ChIP-Seq -method for studying epigenetic...

ChIP-Seq - method for studying

epigenetic mechanisms

Oleg Shpynov

28.07.2018

● Regulation

● TFs and histone modifications

● ChIP-Seq protocol

● Ultra-Low-Input ChIP-Seq

● MACS2 and SICER peak callers

● Semi-supervised approach to Peak Calling

● Human monocytes aging project results

Agenda

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3

● Evolution mainly works on regulatory, not protein-coding, DNA

● If we know regulation, we can find key spots in large pathways

● Next-generation sequencing!

http://massgenomics.org/2012/01/the-current-state-of-dbsnp.html

Why study regulation?

Primate to human: it’s in the regulation

4

● Gene structure and expression are

well conserved

● Gene coexpression is not

● The difference lies in gene regulation

A

C

B

D

A

C

B

D

humanchimpanzee

Proc Natl Acad Sci U S A. 2006 Nov 21;103(47):17973-8.

Chromosomes and chromatin

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● Chromosomes are dense complexes of DNA and proteins

● Each human chromosome contains on average 5 cm of DNA

● This is about 2 m of DNA overall – too much!

● Chromatin = euchromatin + heterochromatin

https://www.shmoop.com/dna/dna-packaging.htmlhttp://www.mun.ca/biology/scarr/FISH_chromosome_painting.html

ChromEMT

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● In 2017, a new method for chromatin

staining allowed to obtain high-contrast

electron tomography images of mitotic

chromosomes

● Chaotic 5 to 22nm structures observed

http://science.sciencemag.org/content/357/6349/eaag0025.long

Regulation of transcription

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● Basal transcription: general

transcription factors bind the

promoter and RNA polymerase II

● Activator proteins bind DNA spots

named enhancers

● Enhancers are often located far

and have to loop

https://courses.lumenlearning.com/suny-wmopen-biology1/chapter/eukaryotic-gene-regulation/

Transcription factors

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● ~1,500 transcription factors in humans

● Binding motif represented by consensus sequence

● Master regulators exist but are not always known

● Functions:

○ stabilize/block RNAP II binding to DNA

○ catalyze histone acetylation or deacetylation

○ recruit coactivator or corepressor

http://www.broadinstitute.org/education/glossary/transcription-factor

Chromatin Immunoprecipitation (ChIP)

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http://www.bio.brandeis.edu/haberlab/jehsite/chIP.html

DNA-binding proteins are crosslinked

to DNA with formaldehyde in vivo.

Isolate the chromatin. Shear DNA

along with bound proteins into small

fragments.

Bind antibodies specific to the DNA-

binding protein to isolate the complex

by precipitation. Reverse the cross-

linking to release the DNA and digest

the proteins.

Use PCR to amplify specific DNA

sequences to see if they were

precipitated with the antibody.

Who was first?

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http://www.snarkyscientist.com/2013/06/19/the-history-of-the-biggest-technique-of-2009-who-invented-chip-seq/

ChIP-Seq

● DNA library obtained after ChIP can be amplified and sequenced

● ChIP-Seq can be used for both transcription factors and histone

modifications, like H3K4me3

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Epigenetic regulation

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http://en.wikipedia.org/wiki/Epigenetics

● Histone modifications

● DNA methylation

● Noncoding RNA

Histone tail modification

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https://www.irbbarcelona.org/en/news/understanding-the-molecular-origin-of-epigenetic-markers

● Histones tails stick outside

and can be recognized

● Chemical modifications

of histones influence

DNA accessibility

● Histone modifications

can be read, erased, and

recognized

Promoter histone marks

14The EMBO Journal, 31, pp 3130–3146 (2012) C. Xu et al, Nature Communications , 2(227),pp 1-8 (2011)

Mouse

heart

● Narrow peaks of H3K4me3 mark promoters

● Enzyme that methylates K4 binds only to non-CpG-methylated promoters!

Enhancer histone marks

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Bauer, D.E.; Kamran, S.C. et al, Science, 342(6155), pp 253-7 (2013)

Human

erythroblasts

● Enhancers are associated with H3K4me1 and H3K27ac

● H3K27ac is thought to distinguish active enhancers from poised

Transcription elongation marks

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Mouse

proB cells

Proc Natl Acad Sci U S A, 107(50), pp 21931-6 (2010)

Inactive Active Active

● Elongation is marked my H3K36me3 and H3K79me2

Gene repression by chromatin marks

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H3K4me3

H3K4me3

H3K4me3

H3K27me3

H3K27me3

H3K27me3

ES

NPC

MEF

Mikkelsen, T.S.; Ku, M. et al, Nature 448, pp 553-560 (2007)Vastenhouw N.L.; Zhang Y. et al, Nature 464, pp 922-6 (2010)

● H3K27me3 marks suppressed genes poised to be activated

● Stem cells can have both H3K4me3 and H3K27me3 – unique!

Heterochromatin marks

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http://medcell.med.yale.edu/histology/cell_lab/euchromatin_and_heterochromatin.php

● Marks H3K9me2 and

H3K9me3 are strongly

associated with heterochromatin

● Binding with protein HP1

Chromatin marks regulation (simplified)

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Lee, T.I.; Young, R.A., Cell, 152(6), pp 1237-51 (2013)

Let’s study how regulation by histone modifications

changes with aging

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Multiomics dissection of healthy human aging

5 marks x 40 donors

http://artyomovlav.wustl.edu/aging

Conventional vs Ultra Low Input ChIP-Seq+

Robust well-adopted

protocol

Good signal-to-noise

ratio

Lots of high quality data

available for human

Guidelines and pipelines

by ENCODE, Blueprint,

etc

–

2-5mln cells required

per single run

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+

100k cells required per

single run

–

Difficulties to process in

wet lab

Worse signal-to-noise

ratio than conventional

ChIP-Seq Original

protocol is for mice

No high quality data

available for human

Сonventional vs ULI ChIP-Seq

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Co

nve

ntio

na

lU

LI

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ULI ChIP-Seq is always noisy

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H3K4me3 - big variance in signal-to-noise ratio

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ULI ChIP-Seq challenges

● High noise in the data due to ULI protocol

● High variance in signal-to-noise ratio

Peak calling - easy signal extraction problem

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86 existing tools on the list*

Tools for easy problem?

* https://omictools.com/peak-calling-category

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How to chose?

ENCODE ChIP-Seq pipeline

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Problems

● MACS2 performs poorly on broad modifications

● Different signal-to-noise ratio in replicates

● Replicate concordance step fails

● IDR method works only for 2 replicates

https://www.encodeproject.org/pipelines/ENCPL272XAE/

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Different tools = Different Data models

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MACS2 - not good for broad marks● Estimate fragment size to shift tags

● Estimate local λ for Poisson from control track (non-specific binding)

● Use posterior probabilities to compute p-values and q-values,

merging close enriched locations to peaks

SICER - fails for TFs and narrow marks

● Uses coverage to estimate for λ-s for 2 Poisson distributions

● Uses blacklist regions to overcome mappability issues

● Complicated procedure of scoring islands and significance detection

Different Data models = Different cases

Modification Tool

TFs or NARROW Histone marks MACS2 or SPP or PeakSeg

BROAD or MIXED Histone marks SICER or PeakSeg or RSEG

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Different cases = Different tools

https://github.com/olegs/bioinformatics/blob/master/chipseq/chipseq.pdf

Application for ULI ChIP-Seq data

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MACS2, SICER peaks number

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MACS2, SICER peaks length

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Proc & Cons

Jaccard* ● Widely used

● Bad for shifts and

enclosed regions

● Symmetric

Overlap● Works with enclosed

regions (A < B)

● Tolerant for shifts

● Non-symmetric

*Jaccard(A, B) = length(A intersect B) / length(A union B) 37

How to estimate consistency?

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How to estimate consistency?

Overlap(A, B) = ⅓Overlap (B, A) = 1 B

A

MACS2, SICER pairwise peaks overlap

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~400 points shown,

pairwise 20 vs 20

MACS2, SICER pairwise peaks overlap

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These are the tracks with

low signal-to-noise ratio

~400 points shown,

pairwise 20 vs 20

Are standard tools not applicable

or we failed to use correct parameters?

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Classical vs supervised approach

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Peak callers used in our study:● PeakSeg - didn’t work out-of-the-box

● MACS2 --broad

● RSEG - was too slow

● SICER

Peak callers optimization performance

https://academic.oup.com/bioinformatics/article/33/4/491/2608653

Semi-supervised approach

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● Manually labelled dataset

● Parameter grid for each peak caller (MACS2, SICER, SPAN)

● Determine the parameter which gives the lowest error rate

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● Preprocess input data

● Create 3 state HMM

● Train model by Baum-Welch

EM algorithm

● Compute posterior

probabilities

SPAN Peak Analyzer

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● Preprocess input data

● Create 3 state HMM

● Train model by Baum-Welch

EM algorithm

● Compute posterior

probabilities

Parameters

● Use q-values to control FDR

at level alpha

● Use gap to merge close

enriched positions

SPAN Peak Analyzer

● Train models

● Visualize tracks in

genome browser

● Create visual labels:

500+ labels x 5 ChIP-seq

targets

● Optimize parameters in

single click

● Consistent peak calling!

Semi-supervised scheme

Peaks with default parameters

http://artyomovlab.wustl.edu/aging/howto.html

Peaks with optimized parameters

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Overall ChIP-Seq processing scheme

Peaks number consistency improved

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* https://genome.cshlp.org/content/11/12/1975.full.html

Peaks length consistency improved

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* http://bionumbers.hms.harvard.edu/bionumber.aspx?id=105336

Consistency between samples improved

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Criticism

A: Use consistency as a quality function,

while learning on the same markup

Q: Only a small fraction of genome is used,

labels are created only where we see consistency

visually

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Validation: consistency with ENCODE improved

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Validation: expected overlap between all experiments

No difference in core 5 histone marks found

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No difference? Talk about variation!

Is it about regulation?

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● No differences in 5 histone marks

● Difference in DNA methylation

DMRs are overrepresented in histone marks

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oleg.shpynov@jetbrains.comhttps://research.jetbrains.org/groups/biolabs

Summary

● Histone modifications is mechanism of regulation

● ChIP-Seq allows to profile histone modifications

● ULI ChIP-Seq allows to profile many modifications for same donor

● MACS2, SICER are not applicable for data with different signal-to-noise ratio

● Semi-supervised approach produces high quality results

● No changes in 5 core histone marks in HEALTHY human monocytes aging

● Regulation? Potentially interesting changes in DNA methylation in enhancers

Thank you!

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ENCODE project

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● ENCODE = ENCyclopedia Of DNA Elements

● Pilot cost (2007): $55M, up to date: ~$300M

● RNA-Seq, ChIP-seq of major TFs and histone modifications, DNA methylation

● Series of publications in the Fall of 2012 (6 Nature papers, 30 papers overall)

http://www.sciencemag.org/content/337/6099/1159/F2.expansion.html

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ENCODE project discoveries

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● 400,000 enhancers and 70,000

promoters

● More than 90% of genomic variation

are in noncoding areas

● DNase I footprint is not that big

● mRNAs are more abundant in cytosol,

other RNAs – in the nucleus

● “More than 80% of human genome is

functionally active”

http://www.evolutionnews.org/2012/09/the_demise_of_j_1064061.html

ENCODE project criticism

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● 80% of DNA cannot be truly functional, since

only about 10% (5-15%) is conserved

● This means ~70% of genome is either

○ impervious to deleterious mutations, or

○ does not mutate, or

○ does not have deleterious mutations

http://blogs.scientificamerican.com/guest-blog/2012/09/17/junk-dna-junky-pr/

Histone code

hypothesis

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Strahl, Allis, Nature 403(6), 2000, 41-45

● Concept similar to

genetic code

● Implies existence of

histone mark

combinations that

have specific

function

Main tools for genome segmentation

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Jason Ernst lab - ChromHMM William Noble lab - Segway

Nat Methods 2012 Feb 28;9(3):215-6. doi: 10.1038/nmeth.1906Nat Methods 2012 Mar 18;9(5):473-6. doi: 10.1038/nmeth.1937

ChromHMM

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● BED files are binarized using the selected chromatin marks

(present: 1, absent: 0)

● Marks are then grouped in a number of states – biologically meaningful

combinations of marks

● Transition is transfer between states, emission – probability of causing the

observed effect

Nature 2011 May 5;473(7345):43-9. doi: 10.1038/nature09906

Genome annotation

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● Segmentation

allows discovery

of novel elements,

alternative

promoters

● Here we find a

new non-coding

RNA

Nucleic Acids Res 2013 Jan;41(2):827-41. doi: 10.1093/nar/gks1284

Discovery of lncRNAs

71Nature 2009 Mar 12;458(7235):223-7. doi: 10.1038/nature07672

● Long noncoding RNAs in 2008 were rare, considered artifacts

● ChIP-Seq of H3K4me3/H3K4me36 revealed thousands of lincRNAs

Superenhancers

● There are estimated 400,000 enhancers in human genome

● Not all are active in every cell – estimated 5,000 - 100,000 per cell type

● There are special types of enhancer elements called superenhancers

● Enriched for Med1, H3K27ac, H3K4me1, and master TFs

72Cell 2013 Apr 11;153(2):307-19. doi: 10.1016/j.cell.2013.03.035

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MACS2

Step 1: estimating fragment length d

● Slide a window of size BANDWIDTH

● Find top regions with MFOLD enrichment of treatment vs input

● Use +/- strand cross correlation to estimate d

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Step 2: identification of local noise parameter

● Slide a window of size 2*d across treatment and input

● Estimate λ for Poisson distribution

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Step 3: identification of enriched regions

● Find regions with P-values < PVALUE

● Determine summit position inside enriched regions as max density

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Step 4: Significance testing

● Swap treatment and control, call peaks using same PVALUE

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Step 5: Broad peak calling

● Use PVALUE or BROAD-CUTOFF option to filter enriched peaks

● Compose broad regions of nearby enriched peaks

● Max length of region is 4*d

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SICER

Step 1: detection of Islands

● Use coverage to estimate global λ-s for

Poisson distributions (treatment and

control)

● Classify enriched windows

● Enriched windows are separated by gaps

● Island is a cluster of enriched windows

separated by gaps of size at most GAP

windows

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Example: GAP = 2

Step 2+: scoring

● The scoring function is based on probability of observation tags count in a

random background

● Scoring for enriched window = -ln P(m, lambda)

● Scoring for island is the aggregated score of all enriched windows in the

island, corresponds to the background probability of finding the observed

pattern

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Score(I) = F* (Score(I1), Gap, Score(I2))

Step N: significance testing

● Use control library as background to calculate p-value for islands

● Or use random background model to calculate p-values for islands

● Compute q-values by p-values

● Filter by p-value of by q-value (FDR)

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