An approach to predict transcription factor DNA binding site specificity based upon gene and transcription factor functional categorization.
BACKGROUND
To understand transcription regulatory mechanisms, it is indispensable to investigate transcription factor (TF) DNA binding preferences. We noted that the generally acknowledged information of functional annotations of TFs as well as that of their target genes should provide useful hints in determining TF DNA binding preferences.


RESULTS
In this contribution, we developed an integrative method based on the Nearest Neighbor Algorithm, to predict DNA binding preferences through integrating both the functional/structural information of TFs and the interaction between TFs and their targets. The accuracy of cross-validation tests on the dataset consisting of 3430 positive samples and 7000 negative samples reaches 87.0% for 10-fold cross-validation and 87.9% for jackknife cross-validation test, which is a much better result than that in our previous work. The prediction result indicates that the improved method we developed could be a powerful approach to infer the TF DNA preference in silico.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Qian
Ziliang
Z
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China.
Lu
Lingyi
L
Liu
XiaoJun
X
Cai
Yu-Dong
YD
Li
Yixue
Y
D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D008969:Molecular Sequence Data; D011485:Protein Binding; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D013329:Structure-Activity Relationship; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics*; D015533:Transcriptional Activation / Q000235:genetics
D014157:Transcription Factors; D004247:DNA