Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

Abstract

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human. Standard analysis of the genes whose regulatory DNA is bound by a TF, assayed by ChIP-chip/seq, and the genes that respond to a perturbation of that TF, shows that these two data sources rarely converge on a common set of direct, functional targets. Even taking the few genes that are both bound and responsive as direct functional targets is not safe -- when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce Dual Threshold Optimization, a new method for setting significance thresholds on binding and response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that has been processed by network inference algorithms, which further improves convergence. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards or ChIP-exo improve the network synergistically.