CLIPdb: a CLIP-seq database for protein-RNA interactions
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Updated: Apr 17, 2026

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
Published on: April 30, 2011
Yu-Cheng T Yang1, Chao Di2, Boqin Hu3
1MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology and Center for Plant Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China. yang.thomas.yucheng@gmail.com.
Background:
RNA-binding proteins (RBPs) play essential roles in gene expression regulation through their interactions with RNA transcripts, including coding, canonical non-coding and long non-coding RNAs. Large amounts of crosslinking immunoprecipitation (CLIP)-seq data (including HITS-CLIP, PAR-CLIP, and iCLIP) have been recently produced to reveal transcriptome-wide binding sites of RBPs at the single-nucleotide level.
Description:
Here, we constructed a database, CLIPdb, to describe RBP-RNA interactions based on 395 publicly available CLIP-seq data sets for 111 RBPs from four organisms: human, mouse, worm and yeast. We consistently annotated the CLIP-seq data sets and RBPs, and developed a user-friendly interface for rapid navigation of the CLIP-seq data. We applied a unified computational method to identify transcriptome-wide binding sites, making the binding sites directly comparable and the data available for integration across different CLIP-seq studies. The high-resolution binding sites of the RBPs can be visualized on the whole-genome scale using a browser. In addition, users can browse and download the identified binding sites of all profiled RBPs by querying genes of interest, including both protein coding genes and non-coding RNAs.
Conclusion:
Manually curated metadata and uniformly identified binding sites of publicly available CLIP-seq data sets will be a foundation for further integrative and comparative analyses. With maintained up-to-date data sets and improved functionality, CLIPdb ( http://clipdb.ncrnalab.org ) will be a valuable resource for improving the understanding of post-transcriptional regulatory networks.
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