CLASH Analyst: A Web Server to Identify In Vivo RNA-RNA Interactions from CLASH Data.

Wei-Sheng Wu,Pin-Hao Chen,Dong-En Lee,Sheng-Cian Shiue,Heng-Chi Lee,Jordan S Brown

doi:10.3390/ncrna8010006

Wei-Sheng Wu, Pin-Hao Chen + Show 4 more

Open Access

https://doi.org/10.3390/ncrna8010006

Copy DOI

Abstract

Non-coding RNAs, such as miRNAs and piRNAs, play critical roles in gene regulation through base-pairing interactions with their target molecules. The recent development of the crosslinking, ligation, and sequencing of hybrids (CLASH) method has allowed scientists to map transcriptome-wide RNA–RNA interactions by identifying chimeric reads consisting of fragments from regulatory RNAs and their targets. However, analyzing CLASH data requires scientists to use advanced bioinformatics, and currently available tools are limited for users with little bioinformatic experience. In addition, many published CLASH studies do not show the full scope of RNA–RNA interactions that were captured, highlighting the importance of reanalyzing published data. Here, we present CLASH Analyst, a web server that can analyze raw CLASH data within a fully customizable and easy-to-use interface. CLASH Analyst accepts raw CLASH data as input and identifies the RNA chimeras containing the regulatory and target RNAs according to the user’s interest. Detailed annotation of the captured RNA–RNA interactions is then presented for the user to visualize within the server or download for further analysis. We demonstrate that CLASH Analyst can identify miRNA- and piRNA-targeting sites reported from published CLASH data and should be applicable to analyze other RNA–RNA interactions. CLASH Analyst is freely available for academic use.

Highlights

To allow scientists with and without knowledge of bioinformatics to perform inTo allow scientists with and without knowledge of bioinformatics to perform intetegrative analysis of CLASH data, we developed CLASH Analyst, which accepts raw grative analysis of CLASH data, we developed CLASH Analyst, which accepts raw CLASH data and outputs fully processed and easy-to-understand representations of the transcriptome-wide RNA–RNA interactions contained within those data
Data are not trivial, and current tools are limited to researchers with advanced bioinformatic experience
Our goal in developing the CLASH Analyst was to allow researchers of varying levels of bioinformatics expertise to harness the power of CLASH to identify RNA–RNA

Summary

Introduction

RNA molecules perform some of the most fundamental and important functions in organisms, including coding for proteins (mRNAs), providing structural support (tRNAs, lncRNAs), acting as vital enzymes (snRNAs, ribozymes), and guiding proteins to regulate other RNAs (miRNAs, piRNAs, siRNAs). Discovered only relatively recently, this last category of RNA function has been shown to play a prominent role in posttranscriptional and transcriptional gene regulation. By guiding Argonaute family proteins to RNA targets using base-pairing interactions, miRNAs, piRNAs, and siRNAs can effectively and target diverse mRNAs for regulation. It has been estimated that ~50% of human genes are regulated by miRNA [1,2,3]. To attempt to identify functionally significant RNA–RNA interactions, researchers have historically relied on bioinformatic prediction to locate possible regulatory events based on sequence complementarity, sequence conservation of target sites, and by experimentally validated results of each regulatory site individually [4]

Methods

Results

Conclusion