دورية أكاديمية
SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data.
| العنوان: | SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data. |
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| المؤلفون: | Ivan Dotu, Scott I Adamson, Benjamin Coleman, Cyril Fournier, Emma Ricart-Altimiras, Eduardo Eyras, Jeffrey H Chuang |
| المصدر: | PLoS Computational Biology, Vol 14, Iss 3, p e1006078 (2018) |
| بيانات النشر: | Public Library of Science (PLoS), 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Biology (General) |
| مصطلحات موضوعية: | Biology (General), QH301-705.5 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1553-734X 1553-7358 |
| Relation: | http://europepmc.org/articles/PMC5892938?pdf=render; https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358 |
| DOI: | 10.1371/journal.pcbi.1006078 |
| URL الوصول: | https://doaj.org/article/9acce46fc8724d6abe84f928cd8dadf6 |
| رقم الأكسشن: | edsdoj.9acce46fc8724d6abe84f928cd8dadf6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1553734X 15537358 |
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| DOI: | 10.1371/journal.pcbi.1006078 |