Last updated: 2020-06-01
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contact: yzheng23@fredhutch.org
File creation: March 13, 2020
Final Update: May 31, 2020
Approximate time: 60 - 120 minutes
Cleavage Under Targets and Tagmentation (CUT&Tag) is an epigenomic profiling strategy in which antibodies are bound to chromatin proteins in situ in permeabilized nuclei, and then used to tether the cut-and-paste transposase Tn5. Activation of the transposase simultaneously cleaves DNA and adds DNA sequencing adapters (“tagmentation”) for paired-end DNA sequencing. The most recent streamlined CUT&Tag protocol has successfully suppressed exposure artifacts to ensure high-fidelity mapping of the antibody-targeted protein and improved signal-to-noise over current chromatin profiling methods. Streamlined CUT&Tag can be performed in a single PCR tube from cells to amplified libraries, providing low-cost high-resolution genome-wide chromatin maps. By simplifying library preparation, CUT&Tag requires less than a day at the bench from live cells to sequencing-ready barcoded libraries. Because of low background levels, barcoded and pooled CUT&Tag libraries can be sequenced for ~$25 per sample, enabling routine genome-wide profiling of chromatin proteins and modifications that requires no special skills or equipment.
The mapping of chromatin features genome-wide has traditionally been performed using chromatin immunoprecipitation (ChIP), in which chromatin is cross-linked and solubilized and an antibody to a protein or modification of interest is used to immunoprecipitate the bound DNA. Very little has changed in the way ChIP is most generally performed since it was first described 35 years ago, and remains fraught with signal-to-noise issues and artifacts. An alternative chromatin profiling strategy is enzyme tethering in situ whereby the chromatin protein or modification of interest is targeted by an antibody or fusion protein, and the underlying DNA is marked or released. A succession of enzyme-tethering methods have been introduced over the past two decades. Cleavage Under Targets & Tagmentation (CUT&Tag) is a tethering method that uses a protein-A-Tn5 (pA-Tn5) transposome fusion protein. Permeabilized cells or nuclei are incubated with antibody to a specified chromatin protein, and then pA-Tn5 loaded with mosaic end adaptors is successively tethered to antibody-bound sites. Activation of the transposome by adding magnesium ions results in the integration of the adaptors into nearby DNA. These are then amplified to generate sequencing libraries. Antibody-tethered Tn5-based methods achieve high sensitivity owing to stringent washing of samples after pA-Tn5 tethering and the high efficiency of adaptor integration. The improved signal-to-noise relative to ChIP-seq translates to an order-of-magnitude reduction in the amount of sequencing required to map chromatin features. Therefore, we use barcoded PCR primers to enable sample-pooling (typically up to 90 samples) for paired-end sequencing on Illumina NGS sequencers.
With all the differences between ChIP-seq and CUT&Tag data, we launched this tutorial tailored for processing and analyzing CUT&Tag data.
This tutorial is designed for processing and analyzing CUT&Tag data following the Bench top CUT&Tag V.3 protocol, an enzyme-tethering strategy that provides efficient high-resolution sequencing libraries for profiling diverse chromatin components.
Linux system
FastQC(version >= 0.11.9)
Bowtie2 (version >= 2.3.4.3)
samtools (version >= 1.10)
bedtools (version >= 2.29.1)
Picard (version >= 2.18.29)
SEACR (version >= 1.3)
deepTools (version >= 2.0)
In this tutorial, we use data from Kaya-Okur et al. (2020).
We will do the data downloading, pre-processing and processing on each histone sample respectively. In this tutorial, we will use H3K27me3 replicate 1 as the illustration.
First, we need to denote the histone name and project path.
##== linux command ==##
histName="K27me3_rep1"
projPath="/path/to/project/where/data/and/results/are/saved"
Data were downloaded from http://heniweb.fhcrc.org/data/hs20190927_SH.html
.
##== linux command ==##
mkdir -p $projPath/data
wget -P $projPath/data path/to/open/source/data
##== linux command ==##
mkdir -p ${projPath}/fastq
cat ${projPath}/data/${histName}/*_R1_*.fastq.gz >${projPath}/fastq/${histName}_R1.fastq.gz
cat ${projPath}/data/${histName}/*_R2_*.fastq.gz >${projPath}/fastq/${histName}_R2.fastq.gz
##== linux command ==##
mkdir -p $projPath/tools
wget -P $projPath/tools https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.11.9.zip
cd $projPath/tools
unzip fastqc_v0.11.9.zip
##== linux command ==##
mkdir -p ${projPath}/fastqFileQC/${histName}
$projPath/tools/FastQC/fastqc -o ${projPath}/fastqFileQC/${histName} -f fastq ${projPath}/fastq/${histName}_R1.fastq.gz
$projPath/tools/FastQC/fastqc -o ${projPath}/fastqFileQC/${histName} -f fastq ${projPath}/fastq/${histName}_R2.fastq.gz
Quality check reference: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/bad_sequence_fastqc.html
?? - Tn5 preference
##== linux command ==##
cores=8
ref="/path/to/bowtie2Index/hg38"
mkdir -p ${projPath}/alignment/sam/bowtie2_summary
mkdir -p ${projPath}/alignment/bam
mkdir -p ${projPath}/alignment/bed
mkdir -p ${projPath}/alignment/bedgraph
## Build the bowtie2 reference genome index if needed:
## bowtie2-build path/to/hg38/fasta/hg38.fa /path/to/bowtie2Index/hg38
bowtie2 --end-to-end --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700 -p ${cores} -x ${ref} -1 ${projPath}/fastq/${histName}_R1.fastq.gz -2 ${projPath}/fastq/${histName}_R2.fastq.gz -S ${projPath}/alignment/sam/${histName}_bowtie2.sam &> ${projPath}/alignment/sam/bowtie2_summary/${histName}_bowtie2.txt
The paired-end reads are aligned by Bowtie2 using parameters: --end-to-end --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700
Parameters explanation:
http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml
Bowtie2 alignment results summary is saved at ${projPath}/alignment/sam/bowtie2)summary/${histName}_bowtie2.txt
:
2984630 reads; of these:
2984630 (100.00%) were paired; of these:
125110 (4.19%) aligned concordantly 0 times
2360430 (79.09%) aligned concordantly exactly 1 time
499090 (16.72%) aligned concordantly >1 times
95.81% overall alignment rate
We first check the sequencing depth of the data we have.
##=== R command ===##
## Path to the project and histone list
projPath = "/fh/fast/gottardo_r/yezheng_working/cuttag/CUTTag_tutorial"
histList = c("K27me3_rep1", "K27me3_rep2", "K4me3_rep1", "K4me3_rep2", "IgG_rep1", "IgG_rep2")
## Collect the alignment results from the bowtie2 alignment summary files
alignResult = c()
for(hist in histList){
alignRes = read.table(paste0(projPath, "/alignment/sam/bowtie2_summary/", hist, "_bowtie2.txt"), header = FALSE, fill = TRUE)
alignRate = substr(alignRes$V1[6], 1, nchar(as.character(alignRes$V1[6]))-1)
histInfo = strsplit(hist, "_")[[1]]
alignResult = data.frame(seqDepth = alignRes$V1[1] %>% as.character %>% as.numeric, alignNum = alignRes$V1[4] %>% as.character %>% as.numeric + alignRes$V1[5] %>% as.character %>% as.numeric, alignRate = alignRate %>% as.numeric, Histone = histInfo[1], Replicate = histInfo[2]) %>% rbind(alignResult, .)
}
## Generate sequencing depth boxplot
alignResult %>% ggplot(aes(x = Histone, y = seqDepth, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Sequencing Depth") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
One aspect to check the quality of the data is to look at the alignment rate.
##=== R command ===##
alignResult %>% ggplot(aes(x = Histone, y = alignRate, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("% of Mapped Fragments") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
After alignment, we can check the number of mapped reads, i.e., valid sequencing depth for downstream analysis.
##=== R command ===##
alignResult %>% ggplot(aes(x = Histone, y = alignNum, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("# of Mapped Fragments") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
Some project may require more stringent filtering on the alignment quality score. This blog detailedly discussed how does bowtie assign quality score with examples.
MAPQ(x) = -10 * \(log_{10}\)(P(x is mapped wrongly)) = -10 * \(log_{10}(p)\)
which ranges from 0 to 37, 40 or 42.
samtools view -q minQualityScore
will eliminate all the alignment results that are below the minQualityScore defined by user.
##== linux command ==##
minQualityScore=2
samtools view -q $minQualityScore ${projPath}/alignment/sam/${histName}_bowtie2.sam >${projPath}/alignment/sam/${histName}_bowtie2.qualityScore$minQualityScore.sam
CUT&Tag uses the antibody-tethered Tn5-based methods that can achieve high sensitivity, hence the resulting background noise is low and signal regions are enriched and narrow. Therefore, theoretically we may collect fragments that share exactly the same starting and ending sequences which are not because of the PCR duplication.
Practically, we found for high quality and high sequencing depth samples, the duplication rate is pretty low and the rest duplicated fragments are highly likely to be the true enrichment signals. Thus, for these sample, we do not recommend removing the duplicates.
However, for samples that are of low quality and have too few sequencing depth, their duplication rate can be super high. We recommend removing the duplicates for these samples.
The following commands show how to check the duplication rate using Picard
.
##== linux command ==##
## depending on how you load picard and your server environment, the picardCMD can be different. Adjust accordingly.
picardCMD="java -jar picard.jar"
mkdir -p $projPath/alignment/rmDuplicate/picard_summary
## Sort by coordinate
$picardCMD SortSam I=$projPath/alignment/sam/${histName}_bowtie2.sam O=$projPath/alignment/sam/${histName}_bowtie2.sorted.sam SORT_ORDER=coordinate
## mark duplicates
$picardCMD MarkDuplicates I=$projPath/alignment/sam/${histName}_bowtie2.sorted.sam O=$projPath/alignment/removeDuplicate/${histName}_bowtie2.sorted.dupMarked.sam METRICS_FILE=$projPath/alignment/removeDuplicate/picard_summary/${histName}_picard.dupMark.txt
## remove duplicates
picardCMD MarkDuplicates I=$projPath/alignment/sam/${histName}_bowtie2.sorted.sam O=$projPath/alignment/removeDuplicate/${histName}_bowtie2.sorted.rmDup.sam REMOVE_DUPLICATES=true METRICS_FILE=$projPath/alignment/removeDuplicate/picard_summary/${histName}_picard.rmDup.txt
First, we summarize the duplication rate.
##=== R command ===##
## Summarize the duplication information from the picard summary outputs.
dupResult = c()
for(hist in histList){
dupRes = read.table(paste0(projPath, "/alignment/rmDuplicate/picard_summary/", hist, "_picard.rmDup.txt"), header = TRUE, fill = TRUE)
histInfo = strsplit(hist, "_")[[1]]
dupResult = data.frame(mappedN = dupRes$READ_PAIRS_EXAMINED[1] %>% as.character %>% as.numeric, dupRate = dupRes$PERCENT_DUPLICATION[1] %>% as.character %>% as.numeric, Histone = histInfo[1], Replicate = histInfo[2]) %>% mutate(uniqN = mappedN * (1-dupRate)) %>% rbind(dupResult, .)
}
## generate boxplot figure for the duplication rate
dupResult %>% ggplot(aes(x = Histone, y = dupRate * 100, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Duplication Rate (*100%)") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
Next, we check the unique library size without duplications.
##=== R command ===##
dupResult %>% ggplot(aes(x = Histone, y = uniqN, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("# of Unique Fragments") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
##== linux command ==##
mkdir -p $projPath/alignment/sam/fragmentLen
## Extract the 9th column from the alignment sam file which is the fragment length
samtools view -F 0x04 $projPath/alignment/sam/${histName}_bowtie2.sam | awk -F'\t' 'function abs(x){return ((x < 0.0) ? -x : x)} {print abs($9)}' | sort | uniq -c | awk -v OFS="\t" '{print $2, $1/2}' >$projPath/alignment/sam/fragmentLen/${histName}_fragmentLen.txt
##=== R command ===##
## Collect the fragment size information
fragLen = c()
for(hist in histList){
histInfo = strsplit(hist, "_")[[1]]
fragLen = read.table(paste0(projPath, "/alignment/sam/fragmentLen/", hist, "_fragmentLen.txt"), header = FALSE) %>% mutate(fragLen = V1 %>% as.numeric, Weight = as.numeric(V2)/sum(as.numeric(V2)), Histone = histInfo[1], Replicate = histInfo[2], histInfo = hist) %>% rbind(fragLen, .)
}
## Generate the fragment size density plot (violin plot)
fragLen %>% ggplot(aes(x = histInfo, y = fragLen, weight = Weight, fill = Histone)) +
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 20) +
ylab("Fragment Length") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
This section is required in preparation for the peak calling and visualization where there are a few filtering and file format conversion that need to be done.
##== linux command ==##
## Filter and keep the mapped read pairs
samtools view -bS -F 0x04 $projPath/alignment/sam/${histName}_bowtie2.sam $projPath/alignment/bam/${histName}_bowtie2.mapped.bam
## Convert into bed file format
bedtools bamtobed -i $projPath/alignment/bam/${histName}_bowtie2.mapped.bam -bedpe $projPath/alignment/bed/${histName}_bowtie2.bed
## Keep the read pairs that are on the same chromosome and fragment length less than 1000bp.
awk '$1==$4 && $6-$2 < 1000 {print $0}' $projPath/alignment/bed/${histName}_bowtie2.bed $projPath/alignment/bed/${histName}_bowtie2.clean.bed
## Only extract the fragment related columns
cut -f 1,2,6 $projPath/alignment/bed/${histName}_bowtie2.clean.bed | sort -k1,1 -k2,2n -k3,3n >$projPath/alignment/bed/${histName}_bowtie2.fragments.bed
This section is optional but recommended depending on your experimental protocol.
To calibrate samples in a series for samples done in parallel using the same antibody we use counts of E. coli fragments carried over with the pA-Tn5 the same as one would for an ordinary spike-in.
We align E. coli carry-over fragments to the NCBI Ecoli genome (Escherichia coli str. K12 substr. MG1655 U00096.3) with --no-overlap --no-dovetail
options (```–end-to-end –very-sensitive –no-overlap –no-dovetail –no-mixed –no-discordant –phred33 -I 10 -X 700````) to avoid possible cross-mapping of the experimental genome to that of the carry-over E. coli DNA that is used for calibration.
##== linux command ==##
spikeInRef="/shared/ngs/illumina/henikoff/Bowtie2/Ecoli"
chromSize="/fh/fast/gottardo_r/yezheng_working/SupplementaryData/hg38/chromSize/hg38.chrom.size"
## bowtie2-build path/to/Ecoli/fasta/Ecoli.fa /path/to/bowtie2Index/Ecoli
bowtie2 --end-to-end --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700 -p ${cores} -x ${spikeInRef} -1 ${projPath}/fastq/${histName}_R1.fastq.gz -2 ${projPath}/fastq/${histName}_R2.fastq.gz -S $projPath/alignment/sam/${histName}_bowtie2_spikeIn.sam &> $projPath/alignment/sam/bowtie2_summary/${histName}_bowtie2_spikeIn.txt
seqDepthDouble=`samtools view -F 0x04 seqDepth=$((seqDepthDouble/2))
echo $seqDepth >$projPath/alignment/sam/bowtie2_summary/${histName}_bowtie2_spikeIn.seqDepth
if [[ "$seqDepth" -gt "1" ]]; then
mkdir -p $projPath/alignment/bedgraph
scale_factor=`echo "10000 / $seqDepth" | bc -l`
echo "Scaling factor for $histName is: $scale_factor!"
bedtools genomecov -bg -scale $scale_factor -i $projPath/alignment/bed/${histName}_bowtie2.fragments.bed -g $chromSize > $projPath/alignment/bedgraph/${histName}_bowtie2.fragments.normalized.bedgraph
fi
##=== R command ===##
spikeAlign = c()
for(hist in histList){
spikeRes = read.table(paste0(projPath, "/alignment/sam/bowtie2_summary/", hist, "_bowtie2_spikeIn.txt"), header = FALSE, fill = TRUE)
alignRate = substr(spikeRes$V1[6], 1, nchar(as.character(spikeRes$V1[6]))-1)
histInfo = strsplit(hist, "_")[[1]]
spikeAlign = data.frame(seqDepth = spikeRes$V1[1] %>% as.character %>% as.numeric, alignNum = spikeRes$V1[4] %>% as.character %>% as.numeric + spikeRes$V1[5] %>% as.character %>% as.numeric, alignRate = alignRate %>% as.numeric, Histone = histInfo[1], Replicate = histInfo[2]) %>% rbind(spikeAlign, .)
}
## Generate alignment rate boxplot
spikeAlign %>% ggplot(aes(x = Histone, y = alignRate, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Spike-in Alignment Rate") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
The scaling factor is defined to be
Scaling_factor = Scale multiplier/ (Total # of fragments mapped to spike-in genome)
##=== R command ===##
scaleFactor = c()
multiplier = 10000
for(hist in histList){
spikeDepth = read.table(paste0(projPath, "/alignment/sam/bowtie2_summary/", hist, "_bowtie2_spikeIn.seqDepth"), header = FALSE, fill = TRUE)$V1[1]
histInfo = strsplit(hist, "_")[[1]]
scaleFactor = data.frame(scaleFactor = multiplier/spikeDepth, Histone = histInfo[1], Replicate = histInfo[2]) %>% rbind(scaleFactor, .)
}
## Generate sequencing depth boxplot
scaleFactor %>% ggplot(aes(x = Histone, y = scaleFactor, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Spike-in Scalling Factor") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
There is actually no universal scale factor that can be used on different samples. Instead, the key idea of calibration is
(primary_genome_mapped_count_at_bp) * Scaling_factor =
(primary_genome_mapped_count_at_bp) * Scale multiplier / (spike-in_genome_total_of_mapped_fragments)
The underlying assumption is that the primary # genome to spike-in genome ratio per cell is expected to be the same for all samples that use the same number of cells in an experiment comparing treatment.
The “scale multiplier” in the above formula can be an arbitrary multiplier (e.g., 10000) to avoid small fractions.
The drop of file size should be expected as the input files are the alignment for all the fragments but the output is genome coverage which is like a summary of the fragment alignment results. However, it won’t hurt to carefully check the output file.
##=== R command ===##
normDepth = inner_join(scaleFactor, alignResult, by = c("Histone", "Replicate")) %>% mutate(normDepth = alignNum * scaleFactor)
normDepth %>% ggplot(aes(x = Histone, y = normDepth, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Total number of fragments after normalization") +
xlab("") +
coord_cartesian(ylim = c(1000000, 130000000))
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
ChIPseqSpikeInFree: a ChIP-seq normalization approach to reveal global changes in histone modifications without spike-in is a novel ChIP-seq normalization method to effectively determine scaling factors for samples across various conditions and treatments, which does not rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. The installation details can be found on github.
##=== R command ===##
library("ChIPseqSpikeInFree")
bamDir = paste0(projPath, "/alignment/bam")
metaData = c()
for(hist in histList){
histInfo = strsplit(hist, "_")[[1]]
metaData = data.frame(ID = paste0(hist, "_bowtie2.mapped.bam"), ANTIBODY = histInfo[1], GROUP = histInfo[2]) %>% rbind(metaData, .)
}
write.table(metaData, file = paste0(projPath, "/alignment/ChIPseqSpikeInFree/metaData.txt"), row.names = FALSE, quote = FALSE, sep = "\t")
metaFile = paste0(projPath, "/alignment/ChIPseqSpikeInFree/metaData.txt")
bams = paste0(projPath, "/alignment/bam/", metaData$ID)
ChIPseqSpikeInFree(bamFiles = bams, chromFile = "hg38", metaFile = metaFile, prefix = paste0(projPath, "/alignment/ChIPseqSpikeInFree/SpikeInFree_results"))
The usage manual of SEACR can be found here. We have calibrated the fragment frequency with respect to the spike-in, therefore for field 3, we can set the normalization option to be “non”. Otherwise, “norm” is recommended.
##== linux command ==##
seacr="/fh/fast/gottardo_r/yezheng_working/Software/SEACR/SEACR_1.3.sh"
histControl=$2
mkdir -p $projPath/peakCalling/SEACR
bash $seacr $projPath/alignment/bedgraph/${histName}_bowtie2.fragments.normalized.bedgraph \
$projPath/alignment/bedgraph/${histControl}_bowtie2.fragments.normalized.bedgraph \
non stringent $projPath/peakCalling/SEACR/${histName}_seacr_control.peaks
bash $seacr $projPath/alignment/bedgraph/${histName}_bowtie2.fragments.normalized.bedgraph 0.01 non stringent $projPath/peakCalling/SEACR/${histName}_seacr_top0.01.peaks
##=== R command ===##
peakN = c()
peakWidth = c()
peakType = c("control", "top0.01")
for(hist in histList){
histInfo = strsplit(hist, "_")[[1]]
if(histInfo[1] != "IgG"){
for(type in peakType){
peakInfo = read.table(paste0(projPath, "/peakCalling/SEACR/", hist, "_seacr_", type, ".peaks.stringent.bed"), header = FALSE, fill = TRUE) %>% mutate(width = abs(V3-V2))
peakN = data.frame(peakN = nrow(peakInfo), peakType = type, Histone = histInfo[1], Replicate = histInfo[2]) %>% rbind(peakN, .)
peakWidth = data.frame(width = peakInfo$width, peakType = type, Histone = histInfo[1], Replicate = histInfo[2]) %>% rbind(peakWidth, .)
}
}
}
peakN %>% ggplot(aes(x = Histone, y = peakN, fill = Histone)) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
facet_grid(~peakType) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Number of Peaks") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
##=== R command ===##
peakWidth %>% ggplot(aes(x = Histone, y = width, fill = Histone)) +
geom_violin() +
facet_grid(Replicate~peakType) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Width of Peaks") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
##=== R command ===##
peakWidth %>% ggplot(aes(x = Histone, y = width, fill = Histone)) +
geom_violin() +
facet_grid(Replicate~peakType) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("Width of Peaks") +
xlab("") +
coord_cartesian(ylim = c(0, 25000))
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
##=== R command ===##
histL = c("K27me3", "K4me3")
repL = paste0("rep", 1:2)
peakType = c("control", "top0.01")
peakOverlap = c()
for(type in peakType){
for(hist in histL){
overlap.gr = GRanges()
for(rep in repL){
peakInfo = read.table(paste0(projPath, "/peakCalling/SEACR/", hist, "_", rep, "_seacr_", type, ".peaks.stringent.bed"), header = FALSE, fill = TRUE)
peakInfo.gr = GRanges(peakInfo$V1, IRanges(start = peakInfo$V2, end = peakInfo$V3), strand = "*")
if(length(overlap.gr) >0){
overlap.gr = overlap.gr[findOverlaps(overlap.gr, peakInfo.gr)@from]
}else{
overlap.gr = peakInfo.gr
}
}
peakOverlap = data.frame(peakReprod = length(overlap.gr), Histone = hist, peakType = type) %>% rbind(peakOverlap, .)
}
}
peakReprod = left_join(peakN, peakOverlap, by = c("Histone", "peakType")) %>% mutate(peakReprodRate = peakReprod/peakN * 100)
peakReprod %>% ggplot(aes(x = Histone, y = peakReprodRate, fill = Histone, label = round(peakReprodRate, 2))) +
geom_bar(stat = "identity") +
geom_text(vjust = 0.1) +
facet_grid(Replicate~peakType) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("% of Peaks Reproduced across Biological Replicate") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
##=== R command ===##
library(chromVAR)
bamDir = paste0(projPath, "/alignment/bam")
inPeakData = c()
## overlap with bam file to get count
for(hist in histL){
for(rep in repL){
peakRes = read.table(paste0(projPath, "/peakCalling/SEACR/", hist, "_", rep, "_seacr_control.peaks.stringent.bed"), header = FALSE, fill = TRUE)
peak.gr = GRanges(seqnames = peakRes$V1, IRanges(start = peakRes$V2, end = peakRes$V3), strand = "*")
bamFile = paste0(bamDir, "/", hist, "_", rep, "_bowtie2.mapped.bam")
fragment_counts <- getCounts(bamFile, peak.gr, paired = TRUE, by_rg = FALSE, format = "bam")
inPeakN = counts(fragment_counts)[,1] %>% sum
inPeakData = rbind(inPeakData, data.frame(inPeakN = inPeakN, Histone = hist, Replicate = rep))
}
}
frip = left_join(inPeakData, alignResult, by = c("Histone", "Replicate")) %>% mutate(frip = inPeakN/alignNum * 100)
frip %>% ggplot(aes(x = Histone, y = frip, fill = Histone, label = round(frip, 2))) +
geom_boxplot() +
geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ylab("% of Fragments in Peaks") +
xlab("")
Version | Author | Date |
---|---|---|
8db752d | yezhengSTAT | 2020-06-01 |
##== linux command ==##
histName="K27me3"
controlName="IgG"
mkdir -p $projPath/peakCalling
macs2 callpeak -t ${projPath}/alignment/bam/${histName}_rep1_bowtie2.mapped.bam \
-c ${projPath}/alignment/bam/${controlName}_rep1_bowtie2.mapped.bam \
-g hs -f BAMPE -n macs2_peak_q0.1 --outdir $projPath/peakCalling/MACS2 -q 0.1 --keep-dup all 2>${projPath}/peakCalling/MACS2/macs2Peak_summary.txt
Integrative Genomic Viewer provides web app version and local desktop version. Easy to use.
UCSC Genome Browser provides the most comprehensive supplementary genome information.
We will use the computeMatrix
and plotHeatmap
functions from deepTools to generate the heatmap.
##== linux command ==##
mkdir -p $projPath/alignment/bigwig
samtools sort -o $projPath/alignment/bam/${histName}.sorted.bam $projPath/alignment/bam/${histName}_bowtie2.mapped.bam
samtools index $projPath/alignment/bam/${histName}.sorted.bam
bamCoverage -b $projPath/alignment/bam/${histName}.sorted.bam -o $projPath/alignment/bigwig/${histName}_raw.bw
##== linux command ==##
cores=8
computeMatrix scale-regions -S $projPath/alignment/bigwig/K27me3_rep1_raw.bw \
$projPath/alignment/bigwig/K27me3_rep2_raw.bw \
$projPath/alignment/bigwig/K4me3_rep1_raw.bw \
$projPath/alignment/bigwig/K4me3_rep2_raw.bw \
-R $projPath/data/hg38_gene/hg38_gene.tsv \
--beforeRegionStartLength 3000 \
--regionBodyLength 5000 \
--afterRegionStartLength 3000 \
--skipZeros -o $projPath/data/hg38_gene/matrix_gene.mat.gz -p $cores
plotHeatmap -m $projPath/data/hg38_gene/matrix_gene.mat.gz -out $projPath/data/hg38_gene/Histone_gene.png --sortUsing sum
### 7.2.2. Heatmap on CUT&Tag peaks
##== linux command ==##
computeMatrix scale-regions -S $projPath/alignment/bigwig/${histName}_raw.bw \
-R $projPath/peakCalling/SEACR/${histName}_seacr_control.peaks.stringent.bed \
--skipZeros -o $projPath/peakCalling/SEACR/${histName}_SEACR.mat.gz -p $cores
plotHeatmap -m $projPath/peakCalling/SEACR/${histName}_SEACR.mat.gz -out $projPath/peakCalling/SEACR/${histName}_SEACR_heatmap.png --sortUsing sum --startLabel "Peak Start" -\
-endLabel "Peak End" --xAxisLabel "" --regionsLabel "Peaks" --samplesLabel ${histName}
Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
Limma is an R package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. Limma provides the ability to analyse comparisons between many RNA targets simultaneously in arbitrary complicated designed experiments. Empirical Bayesian methods are used to provide stable results even when the number of arrays is small. Limma can be extended to study differential fragment enrichment analysis within peak regions. Notably, limma can deal with both fixed effect model and random effect model.
Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE. edgeR can deal with multifactor problem.
Usually, the differential tests compare two or more conditions of the same histone modification. In this tutorial, limited by the demonstration data, we will illustrate the differential detection by comparing two replicates of H3K27me3 and two replicates of H3K4me3. We will use DESeq2 (complete tutorial) as illustration.
##=== R command ===##
mPeak = GRanges()
## overlap with bam file to get count
for(hist in histL){
for(rep in repL){
peakRes = read.table(paste0(projPath, "/peakCalling/SEACR/", hist, "_", rep, "_seacr_control.peaks.stringent.bed"), header = FALSE, fill = TRUE)
mPeak = GRanges(seqnames = peakRes$V1, IRanges(start = peakRes$V2, end = peakRes$V3), strand = "*") %>% append(mPeak, .)
}
}
masterPeak = reduce(mPeak)
##=== R command ===##
library(DESeq2)
bamDir = paste0(projPath, "/alignment/bam")
countMat = matrix(NA, length(masterPeak), length(histL)*length(repL))
## overlap with bam file to get count
i = 1
for(hist in histL){
for(rep in repL){
bamFile = paste0(bamDir, "/", hist, "_", rep, "_bowtie2.mapped.bam")
fragment_counts <- getCounts(bamFile, masterPeak, paired = TRUE, by_rg = FALSE, format = "bam")
countMat[, i] = counts(fragment_counts)[,1]
i = i + 1
}
}
colnames(countMat) = paste(rep(histL, 2), rep(repL, each = 2), sep = "_")
##=== R command ===##
selectR = which(rowSums(countMat) > 5) ## remove low count genes
dataS = countMat[selectR,]
condition = factor(rep(histL, each = length(repL)))
dds = DESeqDataSetFromMatrix(countData = dataS,
colData = DataFrame(condition),
design = ~ condition)
DDS = DESeq(dds)
normDDS = counts(DDS, normalized = TRUE) ## normalization with respect to the sequencing depth
colnames(normDDS) = paste0(colnames(normDDS), "_norm")
res = results(DDS, independentFiltering = FALSE, altHypothesis = "greaterAbs")
countMatDiff = cbind(dataS, normDDS, res)
head(countMatDiff)
DataFrame with 6 rows and 14 columns
K27me3_rep1 K4me3_rep1 K27me3_rep2 K4me3_rep2 K27me3_rep1_norm
<numeric> <numeric> <numeric> <numeric> <numeric>
1 1 0 242 182 0.124345459941495
2 0 0 175 88 0
3 0 0 274 194 0
4 212 130 354 637 26.3612375075969
5 0 2 180 174 0
6 1 0 193 186 0.124345459941495
K4me3_rep1_norm K27me3_rep2_norm K4me3_rep2_norm baseMean
<numeric> <numeric> <numeric> <numeric>
1 0 1642.098979559 860.62486419289 625.712047302957
2 0 1187.46827034225 416.126307961397 400.898644575912
3 0 1859.23603470729 917.36936073308 694.151348860094
4 29.5546421781954 2402.07867257804 3012.18702467511 1367.54539423474
5 0.454686802741468 1221.39593520917 822.795199832763 511.161455461169
6 0 1309.60786386317 879.539696372953 547.317976424015
log2FoldChange lfcSE stat pvalue
<numeric> <numeric> <numeric> <numeric>
1 13.9001328992164 1.46237329851921 9.50518784313936 1.9968728862444e-21
2 14.6578324953342 1.58571426872719 9.24367824923438 2.38165977165095e-20
3 15.4521653584048 1.57311982807133 9.82262449603056 8.99701430682874e-23
4 6.59884758504177 0.54558731844186 12.0949431227386 1.12320018631658e-33
5 12.5701262927903 1.12748347918641 11.1488341291359 7.25507086647784e-29
6 13.7045749300893 1.42987365830219 9.58446562779669 9.2937484305191e-22
padj
<numeric>
1 1.76349075171341e-20
2 1.74850821274181e-19
3 1.35404803928629e-21
4 1.47675167643583e-31
5 3.8309051756058e-27
6 9.00551358394595e-21
DESeq2 requires the input matrix should be un-normalized counts or estimated counts of sequencing reads.
DESeq2 model internally corrects for library size.
countMatDiff
summarizes the differential analysis results:
Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG, Ahmad K, Henikoff S: CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications 2019 10:1930 (PMID:31036827).
sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS
Matrix products: default
BLAS/LAPACK: /app/easybuild/software/OpenBLAS/0.2.18-GCC-5.4.0-2.26-LAPACK-3.6.1/lib/libopenblas_prescottp-r0.2.18.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[3] DelayedArray_0.12.3 BiocParallel_1.20.1
[5] matrixStats_0.55.0 Biobase_2.46.0
[7] chromVAR_1.8.0 GenomicRanges_1.38.0
[9] GenomeInfoDb_1.22.1 IRanges_2.20.2
[11] S4Vectors_0.24.4 BiocGenerics_0.32.0
[13] viridis_0.5.1 viridisLite_0.3.0
[15] ggplot2_3.3.0 stringr_1.4.0
[17] dplyr_0.8.5
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.5
[3] Hmisc_4.3-0 workflowr_1.6.2
[5] VGAM_1.1-2 plyr_1.8.5
[7] lazyeval_0.2.2 splines_3.6.2
[9] TFBSTools_1.24.0 digest_0.6.23
[11] htmltools_0.4.0 GO.db_3.10.0
[13] magrittr_1.5 checkmate_1.9.4
[15] memoise_1.1.0 BSgenome_1.54.0
[17] cluster_2.1.0 openxlsx_4.1.4
[19] Biostrings_2.54.0 readr_1.3.1
[21] annotate_1.64.0 R.utils_2.9.2
[23] colorspace_1.4-1 blob_1.2.0
[25] haven_2.2.0 xfun_0.11
[27] crayon_1.3.4 RCurl_1.95-4.12
[29] jsonlite_1.6 genefilter_1.68.0
[31] TFMPvalue_0.0.8 survival_3.1-8
[33] glue_1.3.1 gtable_0.3.0
[35] zlibbioc_1.32.0 XVector_0.26.0
[37] car_3.0-5 abind_1.4-5
[39] scales_1.1.0 DBI_1.0.0
[41] rstatix_0.5.0 miniUI_0.1.1.1
[43] Rcpp_1.0.3 xtable_1.8-4
[45] htmlTable_1.13.3 foreign_0.8-72
[47] bit_1.1-14 Formula_1.2-3
[49] DT_0.10 htmlwidgets_1.5.1
[51] httr_1.4.1 RColorBrewer_1.1-2
[53] acepack_1.4.1 pkgconfig_2.0.3
[55] XML_3.98-1.20 R.methodsS3_1.7.1
[57] farver_2.0.1 nnet_7.3-12
[59] locfit_1.5-9.1 tidyselect_1.0.0
[61] labeling_0.3 rlang_0.4.6
[63] reshape2_1.4.3 later_1.0.0
[65] AnnotationDbi_1.48.0 cellranger_1.1.0
[67] munsell_0.5.0 tools_3.6.2
[69] DirichletMultinomial_1.28.0 generics_0.0.2
[71] RSQLite_2.1.4 broom_0.5.2
[73] evaluate_0.14 fastmap_1.0.1
[75] yaml_2.2.0 knitr_1.26
[77] bit64_0.9-7 fs_1.3.1
[79] zip_2.0.4 caTools_1.17.1.3
[81] purrr_0.3.3 KEGGREST_1.26.1
[83] nlme_3.1-143 whisker_0.4
[85] mime_0.7 R.oo_1.23.0
[87] poweRlaw_0.70.6 pracma_2.2.5
[89] compiler_3.6.2 rstudioapi_0.10
[91] plotly_4.9.1 curl_4.3
[93] png_0.1-7 ggsignif_0.6.0
[95] tibble_2.1.3 geneplotter_1.64.0
[97] stringi_1.4.3 highr_0.8
[99] forcats_0.4.0 lattice_0.20-38
[101] CNEr_1.22.0 Matrix_1.2-18
[103] vctrs_0.2.4 pillar_1.4.2
[105] lifecycle_0.1.0 data.table_1.12.8
[107] bitops_1.0-6 httpuv_1.5.2
[109] rtracklayer_1.46.0 R6_2.4.1
[111] latticeExtra_0.6-28 promises_1.1.0
[113] gridExtra_2.3 rio_0.5.16
[115] gtools_3.8.1 assertthat_0.2.1
[117] seqLogo_1.52.0 rprojroot_1.3-2
[119] withr_2.1.2 GenomicAlignments_1.22.1
[121] Rsamtools_2.2.3 GenomeInfoDbData_1.2.2
[123] hms_0.5.2 grid_3.6.2
[125] rpart_4.1-15 tidyr_1.0.0
[127] rmarkdown_1.18 carData_3.0-3
[129] Cairo_1.5-10 git2r_0.26.1
[131] ggpubr_0.3.0 shiny_1.4.0
[133] base64enc_0.1-3