Examining gene expression and methylation with next gen sequencing

GENETIC
ANALYSIS
of Complex Human Diseases
Examining Gene Expression and
Methylation with Next-Gen
Sequencing
Stephen Turner, Ph.D.
Bioinformatics Core Director
bioinformatics.virginia.edu
University of Virginia

GENETIC
ANALYSIS
Gene expression pre-2008
PCR Microarrays

GENETIC
ANALYSIS

GENETIC
ANALYSIS
Advantages of RNA-Seq
n  No reference necessary
n  Low background (no cross-hybridization)
n  Unlimited dynamic range (FC 9000 Science
320:1344)
n  Direct counting (microarrays: indirect – hybridization)
n  Can characterize full transcriptome
u mRNA and ncRNA (miRNA, lncRNA, snoRNA, etc)
u Differential gene expression
u Differential coding output
u Differential TSS usage
u Differential isoform expression

GENETIC
ANALYSIS
Isoform level data

GENETIC
ANALYSIS
Differential splicing & TSS use

GENETIC
ANALYSIS
Is it accurate?
n  Marioni et al. RNA-seq: An assessment of technical reproducibility and
comparison with gene expression arrays. Genome Research 2008
18:1509.

GENETIC
ANALYSIS
RNA-Seq Challenges
n  Library construction
u  Size selection (messenger, small)
u  Strand specificity?
n  Bioinformatic challenges
u  Spliced alignment
u  Transcript deconvolution
n  Statistical Challenges
u  Highly variable abundance
u  Sample size: never, ever, plan n=1
u  Normalization (RPKM)
►  More reads from longer transcripts,
higher sequencing depth
►  Want to compare features of different
lengths
►  Want to compare conditions with
different total sequence depth

GENETIC
ANALYSIS
RNA-Seq Overview
Condi&on
1

(normal
colon)

Condi&on
2

(colon
tumor)

Samples
of
interest

AAAAA mRNA
AAAAA
mRNA
TTTTT
Library
@HWUSI-EAS100R:6:73:941:1973#0/1
GATTTGGGGTTCAAAGCAGTATCGATCAAATA
+HWUSI-EAS100R:6:73:941:1973#0/1
!''*((((***+))%%%++)(%%%%).1***-
@HWUSI-EAS100R:6:73:941:1973#0/1
CATCGACGTAGATCGACTACATGAACTGCTCG
+HWUSI-EAS100R:6:73:941:1973#0/1
!'’*+(*+!+(*!+*(((***!%%%%!%%(+-

GENETIC
ANALYSIS
Common question #1: Depth
n  Question: how much sequence do I need?
n  Answer: it’s complicated.
n  Oversimplified answer: 20-50 million PE reads / sample
(mouse/human).
n  Depends on:
u  Size & complexity of transcriptome
u  Application: differential gene expression, transcript
discovery
u  Tissue type, RNA quality, library preparation
u  Sequencing type: length, paired-end vs single-end, etc.
n  Find a publication in your field with similar goals.
n  Good news: ¼ HiSeq lane usually sufficient.

GENETIC
ANALYSIS
Common question #2: Sample Size
n Question: How many samples should I
sequence?
n Oversimplified Answer: At least 3 biological
replicates per condition.
n Depends on:
u Sequencing depth
u Application
u Goals (prioritization, biomarker discovery, etc.)
u Effect size, desired power, statistical significance
n Find a publication with similar goals

GENETIC
ANALYSIS
Common question #3: Workflow
n  How do I analyze the data?
n  No standards!
u  Unspliced aligners: BWA, Bowtie, Bowtie2, MANY others!
u  Spliced aligners: STAR, Rum, Tophat, Tophat2-Bowtie1, Tophat2-Bowtie2,
GSNAP, MANY others.
u  Reference builds & annotations: UCSC, Entrez, Ensembl
u  Assembly: Cufflinks, Scripture, Trinity, G.Mor.Se, Velvet, TransABySS
u  Quantification: Cufflinks, RSEM, eXpress, MISO, etc.
u  Differential expression: Cuffdiff, Cuffdiff2, DegSeq, DESeq, EdgeR, Myrna
n  Like early microarray days: lots of excitement, lots of tools, little
knowledge of integrating tools in pipeline!
n  Benchmarks
u  Microarray: Spike-ins (Irizarry)
u  RNA-Seq: ???, simulation, ???

GENETIC
ANALYSIS
Common question #3: Workflow
Eyras et al. Methods to Study Splicing from RNA-Seq.
http://dx.doi.org/10.6084/m9.figshare.679993
Turner SD. RNA-seq Workflows and Tools.
http://dx.doi.org/10.6084/m9.figshare.662782

GENETIC
ANALYSIS
Phases
of
NGS
Analysis

n  Primary

u  Conversion
of
raw
machine
signal
into
sequence
and
quali8es

n  secondary

u  Alignment
of
reads
to
reference
genome
or
transcriptome

u  or
de
novo
assembly
of
reads
into
con8gs

n  Ter8ary

u  SNP
discovery/genotyping

u  Peak
discovery/quan8fica8on
(ChIP,
MeDIP)

u  Transcript
assembly/quan8fica8on
(RNA-‐seq)

n  Quaternary

u  Differen8al
expression

u  Enrichment,
pathways,
correla8on,
clustering,
visualiza8on,
etc.

u  hKp://geMnggene8csdone.blogspot.com/2012/03/pathway-‐analysis-‐for-‐high-‐throughput.html

u  hKp://www.slideshare.net/turnersd/pathway-‐analysis-‐2012-‐17947529

GENETIC
ANALYSIS
Primary
Analysis:
Get
FASTQ
ﬁle

@HWUSI-EAS100R:6:73:941:1973#0/1
GATTTGGGGTTCAAAGCAGTATCGATCAAATA
+HWUSI-EAS100R:6:73:941:1973#0/1
!''*((((***+))%%%++)(%%%%).1***-

GENETIC
ANALYSIS
“Phred-‐scaled”
base
quali&es

#
$p
is
probability
base
is
erroneous

$Q
=
-‐10
*
log($p)
/
log(10);
#
Phred
Q

$q
=
chr(($Q<=40?
$Q
:
40)
+
33);
#
FASTQ
quality
character

$Q
=
ord($q)
-‐
33;
#
33
oﬀset

SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.....................................................
...............................IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII......................
..........................XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_`abcdefghijklmnopqrstuvwxyz{|}~
| | | | | |
33 59 64 73 104 126
S - Sanger Phred+33, 41 values (0, 40)
I - Illumina 1.3 Phred+64, 41 values (0, 40)
X - Solexa Solexa+64, 68 values (-5, 62)

GENETIC
ANALYSIS
Secondary
analysis

n Alignment
back
to
the
reference

u Computa8onally
demanding
–
can’t
use
BLAST

u Many
algorithms
(Maq,
BWA,
bow8e,
bow8e2,

Mosaik,
NovoAlign,
SOAP2,
SSAHA,
…)

u  hKp://en.wikipedia.org/wiki/List_of_sequence_alignment_sokware

u Sensi8vity
to
sequencing
errors,
polymorphisms,

indels,
rearrangements

u Tradeoﬀs
in
8me
vs.
memory
vs.
performance

GENETIC
ANALYSIS
RNA-Seq Workflow 1: Differential
Gene Expression

GENETIC
ANALYSIS
RNA-Seq Workflow 2: Differential
Isoform Expression, Exon Usage

GENETIC
ANALYSIS
Download data & software
n  Public data from GEO. E.g. GSE32038
u  http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE32038
u  Trapnell et al. Differential gene and transcript expression analysis of RNA-seq
experiments with TopHat and Cufflinks. Nature Protocols 2012: 7:562.
n  Sequence, annotation, indexes (Ensembl)
u  iGenomes: http://tophat.cbcb.umd.edu/igenomes.html
u  Genes: /Annotation/Genes/genes.gtf
u  Indexes: /Sequence/BowtieIndex/genome.*
n  Software:
u  Samtools: http://samtools.sourceforge.net/
u  FastQC: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
u  Bowtie: http://bowtie-bio.sourceforge.net/index.shtml
u  Tophat: http://tophat.cbcb.umd.edu/
u  HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html
u  R: http://www.r-project.org/
u  DESeq2: http://www.bioconductor.org/packages/2.12/bioc/html/DESeq2.html
u  Cufflinks: http://cufflinks.cbcb.umd.edu/
u  cummeRbund: http://compbio.mit.edu/cummeRbund/

GENETIC
ANALYSIS
Do some quality assessment
Software:
Picard picard.sourceforge.net
FastQC bioinformatics.bbsrc.ac.uk/projects/fastqc
RSeQC code.google.com/p/rseqc
FastX Toolkit hannonlab.cshl.edu/fastx_toolkit
R/ShortRead bioconductor.org/packages/bioc/html/ShortRead.html

GENETIC
ANALYSIS
Mapping across splice junctions: tophat
1.  Map reads to genome
2.  Collect unmappable reads
3.  Break reads into segments. Small
segments often independently
align. If align 100bp-kbs apart,
infer splice.
tophat –G genes.gtf –o C1_R1_tophatout /path/bowtieindex/genome C1_R1_1.fq C1_R1_2.fq
Gene
Annotation Output Directory Bowtie Index Read 1 Read 2

GENETIC
ANALYSIS
Workflow 1: Differential Gene Expression
Step 1: Align to Genome
Step 2: Count Reads overlapping genes
Step 3: Differential expression

GENETIC
ANALYSIS
Software: HTSeq
http://www-huber.embl.de/users/anders/HTSeq
Run htseq-count on each of the
alignments:
htseq-count <sam_file> <gtf_file>
First convert binary .bam file to
text .sam file using samtools:
samtools view accepted_hits.bam > C1_R1.sam

GENETIC
ANALYSIS
Software: DESeq2
http://www.bioconductor.org/packages/2.12/bioc/html/DESeq2.html
> library(DESeq2)
> sampleFiles <- c("C1_R1.counts.txt", "C1_R2.counts.txt", "C1_R3.counts.txt",
"C2_R1.counts.txt", "C2_R2.counts.txt", "C2_R3.counts.txt")
> sampleCondition <- factor(substr(sampleFiles, 1, 2))
> sampleTable <- data.frame(sampleName=sampleFiles, fileName=sampleFiles,
condition=sampleCondition)
> sampleTable
sampleName fileName condition
1 C1_R1.counts.txt C1_R1.counts.txt C1
dds <- DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=".",
design=~condition)
dds <- DESeq(dds)
results <- results(dds)
results <- results[order(results$FDR), ]
plotMA(dds)
...

GENETIC
ANALYSIS
Changes in fragment count for a
gene does not necessarily equal a
change in expression.
Trapnell, Cole, et al. "Differential analysis of gene regulation at transcript resolution with RNA-seq." Nature biotechnology 31.1 (2012): 46-53.

GENETIC
ANALYSIS
Workflow 2a: Assemble transcripts
for each sample: cufflinks
n Cufflinks
u Identifies mutually
incompatible
fragments
u Identify minimal set
of transcripts to
explain all the
fragments.
cufflinks -o C1_R1_cufflinksout C1_R1_tophatout/accepted_hits.bam
Output Directory Path to alignment

GENETIC
ANALYSIS
Merge assemblies: cuffmerge
n  Merge assemblies to create single merged transcriptome
annotation.
u  Option 1: Pool alignments and assemble all at once.
►  Computationally demanding
►  Assembler will be faced complex mixture of isoforms à more error
u  Option 2: Assemble alignments individually, merge resulting
assemblies
►  Cuffmerge: meta-assembler using parsimony.
►  Genes with low expression à insufficient coverage for reconstruction.
►  Merging often recovers complete gene.
►  Newly discovered isoforms integrated w/ known ones (RABT).

GENETIC
ANALYSIS
Merge assemblies: cuffmerge
n Create “manifest” of location of all assemblies
n Run Cuffmerge on assemblies using RABT
cuffmerge –g /path/to/annotation/genes.gtf –s /path/to/refgenome/genome.fa assemblies.txt
Reference Gene Annotation
./C1_R1_cufflinksout/transcripts.gtf
Assemblies.txt: location of assemblies
Reference Genome Sequence Manifest from above

GENETIC
ANALYSIS
Differential expression: cuffdiff
n Identify differentially expressed genes &
transcripts
cuffdiff –o cuffdiff_out –b genome.fa –u merged.gtf
./C1_R1_tophatout/accepted_hits.bam,
./C1_R3_tophatout/accepted_hits.bam
./C2_R3_tophatout/accepted_hits.bam
Reference
Sequence
Output
directory
Merged
assembly
Location of
alignments
•  1 gene
•  2 TSS
•  2 CDS
•  3 Isoforms

GENETIC
ANALYSIS
Downstream analysis &
visualization

GENETIC
ANALYSIS
Visualization with cummeRbund
n Install cummeRbund:
u Install from BioConductor:
►  source("http://bioconductor.org/biocLite.R")
►  biocLite("cummeRbund")
u Download and install latest version from
http://compbio.mit.edu/cummeRbund/
n Load the package
u library(cummeRbund)
n Read in the data
u  cuff <- readCufflinks(“/path/to/cuffdiff/output”)

GENETIC
ANALYSIS
csDensity(genes(cuff))
csBoxplot(genes(cuff))
csScatter(genes(cuff), "C1", "C2", smooth=T)
csVolcano(genes(cuff), "C1", "C2")

GENETIC
ANALYSIS
mygene2 <- getGene(cuff, "Rala")
expressionBarplot(mygene2)
expressionBarplot(isoforms(mygene2))

GENETIC
ANALYSIS
DEXSeq
n  Differential Gene Expression (E.g.
DESeq)
n  Differential Isoform Expression
(E.g. Cufflinks)
n  Differential Exon Usage
n  What’s different about DEXSeq?
u  Doesn’t do full transcript
assembly (Cufflinks)
u  Doesn’t count fragments
mapping to genes (DESeq)
u  Avoids assembly and looks for
differences in reads mapping to
individual exons.
u  Uses counts (negative binomial)

GENETIC
ANALYSIS
Using DEXSeq: Installation
n Installation & load:
u  source("http://bioconductor.org/biocLite.R")
u  biocLite(“DEXSeq”)
u  library(DEXSeq)
n Installation comes bundled with useful python
scripts in the python_scripts directory of the
library. Put these in your PATH.

GENETIC
ANALYSIS
Using DEXSeq: Data preparation
n First, prepare “flattened” GFF:
n Create sorted SAM files
n Count reads overlapping counting bins
dexseq_prepare_annotation.py input.gtf exons.gff
Reference
AnnotationScript comes with DEXSeq
samtools view C1_R1-tophat-out/accepted_hits.bam | sort –k 1,1 –k2,2n > C1_R1.sam
dexseq_count.py -p no -s no exons.gff C1_R1.sam C1_R1.counts.txt
Script comes with DEXSeq
Flattened
Annotation Alignment Output file
Output file

GENETIC
ANALYSIS
Using DEXSeq: Data import
n  The pasilla package vignette gives detailed
instructions on how to do this:
http://www.bioconductor.org/packages/release/data/experiment/html/pasilla.html
> design <- data.frame(condition=c(rep("C1",3), rep("C2",3)), replicate=rep(1:3,2))
> rownames(design) <- with(design, paste(condition, "_R", replicate, sep=""))
> design
condition replicate
C1_R1 C1 1
C1_R2 C1 2
C1_R3 C1 3
C2_R1 C2 1
C2_R2 C2 2
C2_R3 C2 3
> countfiles <- file.path(".", paste(rownames(design), ".counts.txt", sep=""))
> countfiles
[1] "./C1_R1.counts.txt" "./C1_R2.counts.txt" "./C1_R3.counts.txt" "./C2_R1.counts.txt"
[5] "./C2_R2.counts.txt" "./C2_R3.counts.txt"
> flattenedfile <- "/Users/sdt5z/smb/u/genomes/dexseq/exons_dme_ens_bdgp525.gff"
> exons <- read.HTSeqCounts(countfiles=countfiles, design=design,
flattenedfile=flattenedfile)
> sampleNames(exons) <- rownames(design)

GENETIC
ANALYSIS
Using DEXSeq: Data Analysis
# Estimate size factors (normalizes for sequencing depth)
exons <- estimateSizeFactors(exons)
sizeFactors(exons)
# Estimate dispersion
exons <- estimateDispersions(exons)
exons <- fitDispersionFunction(exons)
# Test for Differential Exon Usage
exons <- testForDEU(exons)
exons <- estimatelog2FoldChanges(exons)
result <- DEUresultTable(exons)
# How many are significant at FDR 0.001?
table(res$padjust<0.0001)
# M vs A plot
plot(result$meanBase, result[, "log2fold(C2/C1)"], log="x”)

GENETIC
ANALYSIS
Using DEXSeq: visualization
plotDEXSeq(exons, "FBgn0030362", cex.axis=1.2, cex=1.3, lwd=2, legend=T, displayTranscripts=T)

GENETIC
ANALYSIS
Using DEXSeq: HTML Report
library(biomaRt)
mart <- useMart("ensembl", dataset="dmelanogaster_gene_ensembl")
listAttributes(mart)[1:25,]
attributes <- c("ensembl_gene_id", "external_gene_id", "description")
DEXSeqHTML(exons, FDR=0.0001, mart=mart, filter="ensembl_gene_id", attributes=attributes)

GENETIC
ANALYSIS
Downstream analysis
n  Now you have a list of:
u Genes
u Isoforms (genes)
u Exons (genes)
n  How to place in functional context?
n  Pathway / functional analysis!
u Gene Ontology over-representation
u Gene Set Enrichment Analysis
u Signaling Pathway Impact Analysis
u Many more…
n  Resources:
u  hKp://geMnggene8csdone.blogspot.com/2012/03/pathway-‐analysis-‐for-‐high-‐throughput.html

u  hKp://www.slideshare.net/turnersd/pathway-‐analysis-‐2012-‐17947529

GENETIC
ANALYSIS
Workflow Management: Galaxy
n http:usegalaxy.org

GENETIC
ANALYSIS
Workflow Management: Taverna
n  Taverna: http://www.taverna.org.uk/
n  TavernaPBS: http://sourceforge.net/projects/tavernapbs/

GENETIC
ANALYSIS
Further Reading
n  RNA-Seq:
u  Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011). Computational methods for transcriptome annotation and
quantification using RNA-seq. Nature methods, 8(6), 469-77.
u  Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008). RNA-seq: an assessment of technical
reproducibility and comparison with gene expression arrays. Genome research, 18(9), 1509-17.
u  Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian
transcriptomes by RNA-Seq. Nature methods, 5(7), 621-8.
u  Ozsolak, F., & Milos, P. M. (2011). RNA sequencing: advances, challenges and opportunities. Nature reviews. Genetics,
12(2), 87-98.
u  Toung, J. M., Morley, M., Li, M., & Cheung, V. G. (2011). RNA-sequence analysis of human B-cells. Genome research,
991-998.
u  Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics,
10(1), 57-63.
n  Bowtie/Tophat:
u  Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA
sequences to the human genome. Genome biology, 10(3), R25.
u  Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (Oxford,
England), 25(9), 1105-11.
n  Cufflinks:
u  Roberts, A., Pimentel, H., Trapnell, C., & Pachter, L. (2011). Identification of novel transcripts in annotated genomes using
RNA-Seq. Bioinformatics (Oxford, England), 27(17), 2325-9.
u  Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., Pimentel, H., et al. (2012). Differential gene and transcript
expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 7(3), 562-578.
u  Trapnell, C., Williams, B. a, Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., et al. (2010). Transcript
assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell
differentiation. Nature biotechnology, 28(5), 511-5.
n  DEXSeq:
u  Vignette: http://watson.nci.nih.gov/bioc_mirror/packages/2.9/bioc/html/DEXSeq.html.
u  Pre-pub manuscript: Anders, S., Reyes, A., Huber, W. (2012). Detecting differential usage of exons from RNA-Seq data.
Nautre Precedings, DOI: 10.1038/npre.2012.6837.2.

GENETIC
ANALYSIS
Online Community Forum and
Discussion
n Seqanswers
u  http://SEQanswers.com
u  Format: Forum
u  Li et al. SEQanswers : An open access community for
collaboratively decoding genomes. Bioinformatics (2012).
n BioStar:
u  http://biostar.stackexchange.com
u  Format: Q&A
u  Parnell et al. BioStar: an online question & answer resource for
the bioinformatics community. PLoS Comp Bio (2011).
n  Other Bioinformatics Resources: stephenturner.us/p/edu

GENETIC
ANALYSIS
DNA Methylation: Importance
n Occurs most frequently at CpG sites
n High methylation at promoters ≈ silencing
n Methylation perturbed in cancer
n Methylation associated with many other complex
diseases: neural, autoimmune, response to env.
n Mapping DNA methylation à new disease
genes & drug targets.

GENETIC
ANALYSIS
DNA Methylation: Challenges
n Dynamic and tissue-specific
n DNA à Collection of cells which vary in 5meC
patterns à 5meC pattern is complex.
n Further, uneven distribution of CpG targets
n Multiple classes of methods:
u Bisulfite, sequence-based: Assay methylated
target sequences across individual DNAs.
u Affinity enrichment, count-based: Assay
methylation level across many genomic loci.

GENETIC
ANALYSIS
DNA Methylation: Mapping
BS-Seq Whole-genome bisulfite sequencing
RRBS-Seq Reduced representation bisulfite sequencing
BC-Seq Bisulfite capture sequencing
BSPP Bisulfite specific padlock probes
Methyl-Seq Restriction enzyme based methyl-seq
MSCC Methyl sensitive cut counting
HELP-Seq HpaII fragment enrichment by ligation PCR
MCA-Seq Methylated CpG island amplification
MeDIP-Seq Methylated DNA immunoprecipitation
MBP-Seq Methyl-binding protein sequencing
MethylCap-seq Methylated DNA capture by affinity purification
MIRA-Seq Methylated CpG island recovery assay
RNA-Seq High-throughput cDNA sequencing
DNA
Methylation
Gene
Expression

GENETIC
ANALYSIS
Methylation: REs and PCR
n Restriction enzyme digest
u Isoschizomers HpaII and MspI both recognize
same sequence: 5’-CCGG-3’
u MspI digests regardless of methylation
u HpaII only digests at unmethylated sites
n PCR à gel electrophoresis à southern blot
n Pros: Highly sensitive
n Cons: Low-throughput, high false positive rate
because of incomplete digestion (for reasons
other than methylation).

GENETIC
ANALYSIS
Bisulfite sequencing
n  Sodium bisulfite converts unmethylated (but not methylated) C’s into U’s.
n  This introduces a methylation-specific “SNP”.
n  RRBS – library enriched for CpG-dense regions by digesting with MspI.

GENETIC
ANALYSIS
MeDIP-Seq
n MeDIP-Seq = Methylated
DNA immunoprecipitation
n Uses antibody against 5-
methylcytosine to retrieve
methylated fragments from
sonicated DNA.
n Enrichment method = count
number of reads

GENETIC
ANALYSIS
MethylCap-Seq
n Uses methyl-binding domain (MBD) protein to
obtain DNA with similar methylation levels.
n Also a counting method.

GENETIC
ANALYSIS
Methylation: Accuracy
n  Bock et al. Quantitative
comparison of genome-wide DNA
methylation mapping technologies.
Nature biotechnology, 28(10),
1106-14.
n  MeDIP, MethylCap, RRBS largely
concordant with Illumina Infinium
assay

GENETIC
ANALYSIS
Methylation methods: coverage
n Coverage varies among different methods

GENETIC
ANALYSIS
Methylation: Features & Biases

GENETIC
ANALYSIS
Methylation: Bioinformatics Resources
Resource
Purpose
URL
Refs

Batman
MeDIP
DNA
methyla8on
analysis
tool
hKp://td-‐blade.gurdon.cam.ac.uk/sokware/batman

BDPC
DNA
methyla8on
analysis
plalorm
hKp://biochem.jacobs-‐university.de/BDPC

BSMAP
Whole-‐genome
bisulphite
sequence
mapping
hKp://code.google.com/p/bsmap

CpG
Analyzer
Windows-‐based
program
for
bisulphite
DNA
-‐

CpGcluster
CpG
island
iden8fica8on
hKp://bioinfo2.ugr.es/CpGcluster

CpGFinder
Online
program
for
CpG
island
iden8fica8on
hKp://linux1.sokberry.com

CpG
Island
Explorer
Online
program
for
CpG
Island
iden8fica8on
hKp://bioinfo.hku.hk/cpgieintro.html

CpG
Island
Searcher
Online
program
for
CpG
Island
iden8fica8on
hKp://cpgislands.usc.edu

CpG
PaKernFinder
Windows-‐based
program
for
bisulphite
DNA
-‐

CpG
Promoter
Large-‐scale
promoter
mapping
using
CpG
islands
hKp://www.cshl.edu/OTT/html/cpg_promoter.html

CpG
ra8o
and
GC
content
PloKer
Online
program
for
ploMng
the
observed:expected
ra8o
of
CpG
hKp://mwsross.bms.ed.ac.uk/public/cgi-‐bin/cpg.pl

CpGviewer
Bisulphite
DNA
sequencing
viewer
hKp://dna.leeds.ac.uk/cpgviewer

CyMATE
Bisulphite-‐based
analysis
of
plant
genomic
DNA
hKp://www.gmi.oeaw.ac.at/en/cymate-‐index/

EMBOSS
CpGPlot/
CpGReport
Online
program
for
ploMng
CpG-‐rich
regions
hKp://www.ebi.ac.uk/Tools/emboss/cpgplot/index.html

Epigenomics
Roadmap
NIH
Epigenomics
Roadmap
Ini8a8ve
homepage
hKp://nihroadmap.nih.gov/epigenomics

Epinexus
DNA
methyla8on
analysis
tools
hKp://epinexus.net/home.html

MEDME
Sokware
package
(using
R)
for
modelling
MeDIP
experimental
data
hKp://espresso.med.yale.edu/medme

methBLAST
Similarity
search
program
for
bisulphite-‐modified
DNA
hKp://medgen.ugent.be/methBLAST

MethDB
Database
for
DNA
methyla8on
data
hKp://www.methdb.de

MethPrimer
Primer
design
for
bisulphite
PCR
hKp://www.urogene.org/methprimer

methPrimerDB
PCR
primers
for
DNA
methyla8on
analysis
hKp://medgen.ugent.be/methprimerdb

MethTools
Bisulphite
sequence
data
analysis
tool
hKp://www.methdb.de

MethyCancer
Database
Database
of
cancer
DNA
methyla8on
data
hKp://methycancer.psych.ac.cn

Methyl
Primer
Express
Primer
design
for
bisulphite
PCR
hKp://www.appliedbiosystems.com/

Methylumi
Bioconductor
pkg
for
DNA
methyla8on
data
from
Illumina
hKp://www.bioconductor.org/packages/bioc/html/

Methylyzer
Bisulphite
DNA
sequence
visualiza8on
tool
hKp://ubio.bioinfo.cnio.es/Methylyzer/main/index.html

mPod
DNA
methyla8on
viewer
integrated
w/
Ensembl
genome
browser
hKp://www.compbio.group.cam.ac.uk/Projects/

PubMeth
Database
of
DNA
methyla8on
literature
hKp://www.pubmeth.org

QUMA
Quan8fica8on
tool
for
methyla8on
analysis
hKp://quma.cdb.riken.jp

TCGA
Data
Portal
Database
of
TCGA
DNA
methyla8on
data
hKp://cancergenome.nih.gov/dataportal

GENETIC
ANALYSIS
Methylation: Further Reading
Bock, C., Tomazou, E. M., Brinkman, A. B., Müller, F., Simmer, F., Gu, H., Jäger, N., et al. (2010). Quantitative
comparison of genome-wide DNA methylation mapping technologies. Nature biotechnology, 28(10), 1106-14.
Brinkman, A. B., Simmer, F., Ma, K., Kaan, A., Zhu, J., & Stunnenberg, H. G. (2010). Whole-genome DNA methylation
profiling using MethylCap-seq. Methods (San Diego, Calif.), 52(3), 232-6.
Brunner, A. L., Johnson, D. S., Kim, S. W., Valouev, A., Reddy, T. E., et al. (2009). Distinct DNA methylation patterns
characterize differentiated human embryonic stem cells and developing human fetal liver, 1044-1056.
Gu, H., Bock, C., Mikkelsen, T. S., Jäger, N., Smith, Z. D., Tomazou, E., Gnirke, A., et al. (2010). Genome-scale DNA
methylation mapping of clinical samples at single-nucleotide resolution. Nature methods, 7(2), 133-6.
Harris, R. A., Wang, T., Coarfa, C., Nagarajan, R. P., Hong, C., Downey, S. L., Johnson, B. E., et al. (2010). Comparison
of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic
modifications. Nature biotechnology, 28(10), 1097-105.
Kerick, M., Fischer, A., & Schweiger, M.-ruth. (2012). Bioinformatics for High Throughput Sequencing. (N.
Rodríguez-Ezpeleta, M. Hackenberg, & A. M. Aransay, Eds.), 151-167. New York, NY: Springer New York.
Laird, P. W. (2010). Principles and challenges of genomewide DNA methylation analysis. Nature reviews. Genetics,
11(3), 191-203.
Weber, M., Davies, J. J., Wittig, D., Oakeley, E. J., Haase, M., Lam, W. L., & Schübeler, D. (2005). Chromosome-wide
and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed
human cells. Nature genetics, 37(8), 853-62. doi:10.1038/ng1598

GENETIC
ANALYSIS
Thank you
Web: bioinformatics.virginia.edu
E-mail: bioinformatics@virginia.edu
Blog: www.GettingGeneticsDone.com
Twitter: twitter.com/genetics_blog

Examining gene expression and methylation with next gen sequencing

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