Skip to main content
  • Poster presentation
  • Open access
  • Published:

CLIP-EZ: a computational tool for HITS-CLIP data analysis


Mapping the binding regions of mRNA-binding proteins is critical to the understanding of their regulatory roles in cellular processes. Recent development in experimental technologies combines high throughput sequencing with crosslink immunoprecipitation (HITS-CLIP), which has the merit of detecting RNA-protein interaction sites at a high resolution to single nucleotide level. Analysis of such data typically involves many steps, and teasing out true signals from noise requires crosslink induced mutations (CIMS) analysis, peak identification, integration of the two signal types, and some downstream analysis such as motif finding and conservation evaluation. To our knowledge, there is a lack of a single computational tool that can perform all the tasks as mentioned in an easily accessible manner. Despite the fact that there are several tools available, each performs an individual task.

Materials and methods

To facilitate the analysis task, we developed a command line tool that implements the analysis pipeline and outputs all relevant results, intermediate and final, with command line parameters specified by the user.


The outputs are comprehensive which include the map of binding regions, pie charts for the annotation of binding sites, a list of high confidence binding regions which are annotated for CIMS footprint, peak height, motif, and conservation score and ranked by a score that integrates information across the annotations. The output also provides a bed file that can be loaded into the UCSC genome browser for visualization. This software can also be used to identify differential binding regions across biological conditions. This command line tool will be made available for download from GitHub.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Yu Shyr.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit

The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, X., Liu, Q. & Shyr, Y. CLIP-EZ: a computational tool for HITS-CLIP data analysis. BMC Bioinformatics 15 (Suppl 10), P1 (2014).

Download citation

  • Published:

  • DOI: