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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.

Show MeSH
Construction of a PBM array design matrix.Both the flanking and interrogating sequences are considered when building the matrix. A column for a constant background component is also included in the matrix.
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pone-0020059-g002: Construction of a PBM array design matrix.Both the flanking and interrogating sequences are considered when building the matrix. A column for a constant background component is also included in the matrix.

Mentions: As the first step of our algorithm, the K-mers present in the probe sequences on a PBM array are represented as a design matrix H, so thatThe design matrix is built in a strand specific manner, so that reverse complement K-mers are considered separately. This allows our model to capture strand specific effects. An extra column of ones is also added to the design matrix in order to account for a constant background in the probe intensities (Figure 2).


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

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

Construction of a PBM array design matrix.Both the flanking and interrogating sequences are considered when building the matrix. A column for a constant background component is also included in the matrix.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020059-g002: Construction of a PBM array design matrix.Both the flanking and interrogating sequences are considered when building the matrix. A column for a constant background component is also included in the matrix.
Mentions: As the first step of our algorithm, the K-mers present in the probe sequences on a PBM array are represented as a design matrix H, so thatThe design matrix is built in a strand specific manner, so that reverse complement K-mers are considered separately. This allows our model to capture strand specific effects. An extra column of ones is also added to the design matrix in order to account for a constant background in the probe intensities (Figure 2).

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