five

Quantifying RNA binding sites transcriptome-wide using DO-RIP-seq. Homo sapiens

收藏
NIAID Data Ecosystem2026-03-09 收录
下载链接:
https://www.ncbi.nlm.nih.gov/bioproject/PRJNA348110
下载链接
链接失效反馈
官方服务:
资源简介:
RNA-binding proteins (RBPs) and non-coding RNAs orchestrate post-transcriptional processes through the recognition of specific sites on targeted transcripts. Thus, understanding the connection between binding to specific sites and active regulation of the whole transcript is essential. Many immunoprecipitation techniques have been developed that identify either whole transcripts or binding sites of RBPs on each transcript using cell lysates. However, none of these methods simultaneously measures the strength of each binding site, and quantifies binding to whole transcripts. In this study, we compare current procedures and present Digestion Optimized (DO)-RIP-seq, a simple method that locates and quantifies RBP binding sites using a continuous metric. We have used the RBP HuR/ELAVL1 to demonstrate that DO-RIP-seq can quantify HuR binding sites with high coverage across the entire human transcriptome, thereby generating metrics of relative RNA binding strength. We demonstrate that this quantitative enrichment of binding sites is proportional to the relative in vitro binding strength for these sites. Also, we used DO-RIP-seq to quantify and compare HuR's binding to whole transcripts, thus allowing for seamless integration of binding site data with whole transcript measurements. Finally, we demonstrate that DO-RIP-seq is useful for identifying functional mRNA target sets, and binding sites where combinatorial interactions between HuR and AGO-microRNAs regulate the fate of the transcripts. Our data indicate that DO-RIP-seq will be useful for quantifying RBP binding events that regulate dynamic biological processes. Overall design: Three HuR immunoprecipitation replicates and three matched negative control immunoprecipitation replicates.
创建时间:
2016-10-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作