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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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Low intensity probe filtering.(a) A filter cutoff point is determined based on the intensity histogram. (b) Two examples of how low intensity filtering successfully removes dark edge artifacts in PBM samples. In both samples pairs, the original sample is on the left, and the filtered sample on the right. Red pixels indicate missing or discarded intensity values.
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pone-0020059-g004: Low intensity probe filtering.(a) A filter cutoff point is determined based on the intensity histogram. (b) Two examples of how low intensity filtering successfully removes dark edge artifacts in PBM samples. In both samples pairs, the original sample is on the left, and the filtered sample on the right. Red pixels indicate missing or discarded intensity values.

Mentions: In the first preprocessing step of Figure 3, we construct a spatial probe intensity map and intensity histogram for each PBM sample. Probes with very low intensities are then discarded using a threshold derived from the intensity histogram. We calculate the threshold by taking the mode of the histogram, and then move toward lower intensity bins until , where is the frequency in bin k, and is the frequency at the mode (Figure 4).


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

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

Low intensity probe filtering.(a) A filter cutoff point is determined based on the intensity histogram. (b) Two examples of how low intensity filtering successfully removes dark edge artifacts in PBM samples. In both samples pairs, the original sample is on the left, and the filtered sample on the right. Red pixels indicate missing or discarded intensity values.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020059-g004: Low intensity probe filtering.(a) A filter cutoff point is determined based on the intensity histogram. (b) Two examples of how low intensity filtering successfully removes dark edge artifacts in PBM samples. In both samples pairs, the original sample is on the left, and the filtered sample on the right. Red pixels indicate missing or discarded intensity values.
Mentions: In the first preprocessing step of Figure 3, we construct a spatial probe intensity map and intensity histogram for each PBM sample. Probes with very low intensities are then discarded using a threshold derived from the intensity histogram. We calculate the threshold by taking the mode of the histogram, and then move toward lower intensity bins until , where is the frequency in bin k, and is the frequency at the mode (Figure 4).

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