Epigenetic modifications are associated with the regulation of co/post-transcriptional processing and differential transcript isoforms are known to be important during cancer progression. It remains unclear 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. Rapid developments in all areas of next-generation sequencing (DNA, RNA-seq, ChIP-seq, Methyl-CpG, etc.) have provided new opportunities to develop novel integration and data-mining approaches, and also allows for exciting hypothesis driven bioinformatics and computational studies. Here, we present a program that we developed and summarize the results of applying our methods to analyze datasets from patient matched tumor or normal (T/N) paired samples, as well as cell lines that were either sensitive or resistant (S/R) to treatment with an anti-cancer drug, 5-Azacytidine (http://sourceforge.net/projects/chiprnaseqpro/). We discuss additional options for user-defined approaches and general guidelines for simultaneously analyzing and annotating epigenetic and RNA-seq datasets in order to identify and rank significant regions of epigenetic deregulation associated with aberrant splicing and RNA-editing.