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html_document	/EPI_2023_module2	Epigenomics Analysis Module 2 Lab	Workshop Pages for Students	Epigenomic Analysis 2023	/site_images/CBW_Epigenome-data_icon.jpg	https://bioinformaticsdotca.github.io/EPI_2023

Introduction to ChIP-Seq Analysis

by Martin Hirst and Edmund Su

Breakdown of markdown file

At the start of each step, the intention will declare.
this is then follow by a code block

Code:

Like this! This is the main code to run for the step.

Additionally the code block will include a header to indicate what environment to the run code for example:
###Shell###
pwd

###R###
getwd()

explaining for commands will be broken down following code block
pwd & getwd()
- see your work directory
sprinkled throughout will also include comments

Note

Important points and considerations will also be raised as so.

Module 2. Peak Calling

Step 1: Dedup BAM file

We first remove duplicates here b/c MACS2(peak caller) identifies duplicates via genomic coordinates (excessive removal of a lot reads).
dedup now and instruct our peak caller to "keep duplicates" after.

Code:

###Shell###
treatment=MCF10A_H3K27ac_chr19
treatment_bam=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.bam
treatment_dedup=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.dedup.bam
samtools view -@4 ${treatment_bam} -bh -q10 -F1028 -o ${treatment_dedup}

treatment=MCF10A_H3K27me3_chr19
treatment_bam=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.bam
treatment_dedup=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.dedup.bam
samtools view -@4 ${treatment_bam} -bh -q10 -F1028 -o ${treatment_dedup}

treatment=MCF10A_H3K4me3_chr19
treatment_bam=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.bam
treatment_dedup=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.dedup.bam
samtools view -@4 ${treatment_bam} -bh -q10 -F1028 -o ${treatment_dedup}

treatment=MCF10A_ATAC_chr19
treatment_bam=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.bam
treatment_dedup=~/workspace/module123/alignments/${treatment}.sorted.dup_marked.dedup.bam
samtools view -@4 ${treatment_bam} -bh -q10 -F1028 -o ${treatment_dedup}

input=MCF10A_input_chr19
input_bam=~/workspace/module123/alignments/${input}.sorted.dup_marked.bam
input_dedup=~/workspace/module123/alignments/${input}.sorted.dup_marked.dedup.bam
samtools view -@4 ${input_bam} -bh -q10 -F1028 -o ${input_dedup}

Run time <1 min per command
Silently completes; no log needed
samtools view -@4 ${input_bam} -bh -q10 -F1028 -o ${input_dedup}
- we subset our file based on the following criteria
- -bh we would like the output to be in BAM format and contain the header
- -q10 reads with mapping qual >10
- -F1028 any reads that do not have the flags UNMAP and DUP
  - whats the difference between -f and -F?

Step 2A : Run Peak Caller (narrow)

MACS has two modes for narrow marks and broad marks.
Refer to this link to reference which mark are narrow or broad

Code:

###Shell###
mkdir -p ~/workspace/module123/peaks
name=MCF10A_H3K27ac
treatment=~/workspace/module123/alignments/${name}_chr19.sorted.dup_marked.dedup.bam
input=~/workspace/module123/alignments/MCF10A_input_chr19.sorted.dup_marked.dedup.bam


macs2 callpeak -t ${treatment} -c ${input} -f BAMPE -g 58617616 -n ${name} --keep-dup all --outdir ~/workspace/module123/peaks/ --bdg 1> ~/workspace/module123/peaks/${name}.out.log 2> ~/workspace/module123/peaks/${name}.err.log

name=MCF10A_H3K4me3
treatment=~/workspace/module123/alignments/${name}_chr19.sorted.dup_marked.dedup.bam
input=~/workspace/module123/alignments/MCF10A_input_chr19.sorted.dup_marked.dedup.bam


macs2 callpeak -t ${treatment} -c ${input} -f BAMPE -g 58617616 -n ${name} --keep-dup all --outdir ~/workspace/module123/peaks/ --bdg 1> ~/workspace/module123/peaks/${name}.out.log 2> ~/workspace/module123/peaks/${name}.err.log

macs2 callpeak -t ${treatment} -c ${input} -f BAMPE -g 58617616 -n ${name} --keep-dup all --outdir ~/workspace/module123/peaks/ --bdg 1> ~/workspace/module123/peaks/${name}.out.log 2> ~/workspace/module123/peaks/${name}.err.log
- macs2 callpeak General purpose peak calling mode
- -t ${treatment} Treatment file, can provide more than one to "pool"
- -c ${input} Control File/Input
- -f BAMPE Instructs MACS2 on what kind of file to expect. Single/Paired-end bed/bam
- -g hs Sets appropriate genome size for background sampling. Typically would set hs=human mm=mouse but in our case we use the HG38 size of chr19
- -n ${name} name or prefix to use
- -q 0.05 FDR q value default
- --outdir ~/workspace/module123/peaks/ where to output files otherwise stores in current working directory
- --bdg outputs pileup into bedgraph (a BED file where the fourth column is pileup/fragment count/coverage)
- 1> ~/workspace/module123/peaks/${name}.out.log output log
- 2> ~/workspace/module123/peaks/${name}.err.log error log
- let's inspect the peaks file
  - chromosome name
  - peak start
  - peak stop
  - peak name
  - int(-10*log10pvalue)
  - strand
  - Fold change at peak summit
  - -log10 P-value at Peak
  - -log10 Q-value at Peak
  - Summit position relative to peak

Step 2B : Run Peak Caller (broad)

Code:

###Shell###
mkdir -p ~/workspace/module123/peaks
name=MCF10A_H3K27me3
treatment=~/workspace/module123/alignments/${name}_chr19.sorted.dup_marked.dedup.bam
input=~/workspace/module123/alignments/MCF10A_input_chr19.sorted.dup_marked.dedup.bam

macs2 callpeak -t ${treatment} -c ${input} -f BAMPE -g 58617616 -n ${name} --keep-dup all --outdir ~/workspace/module123/peaks/ --bdg --broad 1> ~/workspace/module123/peaks/${name}.out.log 2> ~/workspace/module123/peaks/${name}.err.log

macs2 callpeak -t ${treatment} -c ${input} -f BAMPE -g 58617616 -n ${name} --keep-dup all --outdir ~/workspace/module123/peaks/ --bdg --broad 1> ~/workspace/module123/peaks/${name}.out.log 2> ~/workspace/module123/peaks/${name}.err.log
- --broad for broad marks - stitches small peaks together
- let's note the differences between broadPeak vs gappedPeak
- gapped has the additional columns starting from strand where most of the differences are visualization support for the narrrow peaks within the broadPeak
  - thickStart
  - thickEnd
  - itemRgb
  - blockCount
  - blockSizes
  - blockStarts
  - signalValue
  - pValue
  - qValue

Step 3A : Run Peak Caller for ATAC - Make fragment file

Convert our BAM to BED to easily manipulate coordinates
perform shift to account for Tn5 binding as a dimer and inserts two adapters separated by 9 bp

Note

This step can also be done for chipseq

Code:

name=MCF10A_ATAC
dedup=~/workspace/module123/alignments/${name}_chr19.sorted.dup_marked.dedup.bam
nsort=~/workspace/module123/alignments/${name}_chr19.nsorted.dup_marked.dedup.bam
tags=~/workspace/module123/peaks/${name}_chr19.frags.bed
samtools sort -@ 8 -n ${dedup} -o ${nsort}
bedtools bamtobed -bedpe -mate1 -i ${nsort} > ${tags}

samtools sort -@ 8 -n ${dedup} -o ${nsort}
- specifying the -n sorts according to read name rather than coordinates by default
bedtools bamtobed -bedpe -mate1 -i ${nsort} > ${tags}
- bedtools bamtobed converts our BAM to BED/coordinates for our reads
- -bedpe instructs bedtools to report read pairs instead of each read individually
- unpaired reads will emit a warning
- -mate1 instructs bedtools to report read1 information first followed by read2
- columns : chrR1,startR1,endR1,chrR2,startR2,endR2,readName,mapQ,strandR1,strandR2
- e.g. chr19 4534039 4534190 chr19 4534110 4534248 SRR20814384.28 60 + -
- bedtools will check if file is name sorted or coordinate sorted

Step 3B : Run Peak Caller for ATAC - Tn5 Shift

perform shift to account for Tn5 binding as a dimer and inserts two adapters separated by 9 bp

Note

Tn5 shift can be skipped if one is not interested in footprinting.

Code:

###Shell###
name=MCF10A_ATAC

tags=~/workspace/module123/peaks/${name}_chr19.frags.bed
tn5_tags=~/workspace/module123/peaks/${name}_chr19.frags.tn5.bed

<!-- {% raw %} -->

cat ${tags} \
| awk 'BEGIN{{OFS="\t"}}{{printf "%s\t%s\t%s\tN\t1000\t%s\n%s\t%s\t%s\tN\t1000\t%s\n",$1,$2,$3,$9,$4,$5,$6,$10}}'\
| awk 'BEGIN{{OFS = "\t"}} {{if ($6 == "+") {{$2 = $2 + 4}} else if ($6 == "-") {{$3 = $3 - 5}} if ($2 >= $3) {{ if ($6 == "+") {{$2 = $3 - 1}} else {{$3 = $2 + 1}} }} print $0}}'\
> ${tn5_tags}
<!-- {% endraw %} -->

cat ${tags} | awk 'BEGIN{{OFS="\t"}}{{printf "%s\t%s\t%s\tN\t1000\t%s\n%s\t%s\t%s\tN\t1000\t%s\n",$1,$2,$3,$9,$4,$5,$6,$10}}' | awk 'BEGIN {{OFS = "\t"}} {{if ($6 == "+") {{$2 = $2 + 4}} else if ($6 == "-") {{$3 = $3 - 5}} if ($2 >= $3) {{ if ($6 == "+") {{$2 = $3 - 1}} else {{$3 = $2 + 1}} }} print $0}}' > ${tn5_tags}
- read tagFile | rearrange columns | shift Tag depending on strand
- see below for a more through breakdown of code

awk '
BEGIN{{OFS="\t"}}
{
    {
        printf "%s\t%s\t%s\tN\t1000\t%s\n%s\t%s\t%s\tN\t1000\t%s\n",
        $1,$2,$3,$9,$4,$5,$6,$10
    }
}'

BEGIN{{OFS="\t"}} output file as tab delimited
printf "%s\t%s\t%s\tN\t1000\t%s\n%s\t%s\t%s\tN\t1000\t%s\n",$1,$2... print string where %s is substituted with the next specified element
- equivalent to print chrR1,startR1,endR1,"N","1000",strandR1,chrR2,startR2,endR2,strandR2

awk '
BEGIN {{OFS = "\t"}} 
{
    {
        if ($6 == "+") 
            {{$2 = $2 + 4}} 
        else if ($6 == "-") 
            {{$3 = $3 - 5}} 
        if ($2 >= $3) 
        {
            { 
                if ($6 == "+") 
                {{$2 = $3 - 1}} 
                else 
                {{$3 = $2 + 1}} 
            }
        } print $0
    }
}' 
> ${tn5_tags}

if fragment is on + shifted 4 bp to the right
if fragment is on - shift 5 bp to the left
- while this is recommended, others have shifted 4/4 instead of 4/5
after the fix is start coordinates is greater than

Step 3C : Run Peak Caller for ATAC - Peak calling

Code:

###Shell###
name=MCF10A_ATAC
tn5_tags=~/workspace/module123/peaks/${name}_chr19.frags.tn5.bed

macs2 callpeak \
-t ${tn5_tags} \
-f BED \
-n ${name} \
-g 58617616 \
-p 0.01 \
--shift -100 \
--extsize 200 \
--nomodel \
--bdg \
--keep-dup all \
--outdir ~/workspace/module123/peaks/

macs2 callpeak -t /home/ubuntu/workspace/module123/peaks/MCF10A_ATAC_chr19.frags.tn5.bed -f BED -n {name} -g 58617616 -p 0.01 --nomodel --extsize 200 --shift -100 --bdg --keep-dup all
- -f BED we specify a BED format input instead of BAM
- -name ${name} name of file
- -g 58617616 size of chr19
- -p 0.01 Pvalue cutoff
- --nomodel off by default, normally calculates the extsize and shift parameters
- --extsize 200, b/c Tn5's activity is on the 5', we extend the 5' to get more representation
- --shift -100 should be -1*(extsize/2), this focuses MACS on our 5'
- --bdg generate a pileup bedgraph
- --keep-dup all retain "duplicates". As we've already filtered out duplicates, MACS2 will call duplicates via genomic coordinates
- --call-summits

Step 4 : Blacklist removal

Problematic regions can obscure our results, thus filter any peaks that coincide with those regions.

Code:

###Shell###
wget https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz -O ~/workspace/module123/resources/hg38_blacklist.bed.gz

gunzip ~/workspace/module123/resources/hg38_blacklist.bed.gz

blacklist=~/workspace/module123/resources/hg38_blacklist.bed

sample="MCF10A_H3K27ac_peaks"
bedtools intersect -v -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklistRemoved.narrowPeak
bedtools intersect -u -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklist.narrowPeak

sample="MCF10A_H3K4me3_peaks"
bedtools intersect -v -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklistRemoved.narrowPeak
bedtools intersect -u -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklist.narrowPeak

sample="MCF10A_H3K27me3_peaks"
bedtools intersect -v -a ~/workspace/module123/peaks/${sample}.broadPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklistRemoved.broadPeak
bedtools intersect -u -a ~/workspace/module123/peaks/${sample}.broadPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklist.broadPeak

sample="MCF10A_ATAC_peaks"
bedtools intersect -v -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklistRemoved.narrowPeak
bedtools intersect -u -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist} > ~/workspace/module123/peaks/${sample}.blacklist.narrowPeak

bedtools intersect -u -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist}
- bedtools intersect identify elements from fileA and fileB that overlap genomic coordiante wise
- -u by default, bedtools will output every time a element intersection is detected i.e. if fileA_ele1 overlaps fileB_ele1 and fileB_ele2. The -u instead reports the element once regardless of how many overlaps
bedtools intersect -v -a ~/workspace/module123/peaks/${sample}.narrowPeak -b ${blacklist}
- -v reverse the behaviour, identify elements that do not overlap
we'll return to this later when we can visualize the peaks

Step 5A : Visualization of pileup tracks

in the next steps, we convert our pipleup bedgraphs and bed peak files into a smaller managable formats.

Code:

###Shell###
mkdir -p ~/workspace/module123/{bigBed,bigWig}
wget https://hgdownload.cse.ucsc.edu/goldenpath/hg38/bigZips/hg38.chrom.sizes -O ~/workspace/module123/resources/hg38.chrom.sizes

sample="MCF10A_H3K27ac"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bedgraph=~/workspace/module123/peaks/${sample}_treat_pileup.bdg
output_bigwig=~/workspace/module123/bigWig/${sample}_treat_pileup.bw

sort -k1,1 -k2,2n ${input_bedgraph} > ~/workspace/module123/bigWig/tmp
bedGraphToBigWig ~/workspace/module123/bigWig/tmp ${chrom_sizes} ${output_bigwig}
rm ~/workspace/module123/bigWig/tmp

input_bedgraph=~/workspace/module123/peaks/MCF10A_H3K27me3_control_lambda.bdg
output_bigwig=~/workspace/module123/bigWig/MCF10A_Input_control_pileup.bw

sort -k1,1 -k2,2n ${input_bedgraph} > ~/workspace/module123/bigWig/tmp
bedGraphToBigWig ~/workspace/module123/bigWig/tmp ${chrom_sizes} ${output_bigwig}
rm ~/workspace/module123/bigWig/tmp

sort -k1,1 -k2,2n ${input_bedgraph} sorting the BED file
- -k1,1 sort first by chromosome alphabetically
- -k2,2n sort secondarily by genomic coordinates
bedGraphToBigWig convert bedGraph file to bigWig

Step 5B : Visualization of pileup tracks continued

we're converting the bedgraph file to a bigWig file.
note bedgraph is an extension of bed(genomic coordinates) + a column with a numeric value
- in our case pileup/coverage
a bigWig is a binary version of a wig file
- wig has a different format : https://useast.ensembl.org/info/website/upload/wig.html
- the preferred convention for displaying data on a track

Code:

###Shell###
sample="MCF10A_H3K27me3"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bedgraph=~/workspace/module123/peaks/${sample}_treat_pileup.bdg
output_bigwig=~/workspace/module123/bigWig/${sample}_treat_pileup.bw

sort -k1,1 -k2,2n ${input_bedgraph} > ~/workspace/module123/bigWig/tmp
bedGraphToBigWig ~/workspace/module123/bigWig/tmp ${chrom_sizes} ${output_bigwig}
rm ~/workspace/module123/bigWig/tmp

sample="MCF10A_H3K4me3"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bedgraph=~/workspace/module123/peaks/${sample}_treat_pileup.bdg
output_bigwig=~/workspace/module123/bigWig/${sample}_treat_pileup.bw

sort -k1,1 -k2,2n ${input_bedgraph} > ~/workspace/module123/bigWig/tmp
bedGraphToBigWig ~/workspace/module123/bigWig/tmp ${chrom_sizes} ${output_bigwig}
rm ~/workspace/module123/bigWig/tmp

sample="MCF10A_ATAC"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bedgraph=~/workspace/module123/peaks/${sample}_treat_pileup.bdg
output_bigwig=~/workspace/module123/bigWig/${sample}_treat_pileup.bw

sort -k1,1 -k2,2n ${input_bedgraph} > ~/workspace/module123/bigWig/tmp
bedGraphToBigWig ~/workspace/module123/bigWig/tmp ${chrom_sizes} ${output_bigwig}
rm ~/workspace/module123/bigWig/tmp

Step 5C : Visualization of peak tracks

next we convert our BED files Code:

###Shell###
mkdir -p ~/workspace/module123/{bigBed,bigWig}
wget https://hgdownload.cse.ucsc.edu/goldenpath/hg38/bigZips/hg38.chrom.sizes -O ~/workspace/module123/resources/hg38.chrom.sizes

sample="MCF10A_H3K27ac"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bed=~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak
output_bigbed=~/workspace/module123/bigBed/${sample}.blacklistRemoved.bb


sort -k1,1 -k2,2n ${input_bed} | cut -f1-3 > ~/workspace/module123/bigBed/tmp
bedToBigBed ~/workspace/module123/bigBed/tmp ${chrom_sizes} ${output_bigbed}
rm ~/workspace/module123/bigBed/tmp

sort -k1,1 -k2,2n ${input_bedgraph} sorting the BED file
- -k1,1 sort first by chromosome alphabetically
- -k2,2n sort secondarily by genomic coordinates
bedGraphToBigWig convert bedGraph file to bigBed

Step 5D : Visualization of peak tracks continued

Code:

###Shell###
sample="MCF10A_ATAC"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bed=~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak
output_bigbed=~/workspace/module123/bigBed/${sample}.blacklistRemoved.bb


sort -k1,1 -k2,2n ${input_bed} | cut -f1-3 > ~/workspace/module123/bigBed/tmp
bedToBigBed ~/workspace/module123/bigBed/tmp ${chrom_sizes} ${output_bigbed}
rm ~/workspace/module123/bigBed/tmp

sample="MCF10A_H3K4me3"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bed=~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak
output_bigbed=~/workspace/module123/bigBed/${sample}.blacklistRemoved.bb


sort -k1,1 -k2,2n ${input_bed} | cut -f1-3 > ~/workspace/module123/bigBed/tmp
bedToBigBed ~/workspace/module123/bigBed/tmp ${chrom_sizes} ${output_bigbed}
rm ~/workspace/module123/bigBed/tmp

sample="MCF10A_H3K27me3"
chrom_sizes=~/workspace/module123/resources/hg38.chrom.sizes
input_bed=~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.broadPeak
output_bigbed=~/workspace/module123/bigBed/${sample}.blacklistRemoved.bb


sort -k1,1 -k2,2n ${input_bed} | cut -f1-3 > ~/workspace/module123/bigBed/tmp
bedToBigBed ~/workspace/module123/bigBed/tmp ${chrom_sizes} ${output_bigbed}
rm ~/workspace/module123/bigBed/tmp

Step 5E : Visualization of peaks and tracks

can either download your tracks and load them from files or via URL
colours used:
- H3K27ac (~/workspace/module123/bigWig/MCF10A_H3K27ac_treat_pileup.bw) : blue
- H3K27me3 (~/workspace/module123/bigWig/MCF10A_H3K27me3_treat_pileup.bw): Brown
- H3K4me3 (~/workspace/module123/bigWig/MCF10A_H3K4me3_treat_pileup.bw) : Green
- ATAC (~/workspace/module123/bigWig/MCF10A_ATAC_treat_pileup.bw): LightBlue
- Input/Control (~/workspace/module123/bigWig/MCF10A_Input_control_pileup.bw): Black

BCL3 start site is enriched with H3K4me3 and H3K27ac and accessible

Genes around RDH8 show enrichment of H3K27me3 and lack of accessibility and enrichment for H3K27ac and H3K4me3

Blacklist identified region hightlighted in red, represented by both H3K27me3 and ATAC

Step 6A : Quality Control (Enrichment in key genomic areas)

A way to determine the efficacy of your enrichment is to benchmark % of reads to called peaks (FRIP) or known regions
in our toy example we'll be examining enrichment at promoters (TSS+/- 2kb) and encode defining enhancer regions
For H3K27me3 we'd typically look at HOX regions however no HOX regions on chr19 Code :

###Shell###
mkdir ~/workspace/module123/qc
wget https://www.bcgsc.ca/downloads/esu/touchdown/hg38v79_genes_tss_2000.bed -O ~/workspace/module123/resources/hg38v79_genes_tss_2000.bed

sort -k1,1 -k2,2n ~/workspace/module123/resources/hg38v79_genes_tss_2000.bed > tmp
mv tmp ~/workspace/module123/resources/hg38v79_genes_tss_2000.bed

wget https://www.bcgsc.ca/downloads/esu/touchdown/encode_enhancers_liftover.bed -O ~/workspace/module123/resources/encode_enhancers_liftover.bed

TSS=~/workspace/module123/resources/hg38v79_genes_tss_2000.bed
ENH=~/workspace/module123/resources/encode_enhancers_liftover.bed

sample="MCF10A_H3K27ac"
query_bam=~/workspace/module123/alignments/${sample}_chr19.sorted.dup_marked.bam

samtools view -@4 -q 10 -F 1028 $query_bam -c
samtools view -@4 -q 10 -F 1028 $query_bam -L ${ENH} -c
samtools view -@4 -q 10 -F 1028 $query_bam -L ~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak -c

samtools view -@4 -q 10 -F 1028 $query_bam -L ~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak -c
- samtools view parse through our BAM file
- -@4 resources to use
- -q 10 filter for reads with mapping quality(MAPQ) >10
- -F 1028 Remove reads that have the following flags:UNMAP,DUP
- $query_bam Bam of interest
- -c count the number of reads that fulfil the criteria
- -L perform actions on reads that fall within the specified genomic coordiantes (Bed File)

Step 6B : Quality Control (Enrichment in key genomic areas) Continued:

Code:

###Shell###
TSS=~/workspace/module123/resources/hg38v79_genes_tss_2000.bed
ENH=~/workspace/module123/resources/encode_enhancers_liftover.bed

for histone in H3K27ac H3K27me3 H3K4me3 ATAC input;
do
    sample="MCF10A_${histone}"
    query_bam=~/workspace/module123/alignments/${sample}_chr19.sorted.dup_marked.bam
    samtools view -@4 -q 10 -F 1028 $query_bam -c > ~/workspace/module123/qc/col_${histone}
    samtools view -@4 -q 10 -F 1028 $query_bam -L ${TSS} -c >> ~/workspace/module123/qc/col_${histone}
    samtools view -@4 -q 10 -F 1028 $query_bam -L ${ENH} -c >> ~/workspace/module123/qc/col_${histone}

    if [[ "$histone" == "H3K27me3" ]]; then
        peaks=~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.broadPeak
        samtools view -@4 -q 10 -F 1028 $query_bam -L ~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.broadPeak -c >> ~/workspace/module123/qc/col_${histone}
    elif [[ "$histone" == "H3K27ac" || "$histone" == "H3K4me3" || "$histone" == "ATAC" ]]; then
        peaks=~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak
        samtools view -@4 -q 10 -F 1028 $query_bam -L ~/workspace/module123/peaks/${sample}_peaks.blacklistRemoved.narrowPeak -c >> ~/workspace/module123/qc/col_${histone}
    else 
        echo
    fi
done

paste ~/workspace/module123/qc/col_H3K27ac ~/workspace/module123/qc/col_H3K27me3 ~/workspace/module123/qc/col_H3K4me3 ~/workspace/module123/qc/col_ATAC ~/workspace/module123/qc/col_input > ~/workspace/module123/qc/integers.tsv
paste \
<(awk '{print $1/442829*100}' ~/workspace/module123/qc/col_H3K27ac) \
<(awk '{print $1/1610910*100}' ~/workspace/module123/qc/col_H3K27me3) \
<(awk '{print $1/1512352*100}' ~/workspace/module123/qc/col_H3K4me3) \
<(awk '{print $1/2751833*100}' ~/workspace/module123/qc/col_ATAC) \
<(awk '{print $1/1023265*100}' ~/workspace/module123/qc/col_input) > ~/workspace/module123/qc/percentages.tsv

paste ~/workspace/module123/qc/col_H3K27ac ~/workspace/module123/qc/col_H3K27me3 ~/workspace/module123/qc/col_H3K4me3 ~/workspace/module123/qc/col_ATAC ~/workspace/module123/qc/col_input > ~/workspace/module123/qc/integers.tsv

paste lets us aggregate our results where each is a column)
Reads enrichment in key region

	H3K27ac	H3K27me3	H3K4me3	ATAC	Input
Total	442829	1610910	1512352	2751833	1023265
TSS	127577	298326	1087032	924326	194996
Enhancer	242489	760089	834053	1721794	514115
In Peaks	69584	783294	1239717	1082674

Reads % enrichment in key region

	H3K27ac	H3K27me3	H3K4me3	ATAC	Input
TSS	28.8095	18.5191	71.8769	33.5895	19.0563
Enhancer	54.7591	47.1838	55.1494	62.569	50.2426
In Peaks	15.7135	48.6243	81.9728	39.3437

H3K27ac FRIP is poor as a result of the poorer sequencing depth.
H3K4me3 is performing well: high FRIP and enriched in promoter regions

Server resources

QC Resources

###TSS+/-2kb
mkdir ~/workspace/module123/qc
wget https://www.bcgsc.ca/downloads/esu/touchdown/hg38v79_genes_tss_2000.bed -O ~/workspace/module123/resources/hg38v79_genes_tss_2000.bed

sort -k1,1 -k2,2n ~/workspace/module123/resources/hg38v79_genes_tss_2000.bed > tmp
mv tmp ~/workspace/module123/resources/hg38v79_genes_tss_2000.bed

###Enhancer liftover
wget https://www.bcgsc.ca/downloads/esu/touchdown/encode_enhancers_liftover.bed -O ~/workspace/module123/resources/encode_enhancers_liftover.bed

###Blacklist
wget https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz -O ~/workspace/module123/resources/hg38_blacklist.bed.gz

gunzip ~/workspace/module123/resources/hg38_blacklist.bed.gz

hg38v79_genes_tss_2000.bed
- Generated by downloading Ensemblv79 GTF convert to Bed +/-2kb of TSS. See https://www.biostars.org/p/56280/
encode_enhancers_liftover.bed
- download various ChroHMM state7 and merge

Encode Bed

ls ~/CourseData/EPI_data/module123/encode_bed
basal.H3K27ac.peak_calls.bed
basal.H3K27me3.peak_calls.bed
basal.H3K4me1.peak_calls.bed
basal.H3K4me3.peak_calls.bed
lp.H3K27ac.peak_calls.bed
lp.H3K27me3.peak_calls.bed
lp.H3K4me1.peak_calls.bed
lp.H3K4me3.peak_calls.bed
luminal.H3K27ac.peak_calls.bed
luminal.H3K27me3.peak_calls.bed
luminal.H3K4me1.peak_calls.bed
luminal.H3K4me3.peak_calls.bed
stromal.H3K27ac.peak_calls.bed
stromal.H3K27me3.peak_calls.bed
stromal.H3K4me1.peak_calls.bed
stromal.H3K4me3.peak_calls.bed

https://epigenomesportal.ca/tracks/CEEHRC/hg38/
Breast Basal CEMT0035
Breast Stromal CEMT0036
Breast Luminal CEMT0037
Breast Luminal Progenitor CEMT0038

Encode BigWig

ls ~/CourseData/EPI_data/module123/encode_bigWig
basal.H3K27ac.signal_unstranded.bigWig
basal.H3K27me3.signal_unstranded.bigWig
basal.H3K4me1.signal_unstranded.bigWig
basal.H3K4me3.signal_unstranded.bigWig
lp.H3K27ac.signal_unstranded.bigWig
lp.H3K27me3.signal_unstranded.bigWig
lp.H3K4me1.signal_unstranded.bigWig
lp.H3K4me3.signal_unstranded.bigWig
luminal.H3K27ac.signal_unstranded.bigWig
luminal.H3K27me3.signal_unstranded.bigWig
luminal.H3K4me1.signal_unstranded.bigWig
luminal.H3K4me3.signal_unstranded.bigWig
stromal.H3K27ac.signal_unstranded.bigWig
stromal.H3K27me3.signal_unstranded.bigWig
stromal.H3K4me1.signal_unstranded.bigWig
stromal.H3K4me3.signal_unstranded.bigWig

https://epigenomesportal.ca/tracks/CEEHRC/hg38/
Breast Basal CEMT0035
Breast Stromal CEMT0036
Breast Luminal CEMT0037
Breast Luminal Progenitor CEMT0038

MCF10A Fastq

ls ~/CourseData/EPI_data/module123/fastq
MCF10A.ATAC.chr19.R1.fastq.gz
MCF10A.ATAC.chr19.R2.fastq.gz
MCF10A.H3K27ac.chr19.R1.fastq.gz
MCF10A.H3K27ac.chr19.R2.fastq.gz
MCF10A.H3K27me3.chr19.R1.fastq.gz
MCF10A.H3K27me3.chr19.R2.fastq.gz
MCF10A.H3K4me3.chr19.R1.fastq.gz
MCF10A.H3K4me3.chr19.R2.fastq.gz
MCF10A.Input.chr19.R1.fastq.gz
MCF10A.Input.chr19.R2.fastq.gz

MCF10A histone marks and input come courtesy of Dr.Hirst
ATACseq data originates from GSM6431322

Triplicates

CourseData/EPI_data/module123/triplicates/triplicates.csv

CourseData/EPI_data/module123/triplicates/alignments:
MCF10A_H3K4me3_chr19.CondA.Rep1.bam      MCF10A_H3K4me3_chr19.CondB.Rep2.bam      MCF10A_input_chr19.CondA.Rep3.bam
MCF10A_H3K4me3_chr19.CondA.Rep1.bam.bai  MCF10A_H3K4me3_chr19.CondB.Rep2.bam.bai  MCF10A_input_chr19.CondA.Rep3.bam.bai
MCF10A_H3K4me3_chr19.CondA.Rep2.bam      MCF10A_H3K4me3_chr19.CondB.Rep3.bam      MCF10A_input_chr19.CondB.Rep1.bam
MCF10A_H3K4me3_chr19.CondA.Rep2.bam.bai  MCF10A_H3K4me3_chr19.CondB.Rep3.bam.bai  MCF10A_input_chr19.CondB.Rep1.bam.bai
MCF10A_H3K4me3_chr19.CondA.Rep3.bam      MCF10A_input_chr19.CondA.Rep1.bam        MCF10A_input_chr19.CondB.Rep2.bam
MCF10A_H3K4me3_chr19.CondA.Rep3.bam.bai  MCF10A_input_chr19.CondA.Rep1.bam.bai    MCF10A_input_chr19.CondB.Rep2.bam.bai
MCF10A_H3K4me3_chr19.CondB.Rep1.bam      MCF10A_input_chr19.CondA.Rep2.bam        MCF10A_input_chr19.CondB.Rep3.bam
MCF10A_H3K4me3_chr19.CondB.Rep1.bam.bai  MCF10A_input_chr19.CondA.Rep2.bam.bai    MCF10A_input_chr19.CondB.Rep3.bam.bai

CourseData/EPI_data/module123/triplicates/bigWig:
CondA.Rep1.bw  CondA.Rep2.bw  CondA.Rep3.bw  CondB.Rep1.bw  CondB.Rep2.bw  CondB.Rep3.bw

CourseData/EPI_data/module123/triplicates/peaks:
CondA.Rep1_peaks.narrowPeak  CondA.Rep3_peaks.narrowPeak  CondB.Rep2_peaks.narrowPeak
CondA.Rep2_peaks.narrowPeak  CondB.Rep1_peaks.narrowPeak  CondB.Rep3_peaks.narrowPeak

triplicates were generated from MCF10A_H3K4me3 by choosing a list of exclusive peaks for condA and condB and randomly subsampling replicates accordingly

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module2.md

module2.md

Introduction to ChIP-Seq Analysis

Table of Contents

Breakdown of markdown file

Module 2. Peak Calling

Step 1: Dedup BAM file

Step 2A : Run Peak Caller (narrow)

Step 2B : Run Peak Caller (broad)

Step 3A : Run Peak Caller for ATAC - Make fragment file

Step 3B : Run Peak Caller for ATAC - Tn5 Shift

Step 3C : Run Peak Caller for ATAC - Peak calling

Step 4 : Blacklist removal

Step 5A : Visualization of pileup tracks

Step 5B : Visualization of pileup tracks continued

Step 5C : Visualization of peak tracks

Step 5D : Visualization of peak tracks continued

Step 5E : Visualization of peaks and tracks

Step 6A : Quality Control (Enrichment in key genomic areas)

Step 6B : Quality Control (Enrichment in key genomic areas) Continued:

Server resources

QC Resources

Encode Bed

Encode BigWig

MCF10A Fastq

Triplicates

Files

module2.md

Latest commit

History

module2.md

File metadata and controls

Introduction to ChIP-Seq Analysis

Table of Contents

Breakdown of markdown file

Module 2. Peak Calling

Step 1: Dedup BAM file

Step 2A : Run Peak Caller (narrow)

Step 2B : Run Peak Caller (broad)

Step 3A : Run Peak Caller for ATAC - Make fragment file

Step 3B : Run Peak Caller for ATAC - Tn5 Shift

Step 3C : Run Peak Caller for ATAC - Peak calling

Step 4 : Blacklist removal

Step 5A : Visualization of pileup tracks

Step 5B : Visualization of pileup tracks continued

Step 5C : Visualization of peak tracks

Step 5D : Visualization of peak tracks continued

Step 5E : Visualization of peaks and tracks

Step 6A : Quality Control (Enrichment in key genomic areas)

Step 6B : Quality Control (Enrichment in key genomic areas) Continued:

Server resources

QC Resources

Encode Bed

Encode BigWig

MCF10A Fastq

Triplicates