computational methods for deep characterization of dna methylation blueprints fabian müller...

Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs

Fabian Müller

Keystone Symposium on DNA Methylation

Keystone, CO, USA

April 1, 2015

— Workflow


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Assenov, Müller, Lutsik, Walter, Lengauer, Bock (2014), Nature Methods, 11(11), 1138–1140

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3/16Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs

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— Workflow

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Hematopoietic Methylomes


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Coverage Statistics

• Per sample data quality can be assessed using coverage statistics

• Low quality samples and CpGs can be removed in subsequent filtering steps

2015-04-01Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs

coverage filtered per sample CpG counts

number of CpGs covered

cove

rage

(med

ian)

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Unsupervised Learning


first principal component

seco

nd p

rinci

pal c

ompo

nent

• Dimension reduction methods assess the variability in methylation patterns between samples

• Associations between sample characteristics and variance in methylation patterns can be qualified and quantified

• Single CpGs and genomic regions of interest can be analyzed

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Unsupervised Learning

• Dimension reduction methods assess the variability in methylation patterns between samples

• Associations between sample characteristics and variance in methylation patterns can be qualified and quantified

• Single CpGs and genomic regions of interest can be analyzed

• Clustering identifies subgroups of samples


mos

t var

iabl

e pr

omot

ers

methylationlevel

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Differential DNA Methylation• Differences in DNA methylation

are quantified on the basis of single CpGs and genomic regions of interest

• Ranked lists facilitate prioritization of differences for downstream analysis

• Associations with genomic and epigenomic features can be explored


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tiling (5kb), top100

mean methylation: monocytes

mea

n m

ethy

latio

n: n

eutr

ophi

ls

Differential DNA Methylation


promoters (Gencode15), top100

mean methylation: monocytes

mea

n m

ethy

latio

n: n

eutr

ophi

ls

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• Differences in DNA methylation are quantified on the basis of single CpGs and genomic regions of interest

• Ranked lists facilitate prioritization of differences for downstream analysis

• Associations with genomic and epigenomic features can be explored

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GO Enrichment Analysis



2015-04-01 14promoters hypomethylated in neutrophils

GO ID P-valueOdds-Ratio

GO Term

GO:0050832 0 57.2421 defense response to fungus

GO:0044110 0 54.1897 growth involved in symbiotic interaction

GO:0051852 0 120.3167 disruption by host of symbiont cellsGO:0044364 0 33.8474 disruption of cells of other organismGO:0070488 0 Inf neutrophil aggregation

GO:0051883 0 66.8241 killing of cells in other organism involved in symbiotic interaction

GO:0006955 0 5.7081 immune responseGO:0006952 0 6.3051 defense response

GO:00441301.00E-

0450.1076 negative regulation of growth of symbiont

in host

GO:00441441.00E-

0450.1076 modulation of growth of symbiont involved

in interaction with host

GO:00517071.00E-

044.4254

response to other organism

GO:00427421.00E-

0411.2816

defense response to bacterium

GO:00024462.00E-

0431.6316

neutrophil mediated immunity

GO:00326022.00E-

0415.012

chemokine production

GO:00525482.00E-

045.3903

regulation of endopeptidase activity

GO:19030343.00E-

045.3553

regulation of response to wounding

GO:00709434.00E-

0498.8973 neutrophil mediated killing of symbiont

cell

GO:00429935.00E-

0421.4509 positive regulation of transcription factor

import into nucleus

GO:00507295.00E-

0411.7363 positive regulation of inflammatory

response

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Locus View


2015-04-01 met

hyla

tion

Acknowledgements


2015-04-01

Laura Clarke (EMBL-EBI)Avik Datta (EMBL-EBI)Simon Heath (CNAG)Joost Martens (Radboud Univ.)David Richardson (EMBL-EBI)Daniel Rico Rodriguez (CNIO)Ivo Gut (CNAG)Alfonso Valencia (CNIO)Henk Stunnenberg (Radboud Univ.)

Pavlo LutsikKarl NordstrømJörn Walter

Yassen Assenov (MPII, DKFZ)Pavlo Lutsik (Saarland Univ.)Fabian Müller

Felipe AlbrechtPeter EbertGeorg FriedrichJoachim BüchChristoph Bock (MPII, CeMM)Thomas Lengauer

Benedikt Brors (DKFZ)Jürgen Eils (DKFZ)Bernhard Horsthemke (Univ. Duisburg-Essen)Jörn Walter (Saarland Univ.)

http://rnbeads.mpi-inf.mpg.de

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SUPPLEMENT


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Hematopoietic Methylomes

• 74 methylomes


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Monocytes and Neutrophils


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promoters hypomethylated in neutrophils promoters hypomethylated in monocytes

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computational methods for deep characterization of dna methylation blueprints fabian müller...

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