Introduction to epigenetics and ChIP-Seq

Aleksandra Galitsyna

“Analysis of omics data” course
Skoltech
24 February 2022

Introduction: sequencing

"Computational analysis of next generation sequencing data and its applications in clinical oncology" Wadapukar&Vyas 2018

Sanger

Illumina, Roche 454, SOLiD

Nanopore

Illumina sequencing

Illumina sequencers

https://www.illumina.com/systems/sequencing-platforms.html

NGS techniques diversity

Wasserman & Sandelin. Applied bioinformatics for the identification of regulatory elements. Nat. Rev. Genet. 2004

Activation of promoters and enhancers

Key determinant of expression is activation of promoters and enhancers

http://advancedtranscription16.blogspot.com/p/overview.html

Promoter

How to activate enhancer and promoter?

Regulatory networks of enhancers

One gene can regulate multiple enhancers,
while multiple enhancers can influence a gene:

https://cotney.research.uchc.edu/

Wasserman & Sandelin. Applied bioinformatics for the identification of regulatory elements. Nat. Rev. Genet. 2004

Epigenetic encoding of expression patterns:

Sequencing for epigenetics

https://doi.org/10.1016/B978-0-12-812215-0.00004-2

ChIP-Seq: experimental protocol

  • Aim: find all regions in the genome bound by a specic transcription factor (or bearing a specic histone modication)
  • Answer: ChIP-Seq, Chromatin immunoprecipitation with sequencing

Park 2009

Practice

Before we start the practice...

  • Hometask made available on canvas: 
  • Find and follow the instructions for the seminar here

Before we start the practice...

  • We will work in browser and with terminal. We also need to copy files from the remote server, so install appropriate software, if needed (your favourite terminal distributive,
    Putty: https://www.putty.org/ or WinSCP: https://winscp.net/eng/download.php)
  • File formats are specified after the dot (e.g. .bam).
  • Code is running in grey boxes with monospace font, try not to copy it blindly, because:
    some file names are surrounded by brackets "${}". This indicates that it should be replaced by the name of your file.​ You may use the filenames that you like, but I highly recommend to stick to using the name of your factor and input instead. 
echo "Bioinformatics has never been easier! Copy&paste wisely, or rm -rf ~/ will strike you one day."

Before we start the practice...

Review of some useful bash commands: 

  • List files in the current directory:
     
  • Check the current position in the directory tree:
  • Open text file for reading:
     
  • Run command help, one of the options:
    (some of them work for ones
    but not the others)
     
  • Copy file:
  • Enter directory:
  • Exit the directory:
  • Create directory:
  • File decompression:
ls
pwd
less file.txt
some_command -h
some_command --help
man command
cp target_file destination_folder
cd directory_name/
cd ../
ls -hts
mkdir
gzip -d file.gz
unzip file.zip

Expected result of the seminar

  • Quizz on canvas (10 points in total)
  • Extended deadline until 24th of March.
  • Each task of the quizz is screenshot + essay answer.
  • Mistakes on biology and theory are penalized. 
  • Feel free to ask the questions in the chat!

ChIP-Seq theory

ChIP-Seq: density of DNA fragments

  • We expect to find different results for plus (Watson) and minus (Crick) strands of DNA:

Park 2009

ChIP-Seq: Cross-Correlation

  • We expect to find different results for plus (Watson) and minus (Crick) strands of DNA:

Park 2009

ChIP-Seq: Cross-Correlation

  • The cross-correlation typically peaks at the shift corresponding to the fragment length and the shift corresponding to the read length.

Bailey 2013

ChIP-Seq: Cross-Correlation measure

  • NSC (normalized strand cross-correlation coefficient):
    The ratio between the cross-correlation at the fragment length and the background crosscorrelation.

  • RSC (relative strand cross-correlation coefficient):
    The ratio between cross-correlation at the fragment length and the crosscorrelation at the read length.

  • Very successful ChIP experiments generally have NSC>1.05 and RSC>0.8

Bailey 2013

Input control

  • Input experiment is ChIP-Seq control with the same experimental steps but without immunoprecipitation.
  • Input is used for normalization of ChIP-Seq.
  • Input experiment accounts for unwanted factors contributing to sequencing & processing output:
    • DNA-shearing is not uniform across genome -> more reads in open chromatin regions,
    • Amplification bias (GC content),
    • Repetitive regions,
    • DNA accessibility

Park 2009

Normalization by input control

Park 2009

elevated
(absolute values)

enriched
(relative to input)

log(\frac{signal}{input})

ChIP-Seq data processing

Park 2009

ChIP-Seq data processing: implementation

https://hbctraining.github.io/Intro-to-ChIPseq/lessons/07_handling-replicates-idr.html

ENCODE Standards:

https://www.encodeproject.org/chip-seq/transcription_factor/

ChIP-Seq peak calling

MACS2 is the most common software:

https://galaxyproject.org/tutorials/chip/

 

ChIP-Seq peak calling result

Binding of protein is typically specific to DNA sequence:

ChIP-Seq: peak calling

https://www.strand-ngs.com/features/chip-seq

Example output of the procedure:

What is the next step?

Binding events and DNA motifs

Specific and non-specific binding:

Slattery et al. 2014

Genomics tracks association

DNase-Seq coverage for ∼350-kb region:

Thurman et al. Nature 2012

Different cell types

Clustering of samples

Cusanovich et al. Cell 2018

Let's check the similarity of pooled tissue samples with DNase-seq from known tissues. For that, let's plot bi-clustered heatmap of Spearman correlation coefficients:

 

Result of clustering,
the tree reflecting the similarity between samples

Clustering of samples

Cusanovich et al. Cell 2018

Finally, let's do dimensionality reduction, where each dot is a single sample.
Here is an example with t-SNE, although PCA (Principal Component Analysis) and MDS (Multidimensional scaling) are other popular techniques:

 

Practical Part: Samples Comparison

We will use deeptools commands multiBigwigSummary and plotCorrelation to compare the ChIP-Seq experiments. We will construct bi-clustered heatmaps of correlations:

Text

Stackup Plots (enrichment profiles):

https://deeptools.readthedocs.io/en/develop/content/tools/plotHeatmap.html#usage-examples

Additional slides
FYI

Get in touch with your data: step 1

1. Login to the server via terminal:

 

or use Putty on Windows.
(example instructions https://www.ssh.com/ssh/putty/windows/#sec-Configuration-options-and-saved-profiles)

2. Create the folder with your project:

 

 

3. Activate prepared environment:
(if you have problems with it, please, contact the TA)

 


4. Check your environment:

 

ssh username@servername
mkdir EpiPract1
cd EpiPract1
export PATH="/home/a.galitsyna/conda/bin:$PATH"
conda activate chipseq
ls # Should list all the filesin the directory
pwd # Should result in your currentl location
conda list # Should list all the programs installed, if conda imported successfully.

Get in touch with your data: step 2

  1. Find your datasets (here or in the table on the right):
     
  2. Copy to the local folder:


     
  3. Check the content and file sizes:
less /home/a.galitsyna/data
mkdir ~/lesson6/
cp /home/a.galitsyna/data/reads/${your_files.fastq.gz} ~/lesson6/
cd ~/lesson6
ls -hts

Get in touch with your data: step 3

gzip -d <file.fastq.gz> # This will result in <file.fastq> unzipped file
less <file.fastq>
wc -l <file.fastq>
fastqc <file.fastq>

NGS data typical workflow example

Let's review the NGS data processing workflow:

  1. Quality control: fastqc on initial FASTQ
  2. Index build: bowtie2-build on initial chromosomes files
  3. Read mapping: FASTQ → SAM/BAM
  4. Filtering of mapped reads: samtools on SAM from p.3
  5. Conversion to BED: bedtools on SAM from p.4

FASTQC report example

  • Takes FASTQ as input:
     
  • Output format: html and plain text

     
fastqc <input.fastq>
ls 
  # input.fastq input_fastqc/

cd input_fastqc/
ls
  # fastqc_data.txt  fastqc_report.html  Icons  Images  summary.txt

Don't run this code, it's FYI

FASTQC report example

Genome index build example

(Manual: http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml)

  • Required for fast search of query sequence during mapping
  • Takes fasta with reference as input (comma-separated list) and prefix name.
  • Example run and resulting database:
bowtie2-build -h

bowtie2-build chr1.fa,chr2.fa,chr3.fa,chr4.fa hg38

# Check the results: 

ls -hs

# 938M hg38.1.bt2  701M hg38.2.bt2   12K hg38.3.bt2  701M hg38.4.bt2  3.0G hg38.fa  938M hg38.rev.1.bt2  701M hg38.rev.2.bt2

bowtie2-inspect -n hg38

Don't run this code, it's FYI

Read mapping example

  • Takes query FASTQ and indexed database as input.
  • Output is in SAM or BAM format
    (SAM format specification: https://samtools.github.io/hts-specs/SAMv1.pdf)
  • SAM is a TSV file with header, BAM is its compressed binary version. The columns are:

Read mapping example

FASTQ

+ indexed database

-> SAM file

Samtools example

(Manual: http://www.htslib.org/doc/samtools.html)

  • A toolkit designed for SAM/BAM manipulations
  • View:
     
  • Statistics assessment:
     
  • Format conversion:
     
  • Filtering:





     
samtools view file.sam
samtools view -F 4 input.sam
samtools stats file.sam
samtools view -b file.sam > file.bam

Bedtools example

(Manual: https://bedtools.readthedocs.io/en/latest/content/bedtools-suite.html
BED format specification: https://genome.ucsc.edu/FAQ/FAQformat.html)

bedtools bamtobed -i file.bam > file.bed

genomecov

map

intersect

Other formats

  1. BED



     
  2. BEDGRAPH




     
  3. BIGWIG: binary compressed format, created with UCSC tools (see below)

UCSC genome browser & UCSC tools

UCSC tools example

Full set of compiled tools: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/

  • We will need file conversion, check the documentation:
bedGraphToBigWig -h

Next step: index build and mapping

These steps are time-consuming, but we have to complete them:

  1. Build genome index with name prefix hg38_chr5-6_index:



     
  2. Run mapping of your FASTQ file for the index hg38_chr5-6_index:
cd GENOME/
bowtie2-build dm6.fa
bowtie2-build -h
bowtie2 -h
bowtie2 --very-sensitive -x ./GENOME/dm6 <file.fastq> -S <file.sam>
less <file.sam>
ls 
cd ../

Don't run this code, it's FYI

Filtering

 

  • Filter only uniquely mapped reads with high alignment score, sort resulting file:

     
samtools view -h -F 4 <file.sam> | less
samtools --help
samtools view -h -q 10 <file.sam> > <file_filtered.sam>
samtools view -b -q 10 <file.sam> > <file_filtered.bam>
samtools sort <file_filtered.bam> > <file_filtered.sorted.bam>

Don't run this code, it's FYI

File format conversion

  • Read the documentation of bedtools:

     
  • Convert BAM to BEDGRAPH genome coverage:
     
  • Create files with genomic windows (1Kbp bin size, don't forget to use your chromosome!). Convert BAM to BEDGRAPH coverage of windows:
bedtools makewindows -g GENOME/chrom_sizes.tsv -w 1000 > hg38_windows.1000.bed
grep "chr5" hg38_windows.1000.bed > chr5_windows.1000.bed

bedtools genomecov -ibam <file_filtered.sorted.bam> -bga -split > <file.genomecov.bedgraph>
bedtools genomecov -h
bedtools makewindows -h
bedtools coverage -h

bedtools coverage -counts -a chr5_windows.1000.bed -b <file_filtered.sorted.bam> > <file.binned.bedgraph>
  • Check the resulting files, you should see genome coverage
    files (*.genomecov.*) and binned datasets (*.binned.*):
ls
less <file.genomecov.bedgraph>
less <file.binned.bedgraph>

Don't run this code, it's FYI

BEDGRAPH tracks visualization

We will use UCSC browser for bedgraph files visualization.

  

UCSC genome browser

  • Find "add custom tracks button".
  • Check the upload form:

 

 

 

 

 

 

 

 

 

  • Upload your modified BEDGRAPH files (with headers) to UCSC ("Choose file" -> "Submit").

Experiments comparison

  • Convert BEDGRAPH to BIGWIG:

     
  • Rename your final file (substitute YOUR_CELL_LINE and CHROMOSOME):

     
  • Find three of your colleagues with the same chromosome as you. Exchange your BIGWIG files and upload them to the server. Thus you should collect at least 4 BIGWIG files with recognisable names (cell line and chromosome should be present in the file names).
mv <file.genomecov.bw> <YOUR_CELL_LINE-CHROMOSOME.bw>
bedGraphToBigWig <file.genomecov.bedgraph> ./GENOME/chrom_sizes.tsv <file.genomecov.bw>

Samples comparison

Task 1 (1 point):  Provide the final set of commands that you've used and briefly justify the parameters.

Task 2 (1 point): Report the final scatteplot for 100 Kb. Describe your observations.
Task 3 (*1 point): Repeat the procedure for 10 Kb  and 1 Kb and report the changes in observations. At what resolution you might observe the differential DNase I signal?

  1. Collect at least 4 DNase I results for your chromosome in bigWig format. One of them should be yours, the others you may take from /home/shared/EpiPract1/. you may use conda environment "hic" (see instructions on activation can be found in EpiPract3).
  2. Use multiBigwigSummary to create a summary file. Use bins mode, bin size 100 Kb:

     
  3. Plot correlations obtained from your file as a scatterplot:


     
  4. Try to adjust the parameters of the plot to improve the analysis. Try plotting in log-scale (--log1p). If you get the warning about the outliers, try to fix it: use --removeOutliers and --corMethod spearman. Which method worked?
  5. * Repeat the procedure for 10 Kb and 1 Kb. You may want to create another intermediary summary file for that. Inspect the changes.
multiBigwigSummary bins --binSize 100000 -b ${file1.bw} ${file2.bw} ${file3.bw} ${file4.bw} -o ${summary.npz}
plotCorrelation -in ${summary.npz} --corMethod pearson --skipZeros --whatToPlot scatterplot -o ${scatterplot.png}

Experiments comparison

6. Plot correlations for 100 Kb as a heatmap. Use the parameters that you found to be representative for the scatterplots. For example:
 

 

7. Inspect and describe your observations. Adjust the visualization, if needed. 

 

 

plotCorrelation -in ${summary.npz} --corMethod pearson --skipZeros --whatToPlot heatmap --colorMap RdYlBu --plotNumbers -o ${heatmap.png}

Result of clustering

Correlation coefficient

Labels are not representative
(correct if needed)

Practical Part: Enrichment Profiles

We will:

  • Call TADs in S2 cell line (Wang et al. 2017),
  • Plot the enrichment profiles for multiple factors.

 

We'll need conda environment "hic" (see instructions on activation can be found in EpiPract3).

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