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, 2006). PBM data quantification was performed as previously described (Berger and Bulyk, 2009; Berger et?al., 2006). See Extended Experimental Procedures for details. SVR was trained separately for Cbf1 and Tye7. For each TF, we first selected ChIP-chip bound and ChIP-chip unbound probes centered at the E-box CACGTG. To ensure that no additional binding sites occur in the regions flanking CACGTG, we selected probes (280 for Cbf1 and 312 for Tye7) for which the maximum PBM 8-mer E-score in the flanks was Birinapant molecular weight the Cbf1 and Tye7 PBM fluorescence signal intensities. We used the ��-SVR algorithm implemented in the LIBSVM toolkit (Chang and Lin, 2011) for all SVR Terminal deoxynucleotidyl transferase analyses. We performed a grid search using 10-fold and leave-one-out cross-validation to determine the best values for parameters �� and C (see Extended Experimental Procedures). Using these parameters, we trained the final SVR models using all 280 sequences for Cbf1 and all 312 sequences for Tye7 and used them to predict the PBM log signal intensities for all probes on the validation array. We also performed an SVR analysis using the 312 sequences selected for Tye7 but shuffling the PBM log signal intensities; the best R2 on randomized sets of sequences was Dinaciclib and helix twist) of each unique pentamer was used to characterize a pentamer. A query table for pentamers was assembled using these data, and a sliding pentamer window was implemented to compute structural features for any DNA sequence. We validated our HT method for DNA shape predictions based on a comparison with all crystal structures of protein-DNA complexes available in the Protein Data Bank with a DNA duplex of at least one helical turn (10?bp) and no chemical modifications as specified elsewhere (Bishop et?al., 2011). Spearman��s rank correlation coefficients are 0.67 for minor groove width, 0.55 for propeller twist, 0.