Genes in the brain are very long and can be transcribed into diverse RNAs.


To understand how RNPs assemble on RNAs, and how this can go wrong in diseases, we need to identify protein-RNA and RNA-RNA interactions that take place inside cells.

Studies of protein-RNA interactions

We develop techniques that integrate biochemistry and computational biology to obtain comprehensive maps of protein-RNA and RNA-RNA interactions in cells and tissues. To identify protein-RNA contacts at high resolution and quantitative power across the transcriptome, we developed the individual-nucleotide resolution UV crosslinking and immunoprecipitation of protein-RNA complexes (iCLIP). We used computational approaches to demonstrate that great majority of cDNAs in iCLIP truncate at crosslink sites, and therefore the start of reads obtained by high-throughput sequencing can be used to identify protein-RNA crosslink sites. 

A schematic of iCLIP

A schematic of iCLIP and related protocols to study protein-RNA interactions or RNA methylation (from Lee et al, Mol Cell, 2018)



Studies of RNA-RNA interactions

Studies of RNA-RNA interactions

To understand the assembly of RNPs, it is also crucial to identify the RNA-RNA contacts that form between and within RNAs, because RNA structure has an important contribution to the formation of RNP. For this purpose, developed a technique called hiCLIP (or hybrid iCLIP), which identifies the connections that hook sections of RNAs together, referred to as RNA duplexes. This identified RNA duplexes between different regions of the same RNA, as well as interactions between different RNAs, such as long-noncoding RNAs and mRNAs. We were amazed to find that these duplexes often hook together very distant parts of mRNA molecules, and these duplexes interact with the double-stranded RNA binding protein Staufen 1. These long-range RNA-RNA contacts are likely important determinants of the higher-order conformation of RNPs, and we continue using hiCLIP methods to study how the sequence and structure of RNAs defines the composition and function of RNPs on long RNA molecules.

RNA-RNA contacts identified in mRNAs

RNA-RNA contacts, as identified by hiCLIP across human transcriptome, are marked on a standardised mRNA, which is divided into 5' UTR, CDS and 3' UTR (from Sugimoto et al, 2015).

Software for analysis of iCLIP and hiCLIP data

With support from the Wellcome trust open research funding, we have worked jointly with the bioinformatic startup Genialis and our collaborator Tomaž Curk to establish the public web platform iMaps, which provides a streamlined analysis of high-throughput sequencing data produced by iCLIP and all varians of CLIP. The platform contains public data produced by us and other labs, both in raw and processed format, and is freely available for academic groups to analyse their data in a secure and password-protected manner. The server maps the data, defines the peaks of high-occupancy RNA binding sites, annotates the binding sites by transcriptomic features, identifies enriched sequence motifs, and analyses RNA maps, which show the position-dependent binding patterns around important landmarks on transcripts. iMaps is based on various public tools, including iCount, a Python code and associated command-line interface (CLI), which is available from GitHub. We also provide the software for derivation of RNA splicing maps, which accompanies our recent manuscript on Data Science Issues in Understanding Protein-RNA Interactions.

In collaboration with Nick Luscombe lab, we also established hiCLIPr, a software package for analysis of hiCLIP data.

Leading actors

Julian KönigTomaz Curk, Yoichiro Sugimoto, Kathi Zarnack, Nejc Haberman, Ina Huppertz, Chris Sibley, Flora Lee, Anob Chakrabarti, Christina Militti, Igor Ruiz de los Mozos, Charlotte Capitanchik

Selected publications