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Thus, the real-time kinetics of ZF mutants correlates with their genome-wide occupancy as measured by deep sequencing. Based on these data, we conclude that CTCF nuclear dynamics are slower than those?of most transcription factors, and that?mutations affecting individual ZFs increase CTCF mobility in a manner proportional to their interference with binding. In?vitro characterization of CTCF mutants isolated from human tumors (Filippova et?al., 1996, 2002) suggests that ZFs can contribute to CTCF binding as independent units. At the same time, cocrystal structures of C2H2 ZF proteins bound to DNA reveal that adjacent ZFs interact cooperatively with DNA bases in an overlapping pattern of contacts (Wolfe et?al., 2000). This raises the possibility that contiguous ZFs may cluster into DNA binding subdomains. To directly test [http://www.selleckchem.com/products/dinaciclib-sch727965.html Selleck Dinaciclib] this idea, we applied a Pearson��s correlation matrix (Figure?3A) and a principal component analysis (Figure?3B) to variance-stabilized ChIP-seq data. Notably, the two analyses were in good [http://www.selleckchem.com/products/birinapant-tl32711.html Selleck Birinapant] agreement in that they identified five distinct clusters in the data set: (1) ZF8/9/10/11, (2) ZF1/2, (3) WT, (4) ZF4/5/6/7, and (5) ZF3/ZF3* (Figures 3A and 3B). Binding profiles of ZF mutants within a given cluster were highly correlated with an average Pearson��s r of 0.86 (Figure?S3). As an example, the correlation between ZF9 and ZF10 profiles (r?= 0.93) was comparable to that obtained between biological replicates of the same mutants (compare Figures 3C [http://en.wikipedia.org/wiki/Terminal_deoxynucleotidyl_transferase Terminal deoxynucleotidyl transferase] to S1). Additional examples are provided in Figure?S3. Conversely, genome-wide occupancy between members of different clusters was less correlated (Figure?3D), with an average Pearson��s r of 0.65 (p?