Authors:
Mia D. Champion
1
;
Ryan A. Hlady
1
;
Huihuang Yan
1
;
Jared Evans
1
;
Jeff Nie
1
;
Jeong-Heon Lee
1
;
James M. Bogenberger
1
;
Kannabiran Nandakumar
1
;
Jaime Davila
1
;
Raymond Moore
1
;
Asha Nair
1
;
Daniel O'Brien
1
;
Yuan-Xiao Zhu
1
;
K. Martin Kortüm
1
;
Tamas Ordog
1
;
Zhiguo Zhang
1
;
Richard W. Joseph
1
;
A. Keith Stewart
1
;
Jean-Pierre Kocher
1
;
Eric Jonasch
2
;
Keith D. Robertson
1
;
Raoul Tibes
1
and
Thai H. Ho
1
Affiliations:
1
Mayo Clinic, United States
;
2
The University of Texas MD Anderson Cancer Center, United States
Keyword(s):
Epigenetic Modification, Splicing, RNA-Editing, Co/Post-Transcriptional Processing, Bioinformatics, NMD, Alu Elements, Transcriptome, RNA-seq, Methylation, ChIP-seq, Methyl-CpG, DNAme
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Genomics and Proteomics
;
Next Generation Sequencing
;
Systems Biology
;
Transcriptomics
Abstract:
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 t
reatment 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.
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