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A linear model for transcription factor binding affinity prediction in protein binding microarrays.

Annala M, Laurila K, Lähdesmäki H, Nykter M - PLoS ONE (2011)

Bottom Line: Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge.For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles.Our approach for TF identification achieved the best performance in the bonus challenge.

View Article: PubMed Central - PubMed

Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland. matti.annala@tut.fi

ABSTRACT
Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge.

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Pearson and Spearman correlations between the predictions of our method (HK→ME) and measured intensities on the ME array.Due to space constraints, results are only shown for the first 20 TFs. Results for all TFs are provided in supplementary Tables S1 and S2.
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pone-0020059-g009: Pearson and Spearman correlations between the predictions of our method (HK→ME) and measured intensities on the ME array.Due to space constraints, results are only shown for the first 20 TFs. Results for all TFs are provided in supplementary Tables S1 and S2.

Mentions: Using preprocessing and quantile normalization for the training samples only, our model was capable of predicting probe intensities on the target array with average Pearson and Spearman correlations of 0.624 and 0.624, across the 86 paired PBM samples. This placed our method as the best performer in the DREAM5 challenge final ranking. Figure 9 shows the correlation between our model's predictions and measured probe intensities for the 20 first paired PBM samples in the DREAM5 dataset. Full listings of our model's prediction accuracies for all 86 samples are available in supplementary Tables S1 and S2, for HK-to-ME and ME-to-HK predictions, respectively.


A linear model for transcription factor binding affinity prediction in protein binding microarrays.

Annala M, Laurila K, Lähdesmäki H, Nykter M - PLoS ONE (2011)

Pearson and Spearman correlations between the predictions of our method (HK→ME) and measured intensities on the ME array.Due to space constraints, results are only shown for the first 20 TFs. Results for all TFs are provided in supplementary Tables S1 and S2.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3102690&req=5

pone-0020059-g009: Pearson and Spearman correlations between the predictions of our method (HK→ME) and measured intensities on the ME array.Due to space constraints, results are only shown for the first 20 TFs. Results for all TFs are provided in supplementary Tables S1 and S2.
Mentions: Using preprocessing and quantile normalization for the training samples only, our model was capable of predicting probe intensities on the target array with average Pearson and Spearman correlations of 0.624 and 0.624, across the 86 paired PBM samples. This placed our method as the best performer in the DREAM5 challenge final ranking. Figure 9 shows the correlation between our model's predictions and measured probe intensities for the 20 first paired PBM samples in the DREAM5 dataset. Full listings of our model's prediction accuracies for all 86 samples are available in supplementary Tables S1 and S2, for HK-to-ME and ME-to-HK predictions, respectively.

Bottom Line: Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge.For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles.Our approach for TF identification achieved the best performance in the bonus challenge.

View Article: PubMed Central - PubMed

Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland. matti.annala@tut.fi

ABSTRACT
Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge.

Show MeSH