Bioinformatics Strategies for Identifying Regions of Epigenetic

Deregulation Associated with Aberrant Transcript Splicing and

RNA-editing

Mia D. Champion

, Ryan A. Hlady

, Huihuang Yan

, Jared Evans

, Jeff Nie

, Jeong-Heon Lee

James Bogenberger

, Kannabiran Nandakumar

, Jaime Davila

, Raymond Moore

, Asha Nair

Daniel O’Brien

, Yuan-Xiao Zhu

, K. Martin Kortum

, Tamas Ordog

4,6

, Zhiguo Zhang

Richard W. Joseph

, A. Keith Stewart

, Jean-Pierre Kocher

, Eric Jonasch

, Keith D. Robertson

Raoul Tibes

and Thai H. Ho

Division of Biomedical Statistics and Informatics, Mayo Clinic, Scottsdale, AZ, U.S.A.

Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, U.S.A.

Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, U.S.A.

Translational Program Center for Individualized Medicine, Mayo Clinic, Rochester, MN, U.S.A.

Division of Hematology and Oncology, Mayo Clinic, Scottsdale, AZ, U.S.A.

Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, U.S.A.

Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, U.S.A.

Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL, U.S.A.

Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center,

Houston, TX, U.S.A.

Keywords: Epigenetic Modification, Splicing, RNA-editing, Co/Post-Transcriptional Processing, Bioinformatics,

NMD, Alu Elements, Transcriptome, RNA-seq, Methylation, ChIP-seq, Methyl-CpG, DNAme.

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 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.

1 INTRODUCTION

Deregulation of epigenetic modifications either

mimics the effects of genetic changes, or provides

additional heritable alterations that contribute to the

development and progression of many cancers

(Feinberg et al., 2006). Epigenetic regulation is not

dependent upon simple binary states of a single type

of modification nor is it a summation of the

activities of regulating methyltransferases. Rather, it

is dependent upon the interplay of both DNA and

histone level modifications that make up complex

“combinatorial codes” (Jin et al., 2012; Cieślik and

Bekiranov, 2014). Epigenetic modifications have a

profound impact on co/post-transcriptional

processes, which also have been identified as having

163

Champion M., Hlady R., Yan H., Evans J., Nie J., Lee J., Bogenberger J., Nandakumar K., Davila J., Moore R., Nair A., O’Brien D., Zhu Y., Kortüm K.,

Ordog T., Zhang Z., Joseph R., Stewart A., Kocher J., Jonasch E., Robertson K., Tibes R. and H. Ho T..

Bioinformatics Strategies for Identifying Regions of Epigenetic Deregulation Associated with Aberrant Transcript Splicing and RNA-editing.

DOI: 10.5220/0005248001630170

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2015), pages 163-170

ISBN: 978-989-758-070-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

an important role in cancer progression. For

example, modulation of the levels of the histone

modification H3K36me3 (trimethylated histone H3

lysine 36) either by overexpression, or by silencing

of the SETD2 methyltransferase directly effects

alternative splicing of associated exons (Luco et al.,

2010; Simon et al., 2014). One of these alternatively

spliced mRNAs, FGFR2, commonly undergoes an

isoform switch from a “normal IIIb” FGFR2

transcript isoform to a mesenchymal “IIIc” form in

~90% of kidney renal clear cell carcinomas (ccRCC)

(Zhao et al., 2013). The mechanisms determining

how epigenetic modifications influence differential

mRNA splicing are unknown and may involve

modified histone protein mediated recruitment of

components of the splicing machinery, or on the

DNA level, may involve a regulatory role of mobile

sequence elements (e.g. Alu) that are known to

mediate “exonization” (Makalowski et al., 1994;

Ast, 2004). Alu elements are reportedly enriched for

DNA-methylation as well as active (e.g.

H3K36me3) and repressive associated histone

methylation marks (Huda et al., 2010). It is known

that in some cases, histone modifications involved in

transposable element regulation serve as a “seed

region” from which the marks can spread into

adjacent genes (Kidwell and Lisch, 2000; Feschotte,

2008). Alu elements are also involved in mediating

the post-transcriptional process of RNA-editing and

if oriented in opposition, are a favored substrate for

the ADAR enzymes (Athanasiadis et al., 2004;

Bazak et al., 2014). Computational comparative

studies have revealed that >90% of all A->I

substitutions occur within Alu elements present in

mRNAs (Kim et al., 2004; Levanon et al., 2004;

Athanasiadis et al., 2004), and there are over a

hundred million sites (Bazak et al., 2014). Although

the functional implications of Alu-associated RNA

editing are still largely unknown, it is known that

these variations influence splice site modification

(Rueter et al., 1999), mRNA stability (Wang et al.,

2013), and may affect transport. In addition, RNA-

editing sites have a known role in regulating cell

proliferation during the progression of cancer (Paz et

al., 2007; Choudhury et al., 2012; Chen et al.,

2013). Deeper understanding regarding how aberrant

epigenomic modifications regulate gene expression

and co/post-transcriptional changes during the

development and progression of many cancers

continues to unfold. Here, we present methods

packaged as a Python program used for comparative

analysis of paired patient matched tumor or normal

(T/N) samples, as well as cell lines that were either

sensitive or resistant (S/R) to treatment with the anti-

cancer drug, 5-Azacytidine in order to: 1) identify

regions exhibiting shifts in epigenetic modification

peaks between two paired samples, 2) classify

indicators of co/post-transcriptional mis-regulation

associated with epigenetic deregulation as the

abundance of aberrant splicing events and frequency

of RNA editing variations within identified regions

and, 3) assess the significance of differences for 1)

and 2) between paired samples in order to prioritize

regions for further clinical studies.

2 METHODS AND RESULTS

2.1 Source of Material for

Comparative Studies of Epigenetic

Deregulation and

Co/Post-Transcriptional

Modifications

Comparative epigenetic and transcriptional datasets

can come from a number of sources. The two most

commonly studied epigenetic modifications involve

the binding of proteins (e.g. histones) to DNA and

the methylation of cytosine nucleotides (e.g. CpG

dinucleotide). Differential library preparations are

used to characterize diverse types of histone

methylation patterns that are commonly associated

with repressive or enhanced states of chromatin and

gene expression. Transcriptome datasets are

typically generated by either microarray or RNA-seq

technologies. RNA sequencing is more suitable for

studies focused on assessing the effects of aberrant

epigenetic regulation on transcriptional processing

since it enables assessment of the presence and

abundance of novel transcript isoforms, in addition

to known transcripts (Zhao et al., 2014). Read

counts also provide a means to calculating more

absolute levels of expression and reduce signal-to-

noise ratios that are often problematic when

assessing hybridization experiments (Zhao et al.,

2014). DNA sequencing (either at the genome or

exome level) can be used to do comparative filtering

from RNA variation datasets when identifying

known or novel candidate RNA editing sites. With

regard to parameters influencing studies of

epigenetic regulation of co/post-transcriptional

processes, we discuss below some of the key issues

for dataset generation and processing.

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Figure 1: Workflow to expedite identification of diagnostic biomarkers or clinical targets (Champion, 2014).

2.2 Complying with the

“Garbage-In-Garbage-Out”

Concept

Each additional data source used for broad

comparative analysis increases the demand to

comply with the Garbage-In-Garbage-Out (GIGO)

concept. The percentage and distribution of millions

of sequencing reads successfully aligned to a

reference genome greatly impacts predictions of

peak enrichment (ChIP-seq), differential transcript

isoform modeling and abundance (RNA-seq), and

sequence variation identification (DNA and RNA-

seq). Standardizing thresholds for sequencing

quality, number of mismatches allowed per

alignment, and placement of reads mapping to

multiple genomic locations are steps to introducing

consistency that reduces noise when comparing

diverse datasets. Along these lines, community

established “best practices” optimize each

independent workflow, which translates to high

quality comparative datasets when interpreting

convergence of diverse variables (The Encode

Project Consortium, 2011; Landt et al., 2012; The

Broad Institute of MIT and Harvard, 2014).

Different categories of ChIP-seq tag enrichments, or

“peaks”, determine which algorithms or parameter

settings are best for optimization of true signal

prediction. Narrow peaks are characteristic of

sequence specific transcription factor binding or

RNA polymerase II transcription start site specificity

whereas broader peak domains are characteristic of

most histone marks that span a nucleosome sized

region or larger chromatin domain (Pepke et al.,

2014). In our studies (Figure 1A), SICER was used

to identify extended domains of ChIP enrichment by

adjusting the window scan with gaps allowed

parameter to the recommended size of

approximately one nucleosome and linker (~200

bps) (Zang et al., 2009). Number of read pairs from

each peak region in IP and that from the

corresponding region in input was normalized to a

library size of 10 million (FPTM) and the input-

subtracted FPTM values were used for differential

binding analysis. False Discovery Rates (FDRs)

were determined from poisson p-values and

enrichment predictions were further filtered

according to a threshold cutoff (FDR<0.01). For our

genome-wide DNA methylation studies, we used the

Infinium Human Methylation450 BeadChip

(Illumina, 2014) and normalized results using subset

quantile within-array normalization (SWAN)

(Maksimovic et al., 2012). Since there are known

gender specific epigenetic modifications, many

studies typically remove all X/Y associated data

points in order to analyze them separately. However,

we recommend doing this as a final step in the

analysis process since the inclusion of these data

points allow for a more accurate p-value adjustment

for multiple testing. Comparisons of epigenetic

deregulation and co/post-transcriptional processes

are at the gene level, such that the total region of

BioinformaticsStrategiesforIdentifyingRegionsofEpigeneticDeregulationAssociatedwithAberrantTranscriptSplicing

andRNA-editing

165

epigenetic modification affecting identified

transcript isoforms, sequence elements, and

variations is a summation of all significant peaks

within the ORF (Figure 1A).

Differences in alignment methods are also

fundamental to ensuring “best practices” of variant

calling in DNA versus RNA sequencing datasets

(The Broad Institute of MIT and Harvard, 2014).

Correct processing of RNA splice junctions,

avoidance of using soft-clipped bases, and

specialized confidence thresholds minimizes false

positive or negative variation calls. In addition,

RNA-seq library preparation protocols for creating

cDNA from RNA commonly use random hexamers

for the priming step, thus increasing the likelihood

of errors in the terminal 6bp of the read. A

combination of existing GATK parameters (e.g.

FisherStrandFilter, ReadPosRankSumTest (The

Broad Institute of MIT and Harvard, 2014)) provide

best practice filtering approaches instead of

customized methods that likely remove true positive

RNA editing sites (RVboost (Wang et al., 2014)).

Aggressive removal of shared sequence variations

between RNA and comparative DNA datasets is a

first step to identifying known and novel RNA

editing sites (Figure 1B). In addition to variations

identified using DNA sequencing generated from

same individual/cell line collections, DNA SNPs

were also identified from public population

databases (1000 Genomes, HapMap, dbSNP,

BGI200/Danish, ESP6500 European and African

datasets) (dbSNP, 2014; HapMap, 2010; ESP6500,

2014; 1000 Genomes, 2014). Mapping identified

RNA sequencing variations to existing or

customized RNA editing databases and resources

expedites identification of known RNA editing sites

(Champion, 2014; Ramaswami and Li, 2014; Kiran

and Baranov, 2010; Li et al., 2009) (Figure 1C).

Computational prioritization of candidate novel

RNA editing sites includes evaluation of the

proximity of identified RNA sequencing variations

to splice sites or paired Alu elements in opposite

orientation.

RNA-seq alignment methods, such as Tophat

(Trapnell et al., 2010), have been developed to

handle mapping of reads spanning exon-exon splice

junctions (Pepke et al., 2014). Cufflinks uses a

bipartite graphing method to assess a “minimum-

cost-maximum-matching” of bundled fragments

from Tophat read alignments in order to build a

parsimonious set of transcript models (Trapnell et

al., 2010). Cufflinks then also provides an

estimation of expression levels using established

methods (Li et al., 2010; Jiang and Wong, 2009).

We also binned predicted transcript isoforms

according to their scored minor FPKM/major FPKM

ratio in order to identify and compare differences in

transcript isoform abundances (Figure 1A). Unlike

other available algorithms, cufflinks also allows for

the identification of novel as well as known

transcripts, and exhibits superior estimates of

accuracy as measured by the median value of

relative errors in percentage across all genes when

compared to other methods (Nicolae et al., 2011).

Novel transcript predictions are essential for studies

of aberrant co/post-transcriptional processes.

Identifying novel transcripts associated with

epigenetic deregulation is useful for the discovery of

“isoform-switching” events that are associated with

drug resistance or cancer progression, similar to the

mesenchymal “IIIc” FGFR2 isoform abundant in

ccRCC. Correlating the abundance of novel

transcripts identified with regions of epigenetic

deregulation is useful for assessing levels of aberrant

transcription (e.g. “transcriptional-noise”), and

exploring possible regulatory roles of the Nonsense-

Mediated-Decay (NMD) pathway during cancer

progression (Gardner, 2011; Frischmeyer and Dietz,

1999). In addition to evidence that epigenetic

deregulation mediates aberrant splicing, these

processes also affect the rate of transcription (Veloso

et al., 2014; Eswaran et al., 2013); although, the

functional implications of differences in transcript

isoform abundances with regards to tumorigenesis or

drug response are largely unknown. Differences in

“methodological-flow” can also be used to expedite

specific outputs from these types of studies. For

example, transcriptome-profiling studies aimed to

characterize global regulatory shifts in transcript

splicing or abundance would be better done using a

transcriptome-to-peak analysis workflow.

Conversely, identification of diagnostic biomarkers

or potential clinical targets is expedited by starting

with epigenetic deregulated regions (Figure 1A) in

order to identify aberrant target transcript isoforms,

or RNA-editing site variations associated with

phenotype progression.

2.3 Bioinformatics Methods to

Integrate Epigenetic and

Co/Post-Transcriptional Datasets

Historically, methods that integrate epigenetic and

microarray expression datasets were developed to

characterize transcriptional regulons. There are

many available tools and methods available for

comparative analysis via correlative clustering of

significant shifts in epigenetic modification patterns

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166

with differential gene expression (e.g. Rcade,

TransView (Bioconductor Software Packages,

2014)). However, unique to our approach is the

inclusion of additional variables of co/post-

transcriptional mis-regulation associated with

changes in epigenetic modifications. Although

important biological relationships between the

variables used to evaluate epigenetic influences on

transcriptional processes exist, we find that pairwise

Spearman correlations (R Development Core Team,

2011) between most of the variables assessed in our

studies are not significant (Figure 2, Frequency of

non-Alu (A.) and Alu (B.) repeats, C. Frequency of

RNA editing sites, D. Adjusted Peak Length Shift,

E. ORF size, F. Frequency of novel transcript

isoforms). An exception is the expected positive

correlation between the distribution of Alu elements

(B.) and RNA editing sites (C.) within a given

region (Figure 2).

Figure 2: RNA-editing sites and Alu-elements are the only

two variables that are significantly correlated in regions of

epigenetic modification shifts.

Therefore, application of any multi-variable co-

clustering approaches would likely be inadequate

and unnecessarily exhaust computational resources.

Rather, we find that first clustering independent

variables followed by a comparative assessment of

significant differences between two conditions (e.g.

T/N, R/S) across a given region using the Student

unpaired t-Test followed by estimation of FDR using

a beta-uniform mixture model (R Development Core

Team, 2011), provides biologically meaningful

results and is useful for ranking the identified

regions of aberrant epigenetic modification and

co/post-transcriptional deregulation (Figure 3).

Finally, functional clustering of candidate clinical

targets identified by our comparative studies into

predicted interaction networks and biological

pathways identified several genes regulating cell

Figure 3: P-values equal to or less than 0.01 are within the

range of desired false discovery rate as estimated by a

beta-uniform mixture model. Significant p-values were

used to select regions for further functional analysis using

network interaction and biological pathway prediction

algorithms.

cycle progression and mRNA processing, which

further supports the validity of our methodologies

and provides an additional level of prioritization for

future diagnostic biomarker or clinical target

development (Table 1).

3 CONCLUSIONS AND

PERSPECTIVES

Advanced sequencing techniques and well-

considered bioinformatics methods provide

unprecedented opportunities for in depth

comparative studies of paired (T/N or R/S) datasets

in order to understand the regulatory roles of

epigenetic modifications on co/post-transcriptional

processes, and how deregulation of these functional

relationships promotes drug resistance and

contributes to the progression of cancer. Our studies

using a developed program to identify regions

exhibiting significant epigenetic modification

changes and aberrant co/post-transcriptional

processing exemplifies one of the workflows we

presented and provides evidence that studies such as

these yield meaningful results of potential high

impact for subsequent diagnostic biomarker or

therapeutic target design endeavors.

BioinformaticsStrategiesforIdentifyingRegionsofEpigeneticDeregulationAssociatedwithAberrantTranscriptSplicing

andRNA-editing

167

Table 1. Genes exhibiting significant epigenetic

modification shifts associated with aberrant co/post-

transcriptional processing in a 5-Azacytidine resistant

human erythroleukemia cell line, but not in a myeloid

progenitor cell line, cluster into predicted interaction

networks and functional biological pathways.

Biological

Pathway p-value FDR

Frequency

of Genes in

Pathway

Mitotic

Metaphase and

Anaphase 0

<1.00E-

03 10

Mitotic

Prometaphase 0

<5.00E-

04 8

Processing of

Capped Intron-

Containing Pre-

mRNA 0

3.33E-

04 8

RNA transport 0.0001

1.48E-

02 7

PLK1 signaling

events 0.0003

2.06E-

02 4

IL6-mediated

signaling events 0.0003

2.22E-

02 4

Deadenylation-

dependent

mRNA decay 0.0004

2.24E-

02 4

mRNA

surveillance

pathway 0.0004

2.15E-

02 5

Role of

Calcineurin-

dependent

NFAT signaling

in lymphocytes 0.0005

1.97E-

02 4

Regulation of

retinoblastoma

protein 0.001

3.95E-

02 4

IL2 signaling

events mediated

by STAT5 0.001

3.87E-

02 3

Mitotic G2-

G2/M phases 0.0013

4.66E-

02 5

IFN-alpha

signaling

pathway 0.0015

4.74E-

02 2

EPO signaling

pathway 0.0016

4.91E-

02 3

ACKNOWLEDGEMENTS

We would like to thank Ian Davis, W. Kimryn

Rathmell, Kathryn E. Hacker and Jeremy M. Simon

for assistance with genotyping of tissue. We would

like to thank Amylou Dueck for advice regarding

statistical analysis of preliminary studies.

The results published here are in whole or part based

upon data generated by The Cancer Genome Atlas

managed by the NCI and NHGRI. Information about

TCGA can be found at http://cancergenome.nih.gov.

FINANCIAL SUPPORT

T.H.H. is supported by funding from the ASCO

Young Investigator Award from the Kidney Cancer

Association, the Action to Cure Kidney Cancer

Organization, the MD Anderson Hematology-

Oncology Fellowship, a Mayo Clinic CR5 grant,

Mayo Clinic Center for Individualized Medicine

Epigenomics Translational Program and a Kathryn

H. and Roger Penske Career Development Award to

Support Medical Research. This work is supported

in part by the Mayo Clinic Center for Individualized

Medicine Epigenomics Translational Program

REFERENCES

1000Genomes. Accessed November, 2014. Available

from: http://www.1000genomes.org/data#DataAccess.

Ast, G., 2004. How did alternative splicing evolve?, Nat

Rev Genet, vol. 5, no. 10, pp. 773-782.

Athanasiadis, A, Rich, A & Maas, S., 2004. Widespread

A-to-I RNA editing of Alu-containing mRNAs in the

human transcriptome. , PLoS Biol, vol. 2, p. e391.

Bazak, L, Haviv, A, Barak, M, Jacob-Hirsch, J, Deng, P,

Zhang, R, Isaacs, FJ, Rechavi, G, Li, JB, Eisenberg, E

& Levanon, EY., 2014. A-to-I RNA editing occurs at

over a hundred million genomic sites, located in a

majority of human genes, Genome Res., vol. 24, pp.

365-376.

Bioconductor Software Packages. Accessed November,

2014. Available from :

<master.bioconductor.org/packages/release/bioc.

Champion, MD., Accessed November, 2014. ChIP-RNA-

seqPRO: A strategy for identifying regions of

epigenetic deregulation associated with aberrant

transcript splicing and RNA-editing sites. Available

from:

<http://sourceforge.net/projects/chiprnaseqpro/>.

Chen, L, Li, Y, Lin, CH, Chan, TH, Chow, RK, Song, Y,

Liu, M, Yuan, YF, Fu, L, Kong, KL, Qi, L, Li, Y &

Zhang N, TA, Kwong DL, Man K, Lo CM, Lok S,

Tenen DG, Guan XY., 2013. Recoding RNA editing

of AZIN1 predisposes to hepatocellular carcinoma.,

Nature Medicine, vol. 19, no. 2, pp. 209-16.

Choudhury, Y, Tay, FC, Lam, DH, Sandanaraj, E, Tang,

C, Ang, BT & Wang, S., 2012. Attenuated adenosine-

to-inosine editing of microRNA-376a* promotes

invasiveness of glioblastoma cells., J Clin Invest, vol.

122, no. 11, pp. 4059-76.

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

168

Cieślik, M & Bekiranov, S., 2014. Combinatorial

epigenetic patterns as quantitative predictors of

chromatin biology., BMC Genomics, vol. 15, p. 76.

The ENCODE Project Consortium, 2011. A users guide to

the encyclopedia of DNA elements (ENCODE)., PLoS

Biol, vol. 9, no. 4, p. e1001046.

dbSNP. version 137. Accessed November, 2014.

Available from: <http://www.ncbi.nlm.nih.gov/snp/>

ENCODE. DataStandards. Available from: <https://

genome.ucsc.edu/encode/protocols/dataStandards/>.

ESP6500. Accessed November, 2014. Available from:

<evs.gs.washington.edu/EVS/>.

Eswaran, J, Horvath, A, Godbole, S, Reddy, SD, Mudvari,

P, Ohshiro, K, Cyanam, D, Nair, S , Fuqua, SAW,

Polyak, K, Florea, LD, Kumar, R., 2013. RNA

sequencing of cancer reveals novel splicing

alterations, Scientific Reports, vol. 3, p. 1689.

Feinberg, AP, Ohlsson, R & Henikoff, S., 2006. The

epigenetic progenitor origin of human cancer, Nature,

vol. 7, pp. 21-33.

Feschotte, C., 2008. Transposable elements and the

evolution of regulatory networks., Nat Rev Genet, vol.

9, pp. 397-405.

Frischmeyer, PA & Dietz, HC., 1999. Nonsense-mediated

mRNA decay in health and disease., Hum Mol Genet,

vol. 8, no. 10, pp. 1893-900.

Gardner, LB., 2011. Nonsense mediated RNA decay

regulation by cellular stress; implications for

tumorigenesis, Mol Cancer Res, vol. 8, no. 3, pp. 295-

308.

HapMap. Accessed November, 2014. Available from:

<http://hapmap.ncbi.nlm.nih.gov/>

Huda, A, Mariño-Ramírez, L & Jordan, IK., 2010.

Epigenetic histone modifications of human

transposable elements: genome defense versus

exaptation., Mob DNA. , vol. 1, no. 1.

Illumina. Infinium Human Methylation450 Bead Chip.

Accessed November, 2014. Available from:

<http://res.illumina.com/documents/products/datasheet

s/datasheet_humanmethylation450.pdf>.

The Broad Institute of MIT and Harvard. GATK Best

Practices. Available from: <https://

www.broadinstitute.org/gatk/guide/best-practices>

Jiang, H & Wong, WH., 2009. Statistical inferences for

isoform expression in RNA-Seq, Bioinformatics, vol.

25, no. 8, pp. 1026-1032.

Jin, B, Ernst, J, Tiedemann, RL, Xu, H, Sureshchandra, S,

Kellis, M, Dalton, S, Liu, C, Choi, JH & Robertson,

KD., 2012. Linking DNA methyltransferases to

epigenetic marks and nucleosome structure genome-

wide in human tumor cells., Cell Rep, vol. 2, no. 5, pp.

1411-24.

Kidwell, MG & Lisch, DR., 2000. Transposable elements

and host genome evolution, Trends Ecol Evol, vol. 15,

pp. 95-99.

Kim, DD, Kim, TT, Walsh, T, Kobayashi, Y, Matise, TC,

Buyske, S & Gabriel, A., 2004. Widespread RNA

editing of embedded alu elements in the human

transcriptome., Genome Res., vol. 14, no. 9, pp. 1719-

25.

Kiran, A & Baranov, PV., 2010. DARNED: a DAtabase of

RNa EDiting in humans., Bioinformatics, vol. 26, no.

14, pp. 1772-6.

Landt, SG, Marinov, GK, Kundaje, A, Kheradpour, P,

Pauli, F, Batzoglou, S, Bernstein, BE, Bickel, P,

Brown, JB & Cayting P, CY, DeSalvo G, Epstein C,

Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J,

Hartemink AJ, Hoffman MM, Iyer VR, Jung YL,

Karmakar S, Kellis M, Kharchenko PV, Li Q, Liu T,

Liu XS, Ma L, Milosavljevic A, Myers RM, Park PJ,

Pazin MJ, Perry MD, Raha D, Reddy TE, Rozowsky J,

Shoresh N, Sidow A, Slattery M, Stamatoyannopoulos

JA, Tolstorukov MY, White KP, Xi S, Farnham PJ,

Lieb JD, Wold BJ, Snyder M., 2012. ChIP-seq

guidelines and practices of the ENCODE and

modENCODE consortia., Genome Res., vol. 22, no. 9,

pp. 1813-31.

Levanon, E, Eisenberg, Y, Yelin, E, Nemzer, R,

Hallengger, M, Shemesh, R, Fligelman, ZY, Shoshan,

A, Pollock, SR & Sztybel, D., 2004. Systematic

identification of abundant A-to-I editing sites in the

human transcriptome, Nat Biotechnol, vol. 22, pp.

1001-1005.

Li, B, Ruotti, V, Stewart, RM, Thomson, JA & Dewey,

CN., 2010. RNA-Seq gene expression estimation with

read mapping uncertainty, Bioinformatics, vol. 26, no.

4, pp. 493-500.

Li, JB, Levanon, EY, Yoon, JK, Aach, J, Xie, B, Leproust,

E, Zhang, K, Gao, Y & Church, GM., 2009. Genome-

wide identification of human RNA editing sites by

parallel DNA capturing and sequencing., Science, vol.

324, no. 5931, pp. 1210-3.

Luco, RF, Pan, Q, Tominaga, K, Blencowe, BJ, Pereira-

Smith, OM & Misteli, T., 2010. Regulation of

alternative splicing by histone modifications., Science,

vol. 327, no. 5968, pp. 996-1000.

Makalowski, W, Mitchell, GA & Labuda, D., 1994. Alu

sequences in the coding regions of mRNA: a source of

protein

variability. , Trends Genet, vol. 10, pp. 188-193.

Maksimovic, J, Gordon, L & Oshlack, A., 2012. SWAN:

Subset-quantile within array normalization for

illumina infinium HumanMethylation450 BeadChips.,

Genome Biol, vol. 13, no. 6, p. R44.

Nicolae, M, Mangul, S, Măndoiu, II & Zelikovsky, A.,

2011. Estimation of alternative splicing isoform

frequencies from RNA-Seq data., Algorithms Mol

Biol, vol. 6, no. 1, p. 9.

Paz, N, Levanon, EY, Amariglio, N, Heimberger, AB,

Ram, Z, Constantini, S, Barbash, ZS, Adamsky, K,

Safran, M, Hirschberg, A, Krupsky, M, Ben-Dov, I,

Cazacu, S, Mikkelsen, T, Brodie, C, Eisenberg, E &

Rechavi, G., 2007. Altered adenosine-to-inosine RNA

editing in human cancer., Genome Res., vol. 17, no.

11, pp. 1586-95.

Pepke, S, Wold, BJ & Mortazavi, A., 2014. Computation

for ChIP-seq and RNA-seq studies, Nat Methods, vol.

6, no. 11, pp. S22-S32.

Ramaswami, G & Li, JB., 2014. RADAR: a rigorously

annotated database of A-to-I RNA editing, Nucleic

BioinformaticsStrategiesforIdentifyingRegionsofEpigeneticDeregulationAssociatedwithAberrantTranscriptSplicing

andRNA-editing

169

Acids Res. , vol. 42, no. (Database Issue), pp. D109-

13.

Rueter, SM, Dawson, TR & Emeson, RB., 1999.

Regulation of alternative splicing by RNA editing,

Nature, vol. 399, pp. 75-80.

Simon, JM, Hacker, KE, Singh, D, Brannon, AR, Parker,

JS, Weiser, M, Ho, TH, Kuan, PF, Jonasch, E, Furey,

TS, Prins, JF, J.D., L, Rathmell, WK & Davis, IJ.,

2014. Variation in chromatin accessibility in human

kidney cancer links H3K36 methyltransferase loss

with widespread RNA processing defects., Genome

Res., vol. 24, no. 2, pp. 241-50.

R Development Core Team. R: A language and

environment for statistical computing. Available from:

<http://www.r-project.org>

Trapnell, C, Williams, BA, Pertea, G, Mortazavi, A,

Kwan, G, van Baren, MJ, Salzberg, SL, Wold, BJ &

Pachter, L., 2010. Transcript assembly and

quantification by RNA-Seq reveals unannotated

transcripts and isoform switching during cell

differentiation., Nat Biotechnol, vol. 28, no. 5, pp.

511-5.

Veloso, A, Kirkconnell, KS, Magnuson, B, Biewen, B,

Paulsen, MT, Wilson, TE & Ljungman, M., 2014.

Rate of elongation by RNA polymerase II is associated

with specific gene features and epigenetic

modifications, Genome Res., vol. 24, no. 6, pp. 896-

905.

Wang, C, Davila, JI, Baheti, S, Bhagwate, AV, Wang, X,

Kocher, JP, Slager, SL, Feldman, AL, Novak, AJ,

Cerhan, JR, Thompson, EA & Asmann, YW., 2014.

RVboost: RNA-Seq variants prioritization using a

boosting method., Bioinformatics.

Wang, IX, So, E, Devlin, JL, Zhao, Y, Wu, M & Cheung,

VG., 2013. ADAR regulates RNA editing, transcript

stability, and gene expression., Cell Rep, vol. 5, no. 3,

pp. 849-60.

Zang, C, Schones, DE, Zeng, C, Cui, K, Zhao, K & Peng,

W., 2009. A clustering approach for identification of

enriched domains from histone modification ChIP-Seq

data, Bioinformatics, vol. 25, pp. 1952-1958.

Zhao, Q, Caballero, OL, Davis, ID, Jonasch, E, Tamboli,

P, Yung, WK, Weinstein, JN & Yao, J., 2013. Tumor-

specific isoform switch of the fibroblast growth factor

receptor 2 underlies the mesenchymal and malignant

phenotypes of clear cell renal cell carcinomas., Clin

Cancer Res., vol. 19, no. 9, pp. 2460-72.

Zhao, S, Fung-Leung, W-P, Bittner, A, Ngo, K & Liu, X.,

2014. Comparison of RNA-Seq and Microarray in

Transcriptome Profiling of Activated T Cells, PLoS

One, vol. 9, no. 1, p. e78644.

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