chip-seq -method for studying epigenetic...
TRANSCRIPT
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ChIP-Seq - method for studying
epigenetic mechanisms
Oleg Shpynov
28.07.2018
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● 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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● 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?
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Primate to human: it’s in the regulation
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● 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.
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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
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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
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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/
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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
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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.
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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/
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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
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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
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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!
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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
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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
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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!
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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
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Chromatin marks regulation (simplified)
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Lee, T.I.; Young, R.A., Cell, 152(6), pp 1237-51 (2013)
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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
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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
22
+
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
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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
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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?
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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
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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
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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
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MACS2, SICER pairwise peaks overlap
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~400 points shown,
pairwise 20 vs 20
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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
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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
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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
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● 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
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Peaks with default parameters
http://artyomovlab.wustl.edu/aging/howto.html
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Peaks with optimized parameters
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Overall ChIP-Seq processing scheme
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Peaks number consistency improved
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* https://genome.cshlp.org/content/11/12/1975.full.html
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Peaks length consistency improved
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* http://bionumbers.hms.harvard.edu/bionumber.aspx?id=105336
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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
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No difference in core 5 histone marks found
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No difference? Talk about variation!
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Is it about regulation?
59
● No differences in 5 histone marks
● Difference in DNA methylation
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DMRs are overrepresented in histone marks
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[email protected]://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
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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
65
● 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
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ENCODE project criticism
66
● 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/
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Histone code
hypothesis
67
Strahl, Allis, Nature 403(6), 2000, 41-45
● Concept similar to
genetic code
● Implies existence of
histone mark
combinations that
have specific
function
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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
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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
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Genome annotation
70
● 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
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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
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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
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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
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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
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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))
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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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