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Achim Tresch Computational Biology ‘Omics’ - Analysis of high dimensional Data

Achim Tresch Computational Biology

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‘Omics’ - Analysis of high dimensional Data. Achim Tresch Computational Biology. Epigenetics. Slides: Doug Brutlag , Stanford University School of Medicine http://biochem158.stanford.edu/Epigenetics.html. Epigenetics. - PowerPoint PPT Presentation

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Page 1: Achim Tresch Computational Biology

Achim TreschComputational Biology

‘Omics’ - Analysis of high dimensional Data

Page 2: Achim Tresch Computational Biology

Slides: Doug Brutlag, Stanford University School of Medicinehttp://biochem158.stanford.edu/Epigenetics.html

Epigenetics

Page 3: Achim Tresch Computational Biology

• C.H. Waddington coined the term epigenetics to mean above or in addition to genetics to explain differentiation.

• How do different adult stem cells know their fate?– Myoblasts can only form muscle cells– Keratinocytes only form skin cells – Hematopoetic cells only become

blood cells– But all have identical DNA

sequences.

Epigenetics

Page 4: Achim Tresch Computational Biology

Epigenetics

• Modern definition is non-sequence dependent inheritance.

• How can identical twins have different natural hair colors?

• How can a single individual have two different eye colors?

Page 5: Achim Tresch Computational Biology

Mosaicism: One Eye, two Colors

• How can identical twin liter mates show different coat colors?

• How can just paternal or maternal traits be expressed in offspring? This is called genetic imprinting.

• How can females express only one X chromosome per cell?

• How can acquired traits be passed on to offspring?

Page 6: Achim Tresch Computational Biology

Paula Vertino, Henry Stewart Talks

The ‘epigenetic’ code

DNA Methylation & Histone

Modifications

Page 7: Achim Tresch Computational Biology

Paula Vertino, Henry Stewart Talks

Methylation of Cytosine in DNA

cytosine 5-methyl cytosine

Page 8: Achim Tresch Computational Biology

Paula Vertino, Henry Stewart Talks

Methylation of Cytosine in DNA

Page 9: Achim Tresch Computational Biology

• CpG dinucleotides are partially methylated in higher vertebrates

• Human genome: only ~4% of all cytosines are methylated, but ~ 70%-80% 5mCpG

• Spontaneous deamination transforms CpG to TpG or CpA

• Estimated rate (after DNA-repair)[1]: 5.8*10-13 (5.8*10-17) 1/s*sites

Wikipedia: Deamination, Thymine, 5-methylcytosine

cytosine - uracil

5-methylcytosine - thymine

[1] Shen et al. (1993) Nucl. Acids Res.

DNA Methylation (Biochemistry)

Page 10: Achim Tresch Computational Biology

Methylation of Cytosine in DNA

Page 11: Achim Tresch Computational Biology

Me

DNA methylation and Histones

Page 12: Achim Tresch Computational Biology

Maintenance of Cytosine Methylation

Page 13: Achim Tresch Computational Biology

Maintenance of Cytosine Methylation

Page 14: Achim Tresch Computational Biology

Alex Meissner, Henry Stewart Talks

Maintenance of Cytosine Methylation

Page 15: Achim Tresch Computational Biology

Maintenance of Cytosine Methylation

Page 16: Achim Tresch Computational Biology

Functions of cytosine methylation

Page 17: Achim Tresch Computational Biology

Alex Meissner, Henry Stewart Talks

DNA Methylation and Cell Differentiation

Page 18: Achim Tresch Computational Biology

Alex Meissner, Henry Stewart Talks

DNA Methylation and Cell Differentiation

Page 19: Achim Tresch Computational Biology

Nuclear transplantation: Differentiated Cells can become Totipotent

DNA Methylation and Cell Differentiation

Page 20: Achim Tresch Computational Biology

Paula Vertino, Henry Stewart Talks

Methylation Changes During Development

Met

hyla

tion

leve

l

Page 21: Achim Tresch Computational Biology

Paula Vertino, Henry Stewart Talks

Methylation Changes During Development

Met

hyla

tion

leve

l

Page 22: Achim Tresch Computational Biology

Paula Vertino, Henry Stewart Talks

Methylation Changes During Development

Met

hyla

tion

leve

l

Page 23: Achim Tresch Computational Biology

© 2013 American Society of Plant Biologists

TTCGCCGACTAA TTCGCCGAuTAA

•DNA methylation– bisulfite sequencing

•Histone modification • chromatin immunoprecipitation (ChIP)

DNA Methylation and Histone Marks

Page 24: Achim Tresch Computational Biology

GREEN = H3K27me3PURPLE =

methylcytosine

Using next-generation sequencing, epigenetic modifications can be identified genome-wide: EPIGENOMICS and METHYLOMICS

DNA Methylation and Histone Marks

Page 25: Achim Tresch Computational Biology

http://www.39kf.com/uploadfiles/image/15902/TXT-20081228163836878.gif

DNA methylation and Gene Expression

Page 26: Achim Tresch Computational Biology

• Methylation in mammals is mainly targeted at CpG dinucleotides

• CpGs are either unmethylated or methylated on both strands

• Hemi-methylated CpGs are rare

• DNA methyltransferases (DNMTs) bind hemi-methylated sites and modify the remaining position

• Thus the epigenetic information is inherited to daughter cells

Adapted from: http://www.diagenode.com/en/applications/bisulfite-conversion.php Lars Feuerbach

Epigenomics

Page 27: Achim Tresch Computational Biology

© 2013 American Society of Plant Biologists

Bisulfite treatment

TTCGCCGACTAA

No treatment

TTCGCCGACTAA

TTCGCCGAuTAA

Methyl-cytosine

When DNA is bisulfite treated, unmethylated cytosine is converted to uracil. Methylcytosine is not affected.

O N

NH2

N

~O N

NH2

N

~

CH3

cytosine 5-methylcytosine

O N

NH2

N

~

CH3

O N

O

N

~uracil 5-methylcytosine

Bisulfite treatment

Bisulfite Sequencing

Page 28: Achim Tresch Computational Biology

© 2013 American Society of Plant Biologists

Bisulfite treatment

TTCGCCGACTAA

No treatment

TTCGCCGACTAA

TTCGCCGAuTAA

TTCGCCGACTAA TTCGCCGATTAA

Methyl-cytosine

After bisulfite treatment, unmethylated Cs are read as T and so differ in the treated and untreated samples.

By contrast, methyl-C is read as C and is the same as the reference sequence.

Bisulfite Sequencing

Page 29: Achim Tresch Computational Biology

RRBS-Seq• DNA is digested by MSP1 restriction enzyme which

cuts at CCGG sites• All DNA fragments start with CpG• Alignment is simplified as reads have to map to MSP1

restriction sites• Reads are enriched for CpG rich areas

http://www.neb.com/nebecomm/products/productR0106asp

CGGATGTTTTGTACTAGGATAACTATGC CGGAT

Reduced Representation Bisulfite Sequencing

Page 30: Achim Tresch Computational Biology

Reference

Read out

Standard alignment to the reference is not possible. Adapted alignment procedures have lower accuracy.

Alignment of BS converted reads

Page 31: Achim Tresch Computational Biology

Tools supporting the alignment of BS reads:- Bismark- BSMAP - BS Seeker

Simon Andrews, Bioinformatics 2011

Alignment of BS converted reads

Key concept:- Convert the reference genome in silico as bilufite

treatment does- Perform conversion for

+ strand and – strand- Then align reads against

both genomes

Page 32: Achim Tresch Computational Biology

Simon Andrews, Bioinformatics 2011

Alignment of BS converted reads

H = IUPAC character for the letters {A,C,T}

Page 33: Achim Tresch Computational Biology

Pearl-Necklace diagrams (lollipop plots)

Measure unmethylated Cs (#C)Measure methylated Cs (#5mC)Report the methylation ratio

CmCmC

#5#5#

Description of DNA methylation

Page 34: Achim Tresch Computational Biology

© 2013 American Society of Plant Biologists

Reprinted by permission from Macmillan Publishers Ltd: Zhong, S., Fei, Z., Chen, Y.R., Zheng, Y., Huang, M., Vrebalov, J., McQuinn, R., Gapper, N., Liu, B., Xiang, J., Shao, Y., and Giovannoni, J.J. (2013). Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nat Biotechnol. [ in press].

Density of methylated DNA and other features in chromosomes of the tomato fruit

The Tomato Methylome

Page 35: Achim Tresch Computational Biology
Page 36: Achim Tresch Computational Biology

Characterize deamination by repetitive sequences

Page 37: Achim Tresch Computational Biology

Evolution of CpG content in repetitive sequences

Peifer et al. (2008) Bioinformatics

Page 38: Achim Tresch Computational Biology

Evolution of CpG-rich promoters

•AT-rich promoters in bacteria

•Mixed promoters in worm and fly

•Increasing GC and CpG content in mosquito

•Small CpG islands in fish

•Broad CpG islands in humans

Khuu et al., PNAS, Sep. 2007

Page 39: Achim Tresch Computational Biology

Promoter Types in Humans

Weber et al., 2007, Nat. Genet.

Page 40: Achim Tresch Computational Biology

Model of CpG island evolution

Ancestral Genome

0,00%1,00%2,00%3,00%4,00%5,00%6,00%7,00%8,00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Position on chromosome

CpG

freq

uenc

y

Page 41: Achim Tresch Computational Biology

Model of CpG island evolution

After 0.1 transversions

0,00%1,00%2,00%3,00%4,00%5,00%6,00%7,00%8,00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Position on chromosome

CpG

freq

uenc

y

Page 42: Achim Tresch Computational Biology

Model of CpG island evolution

Observable genome

0,00%1,00%2,00%3,00%4,00%5,00%6,00%7,00%8,00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Position on chromosome

CpG

freq

uenc

y

Page 43: Achim Tresch Computational Biology

CpG island definitions

Observable genome

0,00%1,00%2,00%3,00%4,00%5,00%6,00%7,00%8,00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Position on chromosome

CpG

freq

uenc

y

CpG content TakeiJones Gardiner-Gardener

CpG island definition:

1. GC-content

2. Ratio observed over expected CpG frequency

3. Minimal Length

Page 44: Achim Tresch Computational Biology

•CpG dinucleotides are rare in the human genome

•CpG Islands are exceptions•Elevated GC content and

CpG frequency•50-60% of promoters are

CpG islands•Methylation level anti-

correlated to expression in HCP promoters

•Cause or consequence ?

CpG islands

Page 45: Achim Tresch Computational Biology

Caiafa and Zampieri,(2005) JCB

CpG islands and chromatin

Page 46: Achim Tresch Computational Biology

Histone modifications

• How to read the nomenclature:– Histone protein (H3)– Position in tail (K9)– Modification type (me3)

Füllgrabe et al., 2011, Oncogene

Page 47: Achim Tresch Computational Biology

Histone code

•Füllgrabe et al., 2011, Oncogene

H3K4me2-me3 Active transcription, near TSSs

H3K9me3 Heterochromatin

H3K9ac Euchromatin, near TSSs

H3K27me3 Polycomb marker, closes chromatin

H4K16ac Higher order chromatin, repeat methylation

H4K20me3 Heterochromatin

Page 48: Achim Tresch Computational Biology

Interplay

•Ceder&Bergman,2009,Nature Rev Genet

Page 49: Achim Tresch Computational Biology

Allele-unspecific DNA methylation

Page 50: Achim Tresch Computational Biology

Allele-specific DNA methylation

Page 51: Achim Tresch Computational Biology

Imprinting• Origin-of-allele-specific gene expression• Exception from Mendel’s inheritance rules• Mediated by methylation of imprinting control regions

University of Florida: http://www.peds.ufl.edu/divisions/genetics/teaching

Page 52: Achim Tresch Computational Biology

Allele specific – Histone modifications

Adapted from: http://genomebiology.com/content/figures/gb-2005-6-6-113-1-l.jpg

Page 53: Achim Tresch Computational Biology

Reference Methylomes – Laurent et al.

• Laurent data on human embryonic stem cells and fibroblasts

• 70% of all CpGs covered by at least 3 reads

Laurent et al. Genome Research 2010

Page 54: Achim Tresch Computational Biology

Reference methylomes – Molaro et al.

• Male germline methylome for human and chimpanzee

• Direct comparison to Laurent et al. data

Molaro et al. Cell 2011

Page 55: Achim Tresch Computational Biology

ENCODE, IHEC and Epigenome Roadmap

• One Genome many Epigenomes• Cataloguing epigenetic modifications in

different tissues

Page 56: Achim Tresch Computational Biology

Translation into NGS signals

Page 57: Achim Tresch Computational Biology

Translation of epigenetic signals

• Capture-seq– Chromatin Immunoprecipitation (ChIP)– Metylated DNA Immunoprecipitation (MeDIP)– MBD chromatography

• Conversion-seq– Bisulfite sequencing (methyl-seq)– Reduced representation bisulfite sequencing

(RRBS)– Ultra-deep amplicon sequencing

Page 58: Achim Tresch Computational Biology

Two signal types

• Coverage

EnrichmentSequencingMappingPeak calling

Sequence

PreparationSequencingSpecial mappingDecoding

Page 59: Achim Tresch Computational Biology

Enrichment-seq – Workflow I

Genome

Epigenetically modified regions

DNA LibraryPreparation

Page 60: Achim Tresch Computational Biology

Enrichment-seq – Workflow II

Enrichment

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Enrichment-seq – Workflow III

Mapping

Genome

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Enrichment-seq – Workflow IV

Genome

Page 63: Achim Tresch Computational Biology

Methylated DNA immunoprecipitation

•http://en.wikipedia.org/wiki/Methylated_DNA_immunoprecipitation

Page 64: Achim Tresch Computational Biology

Lutsik P et al. Nucl. Acids Res. 2011;nar.gkr312

BiQ Analyzer HT

Page 65: Achim Tresch Computational Biology

Allele-specific methylation analysis pipeline

Matthias Bieg et al., in preparation

Page 66: Achim Tresch Computational Biology

Summary• Epigenetics plays a key role in cell function

• Each cell type has its own epigenome

• Epigenetic modifications are can be converted to NGS signals

• Bioinformatic in depth analysis of epigenomes is still in its infancy

Page 67: Achim Tresch Computational Biology

References

•Laurent, L.; Wong, E.; Li, G.; Huynh, T. et al•“Dynamic changes in the human methylome•during differentiation”•Genome Research (2010) 20 320-331

•Molaro, A.; Hodges, E.; Fang, F.; Song, Q. et al.•“Sperm Methylation Profiles Reveal•Features of Epigenetic Inheritance•and Evolution in Primates”•Cell (2011) 146 1029-1041

•Lutsik,P.; Feuerbach, L. ; Arand, J.; Lengauer, T. et al.•“BiQ Analyzer HT: locus-specific analysis of DNA methylation by high-throughput bisulfite sequencing”•NAR (2011)

Page 68: Achim Tresch Computational Biology

Environment can Influence Epigenetic Changes

Emma Whitelaw, Henry Stewart Talks

Page 69: Achim Tresch Computational Biology

Hongerwinter 1944• German’s blocked food to the Dutch in the winter of 1944.• Calorie consumption dropped from 2,000 to 500 per day for

4.5 million.• Children born or raised in this time were small, short in

stature and had many diseases including, edema, anemia, diabetes and depression.

• The Dutch Famine Birth Cohort study showed that women living during this time had children 20-30 years later with the same problems despite being conceived and born during a normal dietary state.