Estimating binding properties of transcription factors from genome-wide binding profiles

TL;DR

This paper presents an analytical model to predict transcription factor occupancy on DNA using genomic data, and applies it to infer binding characteristics of five Drosophila TFs during development.

Contribution

It introduces a new model integrating sequence preferences, DNA accessibility, and TF copy number to estimate binding occupancy from ChIP-seq data.

Findings

TFs have thousands of molecules specifically bound to DNA

Bicoid and Caudal show higher specificity than others

Many TF molecules are not bound specifically to DNA

Abstract

The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that, while Bicoid and Caudal display a higher specificity, the other three transcription factors (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA.