wavClusteR

Background

What is PAR-CLIP?

The Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a recently developed biochemical method used to map RNA-binding proteins (RBP) - RNA interactions transcriptome-wide and at high resolution.

In the standard PAR-CLIP protocol, cells are first cultured in the presence of a photoreactive ribonucleoside analogs, such as 4-thiouridine (4-SU) and 6-thioguanosine (6-SG), which is incorporated in vivo into nascent RNA transcripts. Irradiation with UV light (365 nm) is then used to covalently cross link the photoreactive nucleoside-labeled cellular RNAs to interacting RBPs. The cross linked RNA-RBP complexes are then isolated by immunoprecipitation of the RBP of interest and the coimmunoprecipitated RNA released by protein degradation. The resulting RNA is converted into a cDNA library and the identity of the RNA molecules is determine by next-generation sequencing (NGS).

Over the past five years, the PAR-CLIP protocol has been widely used to identify the binding sites of several known RBPs and microRNA-containing ribonucleoprotein complexes. However, PAR-CLIP data analysis is not straightforward and dedicated methods are required to control false discovery rates.

Introduction

wavClusteR is an R/BioConductor package for PAR-CLIP data analysis can be used to:

• Infer PAR-CLIP induced transitions and discriminate them from sequencing errors, SNPs, contaminants and additional non-experimental causes, using a non-parametric mixture model (Sievers et al, 2012; Comoglio et al, 2015).
• Resolve protein binding sites (clusters) at high resolution.
• Rank clusters for downstream analyses and validation based on a solid statistical framework.
• Integrate total RNA-Seq data, if available, to estimate false discovery rates.
• Post-processing of clusters, including annotation of protein binding sites, data export in common UCSC formats and motif search analysis.

The package supports multicore computing. While wavClusteR was designed for PAR-CLIP data analysis, it can be adapted to the analysis of other NGS data from experimental procedures exploting nucleotide substitutions for site detection (e.g. BisSeq).

Download and Installation

To install wavClusteR from BioConductor, start R (>3.1.0) and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("wavClusteR")

To install the package directly from this repository, type:

library(devtools)
install_github(repo = 'FedericoComoglio/wavClusteR')

Documentation

The package provides a vignette detailing all steps required to analyze your PAR-CLIP data. To view this document for the version of wavClusteR installed in your system, start R and enter:

browseVignettes("wavClusteR")

Citation

If you are using wavClusteR for your analysis, please cite:

• Comoglio, F., Sievers, C., and Paro, R. (2015). Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data. BMC Bioinformatics, 16, 32. doi 10.1186/s12859-015-0470-y
• Sievers, C., Schlumpf, T., Sawarkar, R., Comoglio, F., and Paro, R. (2012). Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data. Nucleic Acids Res. 40(20), e160. doi 10.1093/nar/gks697

Issues

Should you

• experience any issue using wavClusteR
• need support for your analysis
• have specific operating system requirements

Please post a new topic on the Bioconductor support site https://support.bioconductor.org using wavClusteR as a tag. See https://www.bioconductor.org/help/support/ for more details.

If required, please do not hesitate to contact me @ federico.comoglio@gmail.com