Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs), ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to identify 16 candidate TFs and captured a total of 255 DNA binding peaks resulting in six high-confidence binding motifs, reconstructing the regulons of these ten TFs by determining gene expression changes upon deletion of each TF.