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26 Background: A transcription-factor (TF) network map indicates the direct, functional targets of 27 each TF --the genes it regulates by binding to their cis-regulatory DNA. Data on the genomic 28 binding locations of each TF and the transcriptional responses to perturbations of its activity, 29 such as overexpressing it, could support TF network mapping. Systematic data sets of both 30 types exist for yeast and for human K562 and HEK293 cells. 31 Results: In previous data, most TF binding sites appear to be non-functional, so one cannot 32 take the genes in whose promoters a TF binds as its direct, functional (DF) targets. Taking the 33 genes that are both bound by a TF and responsive to a perturbation of it as its DF targets 34 (intersection algorithm) is also not safe, as we show by deriving a new lower bound on the 35 expected false discovery rate of the intersection algorithm. When there are many non-functional 36 binding sites and many indirect targets, non-functional sites are expected to occur in the cis-37 regulatory DNA of indirect targets by chance. Dual threshold optimization, a new method for 38 setting significance thresholds on binding and response data, improves the intersection 39 algorithm, as does post-processing perturbation-response data with NetProphet 2.0. A 40 comprehensive new data set measuring the transcriptional response shortly after inducing 41 overexpression of a TF also helps, as does transposon calling cards, a new method for 42 identifying TF binding locations. 43 Conclusions: The combination of dual threshold optimization and NetProphet greatly expands 44 the high-confidence TF network map in both yeast and human. In yeast, measuring the 45 response shortly after inducing TF overexpression and measuring binding locations by using 46 transposon calling cards improve the network synergistically. 47 48
Gene expression changes are orchestrated by transcription factors (TFs), which bind to DNA to regulate gene expression. It remains surprisingly difficult to predict basic features of the transcriptional process, including in vivo TF occupancy. Existing thermodynamic models of TF function are often not concordant with experimental measurements, suggesting undiscovered biology. Here, we analyzed one of the most well-studied TFs, the yeast zinc cluster Gal4, constructed a Shea–Ackers thermodynamic model to describe its binding, and compared the results of this model to experimentally measured Gal4p binding in vivo. We found that at many promoters, the model predicted no Gal4p binding, yet substantial binding was observed. These outlier promoters lacked canonical binding motifs, and subsequent investigation revealed Gal4p binds unexpectedly to DNA sequences with high densities of its half site (CGG). We confirmed this novel mode of binding through multiple experimental and computational paradigms; we also found most other zinc cluster TFs we tested frequently utilize this binding mode, at 27% of their targets on average. Together, these results demonstrate a novel mode of binding where zinc clusters, the largest class of TFs in yeast, bind DNA sequences with high densities of half sites.
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