Novel data fusion method and exploration of multiple information sources for transcription factor target gene prediction

Background: Revealing protein-DNA interactions is a key problem in understanding transcriptional regulation at mechanistic level. Computational methods have an important role in predicting transcription factor target gene genomewide. Multiple data fusion provides a natural way to improve transcription factor target gene predictions because sequence specificities alone are not sufficient to accurately predict transcription factor binding sites. Methods: Here we develop a new data fusion method to combine multiple genome-level data sources and study the extent to which DNA duplex stability and nucleosome positioning information, either alone or in combination with other data sources, can improve the prediction of transcription factor target gene. Results. Results on a carefully constructed test set of verified binding sites in mouse genome demonstrate that our new multiple data fusion method can reduce false positive rates, and that DNA duplex stability and nucleosome occupation data can improve the accuracy of transcription factor target gene predictions, especially when combined with other genome-level data sources. Cross-validation and other randomization tests confirm the predictive performance of our method. Our results also show that nonredundant data sources provide the most efficient data fusion.