Tree-based Position Weight Matrix Approach to Model Transcription Factor Binding Site Profiles from ChIP-Seq Data
Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. However, the current motif finding methods are not designed to process large datasets generated by ChIP-Seq and can’t utilize the huge information provided by it. We develop a Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions.
This page provides a link to download the perl source code that was developed for the Y. Bi study in which the ChIP-Seq data were used to construct Tree-based Position Weight Matrix.
This includes the program
, an example input dataset
(snegative.fa is generated by randomly shuffling
the sequences in splus.fa)and the sample results
Please refer to the provided README
for detailed instruction on how to run TPD.pl
Email :Yingtao Bi, PhD