWM) are only a weak indicator of binding affinity because the level of binding also depends on various other factors such as interaction between the nucleotides and, in case of p53-REs, the extent of the spacer between the dimers.

Results: In the current study we introduce a novel in-silico predictor for p53-RE transactivation capability based on a combination of multidimensional scaling and multinomial logistic regression. Experimentally validated known p53-REs along with their transactivation capabilities are used for training. Through cross-validation studies we show that our method outperforms other existing methods. To demonstrate the utility of this method we (a) rank putative p53-REs of target genes and target microRNAs based on the predicted transactivation capability and (b) study the implication of polymorphisms overlapping p53-RE on its transactivation capability.

Conclusion: Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel in-silico regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.

Show MeSH
Related in: MedlinePlus