Download Chip-Seq: Methods and applications in epigenomic studies

CHIP-SEQ:
METHODS AND APPLICATIONS IN
EPIGENOMIC STUDIES
Claudia Angelini
Istituto per le Applicazioni del Calcolo–CNR
Napoli
EMBO Practical course: Bioinformatics and
Comparative Genome Analyses. May 15 Naples
Outline
Why study chromatin?
 Overview of a ChIP-seq experiment
 Overview on ChIP-seq data analysis
 Some (few) methods for peak calling
 Some (few) methods for peak annotation
 Discussion and conclusions
 Software
 Useful references
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Why study chromatin?
Chromatin states are defined for a given time point and cell type
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Chromatin is the combination of DNA and
proteins in eukaryotic cells
Genome-wide mapping of protein-DNA
interactions and epigenetic marks and their
modifications is essential for a full
understanding of transcriptional regulation
and cell differentiation
Chromatin states can influence transcription
directly by altering the packing of the DNA
Chromatin alterations are inheritable, but
potentially reversible epigenetic drugs
For a given (eukaryotic) genome there
are hundreds or thousand of epigenomes
depending on the chromatin states
A scientific illustration of how epigenetic mechanisms can affect health
http://commonfund.nih.gov/epigenomics/figure.aspx
Chromatin Immunoprecipitation (ChIP)
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Chromatin Immunoprecipitation is a technique for assaying
protein-DNA binding in vivo
Antibodies are used to select specific proteins or nucleosomes
which enriches for DNA-fragments that are bound to these
proteins or nucleosomes
Selected fragments can be either hybridized to a microarray
(ChIP-chip) or sequenced on modern NGS platform (ChIPseq).
Therefore, it is possible to profile the DNA bounds in vivo for
specific transcription factors, modified histones, RNA Pol II
Overview of a ChIP-seq experiment
Park, nature review 2009
 The DNA-binding protein is cross-linked to
DNA in vivo by treating cells with
formaldehyde
the chromatin is sheared by sonication into
small fragments, which are generally in the
200–600 bp range.
an antibody specific to the protein of
interest is used to immunoprecipitate the
DNA–protein complex.
 the cross-links are reversed
 library are prepared according to the
protocol and are usually subject to size
selection
 the library is massively sequenced
To account for experimental artifacts a
control sample (i.e., Input) is (often)
sequenced.
Issues on experimental design
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Antibody quality
Sample amount
Control experiment (i.e., INPUT
DNA)
Depth of sequencing
Samples’ number and amount of
replicates
Nowadays the use of a control
experiment is a common practice.
It is strongly suggested since it
significantly improves the results.
Saturation Analysis
From experiment to “data”
Park, nature review 2009
DNA fragments are sequenced
from 5’ end to 3’ end
 the ChIP-seq protocols is strand
specific and both strands are
sequenced simultaneously
 The observed data are the
“short reads”
Short Read
Binding site
Fragment size
Park , Nature Review 2009
Overview on ChIP-seq data analysis
e.g, Input
DNA
1) Identification of “enriched regions” (i.e., peak calling)
2) Interpretation of results, annotations, comparisons among samples and
conditions
Useful readings before starting with ChIP-seq
From reads to coverage profile
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A typical ChIP-seq experiment
produces from few millions to
several tens of millions of short
reads (depending on the experimental design
and the organism under analysis)
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The first step in the analysis is the
read mapping (i.e., the alignment
to the reference genome) using
standard tools such as Bowtie, BWA,
etc
Once the reads have been aligned
the observed “profile” can be
obtained and visualized
Park, nature review 2009
Examples and Visualization
Alignments can be uploaded in
browsers such as UCSC Genome
Browser or IGV etc…
The “signal” appears in from of “peak” 
the problem is turned in detecting peaks
The idea of Peak detection
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The peak detection is the most
“critical” part of any ChIP-seq
analysis. It consists in the
identification of the genomic
regions that were ‘enriched’ in
the ChIP sample.
In practice one has to find
genomic regions that have
produced a significantly
higher number or reads
(respect to the expected value
estimated from the input)
From Pepke et al, 2009
Few key ideas
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Compute the reads count at each position of the genome (either at
bp resolution or within a given bin or [sliding] window)
ChIP
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Determine if the “read count” is greater than expected
Suitably combine pieces of small resolution (i.e., bp bins,…) into
“regions” of enrichment
Several issues & biases
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Reads are not uniformely distributed. Control (i.e., Input DNA) can be
used for addressing such drawback
GC-content
PCR amplification
Mappability ambiguity due repetitive regions
Normalization is often necessary due to
the different number of reads sequences
in each library
What to do?
Methods for Peak detection
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There are several
methods available
 These methods often require a careful
choice/tuning of several parameters to
obtain good results.
 Not always easy to use, often not well
documented
Some comparisons among methods (1)
From Wilbanks & Facciotti 2010
 Different methods can provide
different results when applied to the
same dataset.
 Comparing different methods is
hard due to the lack of benchmark
data-sets
 All methods rely on a set of
parameters that need to be tuned
accordingly to work best
 It is often necessary to use (or to
try) several methods to the same
dataset
 Methods are implemented in
different programming languages.
Some comparisons among methods (2)
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However the top (say 500-1000) peaks are usually consistent
across methods, differences arise when looking for “marginal”
peaks
The use of control provides a significant improvement of the
performance
Deeper sequencing also improves performance (when control is
available)
Visualization is very important
Despite the large number of
methods available (more than
40!!), there is still space for
improvement
In the following few methods will
be described more in details
MACS (1) [Model-based Analysis of ChIP-Seq]
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It is one of the most popular
approaches (also among the
first to appear)
It uses a Poisson distribution to
model the reads and it
empirically estimates the
average fragment length
using information from both
strands
It is implemented as standalone
(freely
available)
software (in Python)
It works either with only ChIP
sample and with both ChIP
and Control
http://liulab.dfci.harvard.edu/MACS/
MACS (2)
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It estimates the average fragment size using a
bimodal distribution on positive and negative
strand (peak model), then shift the reads together
by half of the distance toward the 3’
The number of reads are counted in each bin and
the Poisson distribution (with local parameters
estimated from the sample) is used to compute pvalues and identify the enriched regions.
MACS (3)
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Local Poisson parameters are
estimated when the control is
available (otherwise uniform
Poisson background is assumed)
n 1
k e  k
k 0
k!
X ~ Pois   P X  n   1  
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It reports either p-values and
fold enrichment
FDR is computed
sample swapping
Uniform
background
using
To be estimated in window
of size 1K,5K,10K from the
control
BayesPeak (1)
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it uses a fully Bayesian
Hidden Markov model to
detect enriched location
in the genome
All hyper-parameters are
estimated using MCMC
Enriched regions are
detected on the basis of
their posterior probability
it works either with only
ChIP sample and with
both ChIP and Control
It is implemented in the R
package “BayesPeak”
It is designed for the identification of transcription
factor binding sites, but it has been also applied to
Histone modifications such as H3K4me3
BayesPeak (2)
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 Define the observed counts as Yt and Yt for window t,
on the forward (+) and reverse (-) DNA strand.
Assigns a state St to each region t such that S t  1 if
there is a binding site or modification in that region
causing fragment abundance, and S t  0 if not.
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PSt 1  1 | St  0  p
PSt 1  1 | St  1  r
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Assuming
among
windows
dependence
adiacent
 Modeling the observed counts
Equivalent to the
use of Negative
Binomial
BayesPeak (3)
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The control sample can be used to estimate the hyper-parameters for
the background
Hyper-parameters are estimated via MCMC
Inference is carried out on the posterior probability P(Z|Y)
An “empirical” threshold T (default 0.5) have to be used on the
posterior probability to select which regions are enriched
Once P(Z|Y) is known, it is possible to compute P(S|Y) and several
methods are available for combining bins into “regions”
PICS (1) [Probabilistic Inference for ChIP-Seq]
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It uses a hierarchical Bayesian
approach
Instead of modeling read count,
it models reads positions in
positive and negative strands
by using mixture-models
It models the average fragment
size as the distance of the two
components of the mixture
EM is used for estimating the
hyper-parameters
It uses information about
mappability profiles (treated
as missing values)
It is implemented in the R
package “PICS”
It is designed for the identification of
transcription factor binding sites.
PICS (2)
 A filtering pre-processing step is applied and
regions without a minimum number of reads in a
windows are filtered out.
 Inference is carried out in each sub-regions
independently (fitting mixture models)
 Parameters are obtained using EM
 the number of components in the mixture is
chosen using BIC
PICS (3)
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Reads that may not be aligned due to the
repetitiveness of the genome are treated as
missing (we do not know which read is missing but we may
know where they be missing)
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Mappability profile (vector of 0-1 of length of the
genome, where 1 means that a short read can be aligned to that
position and 0 that it cannot be aligned) is used to handle
repetitive regions
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Missing reads can handled with EM
An enrichment score S() is defined to identify and
rank a statistically significant sub-set of binding
events
FDR is estimated as function of the enrichment score
after swapping the two samples
Regions of no/or
low mappability
ChIP-seq: one “word” many “signal”
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Transcription Factor binding sites
Histone modifications
RNA polymerase II
others
From Wilbanks & Facciotti 2010
From Pepke et al, 2009
From TF to Histone modifications
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While there exists several methods able to detect TFs signatures
along the genome, the identification of histone marks and their
modifications is by far more challenging
SICER
Qeseq
Rseg
It contains also a
comparative study
Some comparisons among methods (3)
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Simulation
Benchmarking publicly
available ChIP-Seq datasets
with qPCR validations
ChIP-seq and repetitive regions (1)
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It is know that many TFs (or histone modifications) are associated to
repetitive regions (also, 10-30% depending on the factors and the
organism)
Typical ChIP-seq analyis considers only uniquely mappable reads
 therefore introduce a bias in the possibility of studying the binding
in repetitive regions
Longer reads (and/or Paired-end protocols) reduce the problem, but
it does not solve
What to do ?
Not yet a well accepted solution
ChIP-seq and repetitive regions (2)
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2)
Two emerging approaches
Align the not uniquely reads on (known) repetitive
regions and count the hits (per family or category)
Assign the “multiple reads” using some euristic or
some probabilistic model
Good starting point
From peaks to annotations and biological interpretation
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Once the “peaks” or the enriched regions have been identified
they need to be annotated and biologically interpreted
Peak annotation consists in relating the peaks’ position with all
annotations and known genomic feature available (relevant for
the problem under analysis) (TTS, genes, CpG, …).
Biological interpretation depends on the problem under study.
It includes motif dicovery and analysis, Gene ontology (Gene
set enrichment-style analyses), pathway analysis, ….., etc. It
may also require to combine information from several
experiments and data integration.
Data integration provides a more comprehensive complex
picture of the biological system. It includes to relate peaks with
expression levels of genes and SNPs and allele-specific binding
ChIPpeakAnno
R package for
annotation
It allows
to integrate peak positions and annotations
To visualize results using descriptive
statistics
To extract Goterms and test enrichment
To visualize over representative sequences
that can constitute a consensus
rGADEM & MotIV
R package motif finding
RSAT [Regulatory Sequence Analysis Tools]
http://rsat.ulb.ac.be/
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A computational pipeline that
discovers
motifs
in
peak
sequences, compares them with
databases, exports putative
binding sites for visualization in
the UCSC genome browser and
generates an extensive report
suited for both naive and expert
users
GREAT [Genomic Regions Enrichment of Annotations Tool ] (1)
 GREAT
supports
direct
enrichment analysis of both the
human and mouse genomes.
 It integrates 20 separate
ontologies containing biological
knowledge about gene functions,
phenotype and disease associations,
regulatory and metabolic pathways,
gene expression data, …..
http://great.stanford.edu/public/html/splash.php
GREAT [Genomic Regions Enrichment of Annotations Tool ] (2)
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Two statistical tests are employed in
order to establish the functional
enrichment: Binomial test over genomic
regions and Hypergeometric tests over
genes
Terms significant by both tests (B ∩ H) provide specific and
accurate annotations supported by multiple genes and binding
events. Terms significant by only the hypergeometric test (H\B)
are general and often associated with genes of large
regulatory domains, whereas terms significant by only the
binomial test (B\H) cluster few genomic regions near only one or
two genes annotated with the term
Software availability
http://seqanswers.com/wiki/Software
On 21/04/2012 there are 558 “Bioinformatics
Application”. Out of which about 47 are devoted
to ChIP-seq
Open Source & freely available
 Single stand-alone software (e.g. MACS,
SICER,….)
 Specifics packages in general enviroments
such as R (several in Bioconductor, see practical)
Web resources (GALAXY, …)
User friendly software with GUI
(ChIPseeqer, Chipster,…)
(General) Web-resoureces
https://main.g2.bx.psu.edu/
Chipster
http://chipster.csc.fi/
ChIPseeqer
The software includes:
 Peak detection
 Gene-level annotation of peaks
 Pathways enrichment analysis
 Regulatory element analysis, using either a de
novo approach, known or user-defined motifs
 Nongenic peak annotation (repeats, CpG
islands, duplications)
 Conservation analysis
 Clustering analysis
 Visualization
 Integration and comparison across different
ChIP-seq experiments
http://physiology.med.cornell.edu/faculty/elemento/lab/chipseq.shtml
Applications
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Studying disease processes
Understanding basic regulatory mechanisms
Studying cell differentiation
…..
For what concerns comparative genome analyses, it is very
important to study regulatory mechanisms across several species.
A must to read for those
interesting in comparative
genome analyses
Examples of ChIP-seq in comparative genome analysis
Among several others
 Woo,
Y.H, et al. Evolutionary Conservation of Histone
Modifications in Mammals, Molecular Biology and Evolution,
(2012)
 Hemberg, M. et al. Conservation of transcription factor binding
events predicts gene expression across species, Nucl. Acids Res.
(2011) 39 (16): 7092-7102.
 He, Q. et al. High conservation of transcription factor binding and
evidence for combinatorial regulation across six Drosophila species.
Nat. Genet. 43, 414–420 (2011).
 Schmidt, D. et al. Five-vertebrate ChIP-seq reveals the evolutionary
dynamics of transcription factor binding. Science 328, 1036–
1040 (2010).
Some other useful resources (1)
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COST Action BM1006: Next Generation
Sequencing Data Analysis Network. http://www.seqahead.eu/
 Training Schools and Short term scientific missions are available.
 Workshops are organized about twice times a year
Some other useful resources (2)
Officially started on 1/1/2012
References
11/05/2012
There are 590 papers with
“Chip-seq”
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