4B). can quantify HuR binding sites with high coverage across the entire human transcriptome, thereby generating metrics of relative RNA binding strength. We demonstrate that this quantitative enrichment of binding sites is proportional to the relative in vitro binding strength for these sites. In addition, we used DO-RIP-seq to quantify and compare HuR’s binding to whole transcripts, thus allowing for seamless integration of binding site data with whole-transcript measurements. Finally, we demonstrate that DO-RIP-seq is useful for identifying functional mRNA target sets and binding sites where combinatorial interactions between HuR and AGO-microRNAs regulate the fate of the transcripts. Our data indicate that DO-RIP-seq will be useful for quantifying RBP binding events that regulate dynamic biological processes. 3UTR (Fig. 2A; Supplemental Fig. S3A), 5UTR (Supplemental Fig. S3B), 3UTR (Supplemental Fig. S3C), and MYC 3UTR (Supplemental Fig. S3D; Levine et al. 1993; Gao and Keene 1996; Kullmann et al. 2002; Lal et al. 2004). These sites were also identified previously using traditional biochemical and molecular biology techniques. Some of these targets (e.g., and mRNA 3UTR in comparison to the binding site deduced by a previous study (green bar and shading) using deletion analysis (Lal et al. 2004). The log of odds score (LOD) and read depth in reads per million (RPM) are depicted. (shift in the curves suggests a greater proportion of regulated targets because HuR is proposed to generally stabilize mRNAs. Targets identified in both techniques (DO-RIP-seq and PAR-CLIP) appear to identify the largest proportion of regulated targets, while targets only identified by DO-RIP-seq (not PAR-CLIP) outperform targets only identified by PAR-CLIP (not DO-RIP-seq) or not identified as targets by either technique. siRNA data are from Mukherjee et al. (2011). (to indicate the cumulative percentage for each saturation curve. (= (correlation = 0.4996. Colors from blue to red represent increasing density of HuR binding sites. The coordinates with the greatest density of binding sites contribute the most to the correlation value. RNAcompete data are from Ray et al. (2009). Assuming that LOD scores indicate the binding strength of HuR, certain specific sequence characteristics associated with HuR binding would be expected to demonstrate higher scores. To address this question, we analyzed HuR Reparixin DO-RIP-seq binding sites for trends between the frequencies of different U-rich submotifs and LOD scores. We selected potential LOD score cutoffs to produce five binding site groupings that were each 20% of all sites (Supplemental Fig. S4A) and enumerated all possible 7-mers. We grouped 7-mers that were similar (the submotifs) and calculated how frequently they were observed among the LOD score groups. The result was that AU-rich submotifs had increasing frequencies at higher LOD scores, while CU- Reparixin and GU-rich submotifs had decreasing frequencies at higher LOD scores (Fig. 3C; Supplemental Fig. S4B). Note that each LOD score group contained some proportion of each of the U-rich submotifs, and that many of our binding sites contained combinations PCDH8 of these submotifs. This raised the question as to how submotif combinations relate to LOD scores for binding sites. We hypothesized that binding sites with combinations of preferential motifs for HuR binding would have Reparixin higher LOD scores. We tested this hypothesis using RNAcompete Z-scores of 7-mer motifs for HuR, where a Z-score is an empirical value that measures the preference of recombinant HuR for the 7-mer in an in vitro reaction (Ray et al. 2009). A comparison of the summation of 7-mer = 0.4996 (Fig. 3D). Therefore, we conclude that HuR binding sites with high LOD scores contain combinations of preferential sequence motifs. However, it should also be noted that while there is a good correlation between DO-RIP-seq LOD scores and in vitro binding measurements, the relationship is not perfect. We believe that at least some of these differences can be Reparixin attributed to the variations between HuR binding under cellular conditions (DO-RIP-seq) versus in vitro binding (RNAcompete). Taken together, we have quantitatively identified cellular binding sites of HuR to distinguish sites of strong relationships that are partially based on frequencies of particular RNA submotifs. Using DO-RIP-seq to quantify whole-transcript Reparixin focuses on of HuR RNA binding proteins bind to local sites within mRNA or pre-mRNA and these local.