clipplotr—a comparative visualization and analysis tool for CLIP data

Abstract

We would like to thank members of the Ule and Luscombe laboratories for testing the tool and providing user feedback during its development, in particular Andrea Elser, Martina Hallegger, and Flora Lee. This research was funded in whole or in part by the Wellcome Trust (FC010110; 215593/Z/19/Z). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC010110), the UK Medical Research Council (FC010110), and the Wellcome Trust (FC010110). A.M.C. was supported by a Wellcome Trust PhD Training Fellowship for Clinicians Award (110292/Z/15/Z) and is currently supported by a Crick Postdoctoral Clinical Fellowship and a Starter Grant for Clinical Lecturers from the Academy of Medical Sciences (SGL023 \1085). This work was also supported by Wellcome Trust Joint Investigator Awards (215593/Z/19/Z) to J.U. and N.M.L. N.M.L. is additionally supported by core funding from the Okinawa Institute of Science & Technology Graduate University. CLIP technologies are now widely used to study RNA–protein interactions and many data sets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either across conditions within a particular project, or incorporating publicly available data. However, the output files produced by data processing pipelines or preprocessed files available to download from data repositories are often not suitable for direct comparison and usually need further processing. Furthermore, to derive biological insight it is usually necessary to visualize a CLIP signal alongside other data such as annotations, or orthogonal functional genomic data (e.g., RNA-seq). We have developed a simple, but powerful, command-line tool: clipplotr, which facilitates these visual comparative and integrative analyses with normalization and smoothing options for CLIP data and the ability to show these alongside reference annotation tracks and functional genomic data. These data can be supplied as input to clipplotr in a range of file formats, which will output a publication quality figure. It is written in R and can both run on a laptop computer independently or be integrated into computational workflows on a high-performance cluster. Releases, source code, and documentation are freely available at https://github.com/ulelab/clipplotr.