Software Packages

Department of Quantitative Health Sciences
Mayo Clinic Research
Formerly known as the Department of Health Sciences Research

Related links: Division Overview R Shiny Applications


Deregulation of epigenetic modifications either mimic the effects of genetic changes, or provide additional heritable alterations that contribute to the development and progression of many cancers.  We are only beginning to understand how disruptions of chromatin-based modifications contribute to tumorigenesis and how this knowledge can be leveraged to develop more potent treatment strategies that target specific isoforms or other products of the co/post-transcriptional regulation pathway.

ChIP-RNA-seqPRO is a resource motivated by this current need and provides a strategy that enables the user to profile regulatory associations between epigenomic modifications and co/post-transcriptional processes.  The modules inherent to the ChIP-RNA-seqPRO program are flexible and available as runnable Python scripts packaged together with customized annotation libraries and wrapped in an easy to run shell script.

Download the tool and annotation libraries from

A limited number of fields are required for all of the input files, therefore, the user has the option to generate epigenomic mark enrichment, RNA variant, and transcript predictions from any preferred program source.  Comparative analysis of matched paired datasets can be applied broadly to include for example, tumor versus normal or parental versus drug-treated samples.  For tumor-versus-normal paired comparisons, outputs include :

  1. identification of novel and known tumor-unique RNA editing sites
  2. predicted tumor-unique novel transcript isoforms
  3. annotation of tumor-unique isoforms and RNA editing sites that are proximal to Alu and non-Alu repetitive elements and associated with epigenetically regulated chromatin regions that are unique or altered in the tumor sample.

These results are summarized in parseable tab-delimited text files that include detailed annotations, as well as a matrix-style summary report that is ‘statistical-program ready’.  Outputs also include GTF files that can be visualized in a genome browser tool (e.g. IGV).

Primary contact: Mia Champion

Page last modified: January 7, 2018