A compendium of RNA-binding motifs for decoding gene regulation

By Debashish Ray, Hilal Kazan, Kate B. Cook, Matthew T. Weirauch, H. Najafabadi, Xiao Li, Serge Gueroussov, Mihai Albu, Hong Zheng, A. Yang and 25 more. Nature, 2013

1,461 citations recorded by the source corpus.

RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6943c053b8d7b29e384c45d180d2da4bac8434cc

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Citations in the corpus

Fragile X Mental Retardation Protein: A Paradigm for Translational Control by RNA-Binding Proteins (2015, methodology): “Finally, a high-throughput in vitro method to determine the RNA binding specificity of RBPs indicated that the KH domains of human and Drosophila FMRP bind to KGACARG and AHGGACR (in which R = A or G and H = A, U or C), respectively (Ray et al., 2013).”
Extensive cross-regulation of post-transcriptional regulatory networks in Drosophila (2015, positive): “Additionally, we compared our motifs to those discovered using the in vitro RNAcompete method (Ray et al. 2013) and found strong correspondence (Supplemental Methods).”
RBPmap: a web server for mapping binding sites of RNA-binding proteins (2014, background): “Recently, Ray et al. have used thousands of short designed RNA oligos to determine the binding preferences of 207 different RBPs, mainly from human and Drosophila melanogaster (3).”
Convergence of Acquired Mutations and Alternative Splicing of CD19 Enables Resistance to CART-19 Immunotherapy (2015, background): “However, the Drosophila homolog of SRSF3, RBP-1, is known to bind to the [A/T] CAAC[A/G] hexamer (23).”
The BioGRID interaction database: 2015 update (2014, background): “At the same time, sequencing-based meth-ods have uncovered many intricacies of gene regulation at a genomic scale, including expression patterns, alternative splicing, non-coding transcription and the myriad of regulatory factors that bind DNA and RNA (7–11).”
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (2015, methodology): “We also applied models trained on in vitro RNAcompete data to in vivo CLIP-seq (crosslinking and immunoprecipitation followed by high-throughput sequencing) and RIP-seq (RNA-immunoprecipitation sequencing) data 22 .”
The human splicing code reveals new insights into the genetic determinants of disease (2014, methodology): “Because the model was built using the reference genome only, its performance was first validated using held-out data, including additional RNA-seq (54), RT-PCR, RBP binding (14), and MBNL knockdown (15) data sets.”
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, methodology): “For example, there are 5 known motifs (M031, M108, M112, M127, M232) in study [34] for protein ELAVL1-2, and all of them have been correctly discovered by iDeep.”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “data, and classifying and detecting cancers from them (D); and predicting DNA- and RNA-sequence specificity (E), RNA binding proteins (F), and micro RNA precursors (G). Ray et al.: method proposed in [148]. 12/32 DUD-E dataset (reported in [187]), the RF based method outperforms the CNN based method”
A census of human RNA-binding proteins (2014, background): “sites (Ascano et al., 2012a; Ray et al., 2013; Spencer et al., 2006; Todd et al., 2001), and the evolution of paralogs represent an alternative to facilitating regulation across cell types.”