SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data

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• dc.contributor.author Dotu, Ivanca
• dc.contributor.author Scott I. Adamsonca
• dc.contributor.author Coleman, Benjaminca
• dc.contributor.author Fournier, Cyrilca
• dc.contributor.author Ricart Altimiras, Emmaca
• dc.contributor.author Eyras Jiménez, Eduardoca
• dc.contributor.author Chuang, Jeffrey H.ca
• dc.date.accessioned 2018-05-09T09:39:46Z
• dc.date.available 2018-05-09T09:39:46Z
• dc.date.issued 2018
• dc.description.abstract RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.
• dc.description.sponsorship JHC was supported by NIH grants R21 HG007554 and R01 NS094637. ERA and EE were supported by the MINECO and FEDER (BIO2014-52566-R), AGAUR (SGR2014-1121), and the Sandra Ibarra Foundation for Cancer (FSI2013).
• dc.format.mimetype application/pdf
• dc.identifier.citation Dotu I, Adamson SI, Coleman B, Fournier C, Ricart-Altimiras E, Eyras E et al. SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data. PLoS Comput Biol. 2018 Mar 29;14(3):e1006078. DOI: 10.1371/journal.pcbi.1006078
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1006078
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/34596
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)ca
• dc.relation.ispartof PLoS Comput Biol. 2018 Mar 29;14(3):e1006078
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/BIO2014-52566-R
• dc.rights © 2018 Dotu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword RNA-binding proteins
• dc.subject.keyword RNA structure
• dc.subject.keyword Double stranded RNA
• dc.subject.keyword RNA folding
• dc.subject.keyword Clustering algorithms
• dc.subject.keyword Sequence alignment
• dc.subject.keyword Immunoprecipitation
• dc.subject.keyword Sequence motif analysis
• dc.title SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation dataca
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion