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Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
Fabian Müller
Keystone Symposium on DNA Methylation
Keystone, CO, USA
April 1, 2015
— Workflow
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
2015-04-01
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
2015-04-01
— Workflow
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Hematopoietic Methylomes
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
2015-04-01
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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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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Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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Unsupervised Learning
2015-04-01Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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
2015-04-01Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
mos
t var
iabl
e pr
omot
ers
methylationlevel
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Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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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
2015-04-01Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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tiling (5kb), top100
mean methylation: monocytes
mea
n m
ethy
latio
n: n
eutr
ophi
ls
Differential DNA Methylation
2015-04-01Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
promoters (Gencode15), top100
mean methylation: monocytes
mea
n m
ethy
latio
n: n
eutr
ophi
ls
13/16
• 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
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
2015-04-01Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
2015-04-01 met
hyla
tion
Acknowledgements
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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Hematopoietic Methylomes
• 74 methylomes
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
2015-04-01 18/16
Monocytes and Neutrophils
Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
2015-04-01
promoters hypomethylated in neutrophils promoters hypomethylated in monocytes
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Computational Methods for DEEP Characterization of DNA Methylation BLUEPRINTs
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