Prediction of protein-DNA interactions of transcription factors linking proteomics and transcriptomics data

Abstract

We compared positional weight matrix-based prediction methods for transcription factor (TF) binding sites using selected fraction of ChIP-seq data with the help of partial AUC measure (limited to false positive rate 0.1, that is the most relevant for the application of the TF search in the genome scale). Comparison of three prediction methods—additive, multiplicative and information-vector based (MATCH) showed an advantage of the MATCH method for majority of transcription factors tested. We demonstrated that application of TF site identifying methods can help to connect the proteomics and phosphoproteomics world of signaling networks to gene regulation and transcriptomics world.