Abstract
RNA-binding proteins (RBPs) are critical components of post-transcriptional gene expression regulation. However, their binding sites have until recently been difficult to determine due to the apparent low specificity of RBPs for their target transcripts and the lack of high-throughput assays for analyzing binding sites genome wide. Here we present a bioinformatics method for predicting RBP binding motif sites on a genome-wide scale that leverages motif conservation, RNA secondary structure, and the tendency of RBP binding sites to cluster together. A probabilistic model is learned from bona fide binding sites determined by CLIP and applied genome wide to generate high specificity binding site predictions.
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Acknowledgements
The authors would like to thank Lauren E. Fairchild and Huijuan Feng for their assistance in testing the protocol and for providing feedback on the manuscript. This work was supported by grants from the National Institutes of Health (NIH) (R00GM95713) and the Simons Foundation Autism Research Initiative (297990 and 307711) to C.Z.
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Weyn-Vanhentenryck, S.M., Zhang, C. (2016). mCarts: Genome-Wide Prediction of Clustered Sequence Motifs as Binding Sites for RNA-Binding Proteins. In: Lin, RJ. (eds) RNA-Protein Complexes and Interactions. Methods in Molecular Biology, vol 1421. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3591-8_17
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DOI: https://doi.org/10.1007/978-1-4939-3591-8_17
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