Beyond position weight matrices: nucleotide correlations in transcription factor binding sites and their description

TL;DR

This paper demonstrates that incorporating pairwise correlations into models of transcription factor binding sites improves their accuracy over traditional independent models, revealing significant interdependence between DNA bases in vivo.

Contribution

It introduces a pairwise interaction model based on maximum entropy principles that captures nucleotide correlations in TFBSs, outperforming PWM-based approaches.

Findings

Independent models do not reproduce observed TFBS statistics.

Pairwise interaction models improve TFBS prediction accuracy.

Most significant interactions are between consecutive base pairs.

Abstract

The identification of transcription factor binding sites (TFBSs) on genomic DNA is of crucial importance for understanding and predicting regulatory elements in gene networks. TFBS motifs are commonly described by Position Weight Matrices (PWMs), in which each DNA base pair independently contributes to the transcription factor (TF) binding, despite mounting evidence of interdependence between base pairs positions. The recent availability of genome-wide data on TF-bound DNA regions offers the possibility to revisit this question in detail for TF binding {\em in vivo}. Here, we use available fly and mouse ChIPseq data, and show that the independent model generally does not reproduce the observed statistics of TFBS, generalizing previous observations. We further show that TFBS description and predictability can be systematically improved by taking into account pairwise correlations in the TFBS via the principle of maximum entropy. The resulting pairwise interaction model is formally equivalent to the disordered Potts models of statistical mechanics and it generalizes previous approaches to interdependent positions. Its structure allows for co-variation of two or more base pairs, as well as secondary motifs. Although models consisting of mixtures of PWMs also have this last feature, we show that pairwise interaction models outperform them. The significant pairwise interactions are found to be sparse and found dominantly between consecutive base pairs. Finally, the use of a pairwise interaction model for the identification of TFBSs is shown to give significantly different predictions than a model based on independent positions.