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Until now, determining the sequences recognized by an RNA-binding protein has been time and labor intensive. Ray et al . use a custom pool of >210,000 oligos that encode linear and stem-loop RNAs to rapidly determine the sequences bound by nine RNA-binding proteins. Metazoan genomes encode hundreds of RNA-binding proteins (RBPs) but RNA-binding preferences for relatively few RBPs have been well defined 1 . Current techniques for determining RNA targets, including in vitro selection and RNA co-immunoprecipitation 2 , 3 , 4 , 5 , require significant time and labor investment. Here we introduce RNAcompete, a method for the systematic analysis of RNA binding specificities that uses a single binding reaction to determine the relative preferences of RBPs for short RNAs that contain a complete range of k-mers in structured and unstructured RNA contexts. We tested RNAcompete by analyzing nine diverse RBPs (HuR, Vts1, FUSIP1, PTB, U1A, SF2/ASF, SLM2, RBM4 and YB1). RNAcompete identified expected and previously unknown RNA binding preferences. Using in vitro and in vivo binding data, we demonstrate that preferences for individual 7-mers identified by RNAcompete are a more accurate representation of binding activity than are conventional motif models. We anticipate that RNAcompete will be a valuable tool for the study of RNA-protein interactions.

Sequence preferences of DNA binding proteins are a primary mechanism by which cells interpret the genome. Despite the central importance of these proteins in physiology, development, and evolution, comprehensive DNA binding specificities have been determined experimentally for only a few proteins. Here, we used microarrays containing all 10-base pair sequences to examine the binding specificities of 104 distinct mouse DNA binding proteins representing 22 structural classes. Our results reveal a complex landscape of binding, with virtually every protein analyzed possessing unique preferences. Roughly half of the proteins each recognized multiple distinctly different sequence motifs, challenging our molecular understanding of how proteins interact with their DNA binding sites. This complexity in DNA recognition may be important in gene regulation and in the evolution of transcriptional regulatory networks.

Members of the large ETS family of transcription factors (TFs) have highly similar DNA‐binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA‐binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA‐binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high‐throughput microwell‐based TF DNA‐binding specificity assay, and protein‐binding microarrays (PBMs). Both approaches reveal that the ETS‐binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino‐acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP‐seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

The most comprehensive analysis to date of models of transcription-factor binding specificity reveals the best methods for predicting in vivo binding from in vitro data. Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro –derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

Two striking findings of vertebrate genome sequencing are the presence of large numbers of DNA binding proteins and the high proportion of conserved non-coding sequences, much of which is cis-regulatory. Lack of comprehensive knowledge about binding specificities of transcription factors (TFs) complicates de novo computational searches for novel cis-regulatory elements. To address the paucity of information on binding affinity of TFs, I aimed to characterize the DNA-binding specificities of ∼1500 murine TFs. I was involved in the construction of a library of DNA-binding domains (DBDs) of mouse TFs. I utilized this library to express/purify the DBDs for application on Protein Binding Microarrays, an array-based approach used to determine binding specificity of TFs. Analysis of preliminary PBM results are consistent with known binding specificities for TFs. Data for uncharacterized TFs yielded diverse new motifs. The data will provide a valuable resource in understanding the relationship between TFs and their cis-regulatory elements.