a linear model for transcription factor binding affinity prediction in protein binding microarrays一个线性模型预测转录因子结合亲和力的蛋白质绑定微阵列.pdfVIP

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays 1 1 ¨ ¨ 2,3 1,3 Matti Annala *, Kirsti Laurila , Harri Lahdesmaki , Matti Nykter 1 Department of Signal Processing, Tampere University of Technology, Tampere, Finland, 2 Department of Information and Computer Science, Aalto University School of Science and Technology, Helsinki, Finland, 3 Turku Centre for Biotechnology, Turku University, Turku, Finland 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. ¨ ¨ Citation: Annala M, Laurila K, Lahdesmaki H, Nykter M (2011) A Linear Model for Transcription Factor Binding A